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NouRed/BioMed-Tuned-Gemma-2b

NouRed

transformers

--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

allknowingroger/DelexaMultiverse-12B-MoE

allknowingroger

transformers

text-generation

--- license: apache-2.0 tags: - moe - frankenmoe - merge - mergekit - lazymergekit - lex-hue/Delexa-7b - allknowingroger/MultiverseEx26-7B-slerp base_model: - lex-hue/Delexa-7b - allknowingroger/MultiverseEx26-7B-slerp --- # DelexaMultiverse-12B-MoE DelexaMultiverse-12B-MoE is a Mixture of Experts (MoE) made with the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing): * [lex-hue/Delexa-7b](https://huggingface.co/lex-hue/Delexa-7b) * [allknowingroger/MultiverseEx26-7B-slerp](https://huggingface.co/allknowingroger/MultiverseEx26-7B-slerp) ## 🧩 Configuration ```yaml base_model: allknowingroger/MultiverseEx26-7B-slerp experts: - source_model: lex-hue/Delexa-7b positive_prompts: ["what"] - source_model: allknowingroger/MultiverseEx26-7B-slerp positive_prompts: ["why"] ``` ## 💻 Usage ```python !pip install -qU transformers bitsandbytes accelerate from transformers import AutoTokenizer import transformers import torch model = "allknowingroger/DelexaMultiverse-12B-MoE" tokenizer = AutoTokenizer.from_pretrained(model) pipeline = transformers.pipeline( "text-generation", model=model, model_kwargs={"torch_dtype": torch.float16, "load_in_4bit": True}, ) messages = [{"role": "user", "content": "Explain what a Mixture of Experts is in less than 100 words."}] prompt = pipeline.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95) print(outputs[0]["generated_text"]) ```

hvein/melotts6246

hvein

Entry not found

nehuggingface/Hermes2pro-mistral-saul-instruct-slerp

nehuggingface

transformers

text-generation

--- base_model: - NousResearch/Hermes-2-Pro-Mistral-7B - Equall/Saul-Instruct-v1 library_name: transformers tags: - mergekit - merge --- # merge This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). ## Merge Details ### Merge Method This model was merged using the SLERP merge method. ### Models Merged The following models were included in the merge: * [NousResearch/Hermes-2-Pro-Mistral-7B](https://huggingface.co/NousResearch/Hermes-2-Pro-Mistral-7B) * [Equall/Saul-Instruct-v1](https://huggingface.co/Equall/Saul-Instruct-v1) ### Configuration The following YAML configuration was used to produce this model: ```yaml slices: - sources: - model: Equall/Saul-Instruct-v1 layer_range: [0, 32] - model: NousResearch/Hermes-2-Pro-Mistral-7B layer_range: [0, 32] merge_method: slerp base_model: NousResearch/Hermes-2-Pro-Mistral-7B parameters: t: - filter: self_attn value: [0, 0.5, 0.3, 0.7, 1] - filter: mlp value: [1, 0.5, 0.7, 0.3, 0] - value: 0.5 dtype: bfloat16 ```

Nwoobyy/KOR_03

Nwoobyy

Entry not found

spxrks3x/BeerusGPT

spxrks3x

Entry not found

bdambrosio/Smaug-2-72B-8bpw-exl2

bdambrosio

--- license: apache-2.0 ---

hflog/capleaf-T-Llama

hflog

transformers

text-generation

--- language: - vi - en license: apache-2.0 model-index: - name: T-Llama results: - task: type: text-generation name: Text Generation dataset: name: AI2 Reasoning Challenge (25-Shot) type: ai2_arc config: ARC-Challenge split: test args: num_few_shot: 25 metrics: - type: acc_norm value: 54.18 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=1TuanPham/T-Llama name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: HellaSwag (10-Shot) type: hellaswag split: validation args: num_few_shot: 10 metrics: - type: acc_norm value: 76.48 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=1TuanPham/T-Llama name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MMLU (5-Shot) type: cais/mmlu config: all split: test args: num_few_shot: 5 metrics: - type: acc value: 47.98 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=1TuanPham/T-Llama name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: TruthfulQA (0-shot) type: truthful_qa config: multiple_choice split: validation args: num_few_shot: 0 metrics: - type: mc2 value: 46.47 source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=1TuanPham/T-Llama name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: Winogrande (5-shot) type: winogrande config: winogrande_xl split: validation args: num_few_shot: 5 metrics: - type: acc value: 71.27 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=1TuanPham/T-Llama name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: GSM8k (5-shot) type: gsm8k config: main split: test args: num_few_shot: 5 metrics: - type: acc value: 29.64 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=1TuanPham/T-Llama name: Open LLM Leaderboard --- <p align="center"> <img src="https://cdn-uploads.huggingface.co/production/uploads/63905e87df447b438817b2cd/QFhLKQlWeyO9XumtyghVo.jpeg" alt="Image" style="width: 400px; height: auto; border-radius: 10px;" /> </p> ## Model Details - **Developed by:** Tuan Pham (FPTU HCM Student) - Contact me at: weekend.2810@gmail.com or tuanpmse160561@fpt.edu.vn - Looking for intern opportunity :D - **Model type:** Llama2-7B Decoder-only - **Finetuned from model :** * meta-llama/Llama-2-7b * bkai-foundation-models/vietnamese-llama2-7b-120GB * yeen214/llama2_7b_merge_orcafamily. - **Bilingual support :** English and Vietnamese ### Model Description <!-- Provide a longer summary of what this model is. --> This model is a proof of effort that one man can fine-tune his own model to reach SOTA. ### Model Sources <!-- Provide the basic links for the model. --> - **Repository:** * Training: https://github.com/vTuanpham/Vietnamese_QA_System * Data: https://github.com/vTuanpham/Large_dataset_translator - **Paper:** ... - **Demo:** ... - Here is a kaggle and colab script to quickly test the model: * https://www.kaggle.com/code/tuanphamm/t-llama-test * https://colab.research.google.com/drive/1Y-f0E6C_gN_Iy72UN3-Y_c5RdPgrPai-?usp=sharing ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Prompt template ``` [SYSTEM_PROMPT] ####### Instruction: [INPUT] %%%%%%% Response: [RESPONSE] ``` Recommend keeping the system prompt in english. ## How to Get Started with the Model Use the code below to get started with the model. ```python import torch from torch.cuda.amp import autocast from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer, pipeline def prompt_format(system_prompt, instruction): prompt = f"""{system_prompt} ####### Instruction: {instruction} %%%%%%% Response: """ return prompt system_prompt = """ You're an AI Large Language Model developed(created) by an AI developer named Tuấn, the architecture of you is decoder-based LM, your task are to think loudly step by step before give a good and relevant response to the user request, answer in the language the user preferred. The AI has been trained to answer questions, provide recommendations, and help with decision making. The AI thinks outside the box and follows the user requests """ instruction = "Xin chào" formatted_prompt = prompt_format(system_prompt, instruction) print(formatted_prompt) model_name = "1TuanPham/T-Llama" model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.bfloat16, use_cache=True, device_map="auto" ) tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=True) streamer = TextStreamer(tokenizer, skip_special_tokens=True) pipe = pipeline("text-generation", model=model, tokenizer=tokenizer, streamer=streamer) with autocast(): output_default = pipe(formatted_prompt, pad_token_id=50256, max_new_tokens=128) ``` Example outputs: ``` > Xin chào Xin chào! Tôi là một AI được phát triển bởi một AI nhà phát triển tên là Tuấn. Tôi được thiết kế để giúp đỡ người dùng bằng cách trả lời các câu hỏi, đưa ra đề xuất và hỗ trợ trong quá trình ra quyết định. Tôi có thể hỗ trợ bạn bằng cách nghĩ ra các câu trả lời hay và phù hợp cho các câu hỏi của bạn. > Việt Nam có cà phê gì nổi tiếng thế giới ? Cà phê robusta của Việt Nam nổi tiếng trên toàn cầu. Loại cà phê này được trồng chủ yếu ở các vùng đất cao như Lâm Đồng, Gia Lai, KonTum và có hương vị đậm đà, đắng hơn so với cà phê Arabica. Cà phê robusta thường được sử dụng để pha chế các loại cà phê espresso và phin truyền thống. > Tôi có 500$ hãy build cho tôi một bộ PC Để xây dựng một bộ PC với 500 USD, bạn có thể bắt đầu bằng cách chọn các thành phần chính như CPU, GPU, bộ nhớ và bộ lưu trữ. Sau đó, bạn có thể thêm các thành phần bổ sung như loa, bàn phím và chuột để hoàn thiện hệ thống. Dưới đây là một số gợi ý về cách bạn có thể cấu thành một bộ PC với 500 USD: 1. CPU: Intel Core i5-12400 hoặc AMD Ryzen 5 5600X là những lựa chọn tốt để bắt đầu. 2. GPU: Nvidia GeForce GTX 1650 hoặc AMD Radeon RX 570 là những lựa chọn tốt để bắt đầu. 3. Bộ nhớ: 8GB RAM là một khởi đầu tốt. 4. Bộ lưu trữ: 500 GB ổ cứng SSD là một khởi đầu tốt. 5. Bàn phím và chuột: Bàn phím và chuột có thể được mua riêng hoặc cùng một bộ. 6. Loa: Một loa có thể được mua riêng hoặc cùng một bộ với bàn phím và chuột. > Cho tôi 3 thức uống để giúp ngủ ngon hơn Chắc chắn! Dưới đây là ba thức uống có thể giúp bạn ngủ ngon hơn: 1. Mật ong và chanh: Trộn một thìa mật ong với một thìa nước cốt chanh và uống trước khi đi ngủ. Mật ong có tác dụng làm dịu, trong khi chanh có chứa chất chống oxy hóa có thể giúp cải thiện chất lượng giấc ngủ. 2. Trà thảo dược: Một số loại trà thảo dược như hoa cúc, hoa oải hương và hoa sen có tác dụng thư giãn và giúp bạn chìm vào giấc ngủ. Hãy thử pha một ấm trà thảo dược ấm và uống trước khi đi ngủ. 3. Sữa ấm: Sữa ấm có chứa tryptophan, một loại hormone giúp bạn cảm thấy dễ chịu và thư giãn. Nó cũng có thể giúp thúc đẩy sản xuất melatonin, hormone giúp bạn chìm vào giấc ngủ. Hãy thử uống một cốc sữa ấm trước khi đi ngủ để giúp bạn ngủ ngon hơn. ``` The translated data has been thoroughly cleaned from the coding task, so in theory, the model should be able to code normally by incorporating the coding capabilities from the English coding examples task. (The translation will translate code definitions and variables.) I would not recommend running any code generated from this model. ```` > Viết cho tôi một đoạn code bằng python xây dựng một ứng dụng bằng Flask hiển thị 'Xin chào thế giới' trên màn hình Để viết một đoạn mã Python xây dựng một ứng dụng bằng Flask hiển thị 'Xin chào thế giới' trên màn hình, bạn có thể làm theo các bước sau: 1. Cài đặt Flask và các thư viện cần thiết: ``` pip install flask ``` 2. Tạo một ứng dụng Flask mới: ``` app = Flask(__name__) ``` 3. Thêm một route để hiển thị 'Xin chào thế giới': ``` @app.route('/') def hello(): return 'Xin chào thế giới!' ``` 4. Khởi tạo ứng dụng Flask: ``` app.run() ``` 5. Chạy ứng dụng Flask: ``` app.run(debug=True) ``` 6. Mở trình duyệt và truy cập vào địa chỉ IP hoặc tên máy chủ của máy tính hoặc máy chủ nơi bạn đã khởi động ứng dụng Flask. ```` These examples are sub-optimal as they were generated with greedy decoding to ensure reproducibility. Here's the recommended configuration: * top_k=50 * top_p=0.5 * temperature=0.5 * penalty_alpha=0.7 * repetition_penalty=1.15 Here is an example using the above generation config: ```` > Tôi buồn ngủ quá, cơ mà thứ 7 phải thi rồi, nay là rạng sáng thứ 6. Tôi có nên đi ngủ không trong khi tôi mới xong chưa được 1/3 bài để ôn ? Nếu bạn cảm thấy mệt mỏi và cần nghỉ ngơi thì việc đi ngủ sớm sẽ rất quan trọng đối với sức khỏe của bạn. Tuy nhiên, nếu bạn vẫn muốn hoàn thành một phần bài tập để chuẩn bị cho kỳ thi sắp tới, hãy cân nhắc thời gian còn lại của mình và ưu tiên những nhiệm vụ quan trọng nhất trước khi đi ngủ. Nếu bạn cảm thấy rằng mình đã làm đủ, hãy dành chút thời gian thư giãn hoặc thực hiện các hoạt động khác để giúp giảm bớt căng thẳng và tăng cường sự tỉnh táo của bạn vào buổi sáng hôm sau. Cuối cùng, quyết định cuối cùng phụ thuộc vào mức độ nghiêm trọng của kỳ thi và khả năng quản lý thời gian của bạn. # temperature=0.7 > Em ăn cơm chưa ? Không, tôi không ăn cơm. Tôi là một mô hình ngôn ngữ máy được phát triển bởi một nhà phát triển AI tên là Tuấn và được đào tạo để trả lời các câu hỏi và cung cấp phản hồi hữu ích cho người dùng. Mục tiêu của tôi là hỗ trợ người dùng bằng cách suy nghĩ trước khi đưa ra những phản hồi phù hợp và chính xác nhất có thể. > Viết cho tôi một ứng dụng flask in ra màn hình 'Xin chào Việt Nam!' Để viết ứng dụng Flask hiển thị "Xin chào Việt Nam!" trên màn hình, chúng ta có thể làm theo các bước sau: 1. Cài đặt Flask và các thư viện cần thiết như BeautifulSoup4 và jsonify. Bạn có thể sử dụng pip để cài đặt chúng: ``` pip install Flask pip install beautifulsoup4 pip install jsonify ``` 2. Tạo một tệp mới trong thư mục của bạn với tên `app.py`. Đây là mã mẫu cơ bản của ứng dụng: ```python from bs4 import BeautifulSoup from jsonify import jsonify import requests app = Flask(__name__) @app.route('/') def home(): html_string = """<h1>Chào mừng đến với Việt Nam!</h1> <p>Đây là một trang web dành cho những ai muốn tìm hiểu về đất nước Việt Nam xinh đẹp.</p>""" soup = BeautifulSoup(html_string, 'html.parser') print(soup) return soup if __name__ == '__main__': app.run() ``` 3. Khởi tạo ứng dụng bằng cách chạy `app.py`: ``` python app.py ``` 4. Mở trình duyệt web và truy cập vào địa chỉ IP hoặc tên máy chủ của ứng dụng (ví dụ: http://localhost:5000). Trang đầu sẽ hiển thị thông báo "Xin chào Việt Nam!". Lưu ý rằng đây chỉ là ví dụ đơn giản và bạn có thể tùy chỉnh ứng dụng để đáp ứng nhu cầu cụ thể của mình. > Liệt kê cho tôi 5 thứ cần thiết để đem đi cắm trại Chắc chắn! Dưới đây là năm vật dụng cần thiết khi đi cắm trại: 1. Lều - Một chiếc lều chắc chắn và thoải mái sẽ cung cấp nơi trú ẩn khỏi thời tiết và côn trùng. 2. Túi ngủ - Một túi ngủ ấm áp và thoải mái sẽ giúp bạn có giấc ngủ ngon trong khi ở ngoài trời. 3. Bếp di động - Một bếp di động như bình đun hoặc bếp cồn sẽ giúp bạn nấu ăn và giữ ấm thức ăn của mình. 4. Đèn pin - Một đèn pin mạnh mẽ sẽ giúp chiếu sáng khu vực cắm trại của bạn vào ban đêm. 5. Bản đồ và la bàn - Bản đồ và la bàn sẽ giúp bạn định hướng chính xác đến địa điểm cắm trại của bạn và quay lại nhà nếu cần. > Viết cho tôi một bài thơ về thành phố Đà Lạt Thành phố sương mù, nơi thiên nhiên thanh bình, Nơi những bông hoa khoe sắc và cây xanh tươi tốt, Một ốc đảo giữa lòng đất, một xứ sở thần tiên, Nơi mọi lo lắng đều tan biến trong không khí. Những con đường nhỏ đan xen qua những ngọn đồi thoai thoải, Và những ngôi nhà cổ kính, với những bức tường màu nâu ấm áp, Nơi những người dân thân thiện chào đón bạn bằng nụ cười, Và tiếng chim hót líu lo như lời chào của thiên nhiên. Những dòng suối chảy êm đềm, những thác nước đổ xuống, Và những hồ nước lấp lánh, những cảnh tượng ngoạn mục, Nơi sự tĩnh lặng ngự trị, nơi bình yên ngự trị, Nơi tâm hồn được chữa lành và cảm xúc được giải phóng. Ở đây, thời gian dường như đứng yên, Nơi những kỷ niệm vẫn còn nguyên vẹn, Nơi những mối quan hệ bền chặt được nuôi dưỡng, Nơi tình yêu và hòa bình chiếm vị trí hàng đầu. Thế giới bên ngoài có vẻ xa xôi, Nhưng ở đây, chỉ cần bước chân đi, Nơi bầu trời rộng lớn, những đám mây cao chót vót, Và những tia nắng vàng rực rỡ, sưởi ấm trái tim chúng ta. Ôi, thành phố sương mù, nơi thiên nhiên thanh bình, Nơi những giấc mơ trở thành hiện thực, nơi trái tim tràn ngập niềm vui, Nơi tình yêu và hòa bình ngự trị, nơi tình yêu và hòa bình ngự trị. ```` Note: 120GB of pre-trained Vietnamese  data might not be enough for a general question about Vietnamese events. ## Training Details **Hardware Type:** * GPU: VGA NVIDIA Tesla P100 16GB * SYSTEM RAM: 32GB **Hours used:** ~47.5 days Approx* ### Training Data * BactrianX * OpenOrca_translated * WizardLM_70k_translated * TigerLabMathInstruct_translated_vi * GradeSchoolMathInstruct_translated * vilm_lima-vi * MTEngVietnamese * databricks_dolly15k_translated * AlpacaCleaned_translated * databricks_dolly15k * OpenOrca * GradeSchoolMathInstruct * AlpacaCleaned * WebglmQA ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> * Learning rate: 2e-5 cosine * Optimizer: PagedLion8bit * QLora: rank: 64 /Q: 4-bit * Batch size: 2 * Gradient accumulation: 128 * Effective batch size: 256 * 250k examples of 70% Vietnamese 30% English for 3.37 epoch * 350k examples of 60% Vietnamese 40% English for 1.4 epoch ### Training loss Last 10 runs:  Each line is 12 hours All run plots:  A bad way to visualize i know... ## Evaluation <!-- This section describes the evaluation protocols and provides the results. -->  Our model currently sits at TOP-5 on the VMLU benchmark # [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_1TuanPham__T-Llama) | Metric |Value| |---------------------------------|----:| |Avg. |54.34| |AI2 Reasoning Challenge (25-Shot)|54.18| |HellaSwag (10-Shot) |76.48| |MMLU (5-Shot) |47.98| |TruthfulQA (0-shot) |46.47| |Winogrande (5-shot) |71.27| |GSM8k (5-shot) |29.64| ## Citation <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> ```bibtex @online{t-llama, author = {Pham Minh Tuan}, title = {T-Llama: A New Language Model for Vietnamese}, % Placeholder year = 2024, url = {https://github.com/vTuanpham/Vietnamese_QA_System} } ```

Kirill47/Tank

Kirill47

--- license: mit datasets: - m-a-p/COIG-CQIA language: - aa ---

as-cle-bert/carbon-footprint-prediction

as-cle-bert

tabular-regression

--- tags: - autotrain - tabular - regression - tabular-regression datasets: - autotrain-uljkp-sdhgs/autotrain-data --- # Model Trained Using AutoTrain - Problem type: Tabular regression ## Validation Metrics - r2: 0.9900762497798218 - mse: 10317.805777253338 - mae: 74.54517527770996 - rmse: 101.57660053995377 - rmsle: 0.042811727450114016 - loss: 101.57660053995377 ## Best Params - learning_rate: 0.016479102091350954 - reg_lambda: 0.3449233788687026 - reg_alpha: 3.244557908377455e-07 - subsample: 0.5379679408548034 - colsample_bytree: 0.9050706969365716 - max_depth: 4 - early_stopping_rounds: 293 - n_estimators: 7000 - eval_metric: rmse ## Usage ```python import json import joblib import pandas as pd model = joblib.load('model.joblib') config = json.load(open('config.json')) features = config['features'] # data = pd.read_csv("data.csv") data = data[features] predictions = model.predict(data) # or model.predict_proba(data) # predictions can be converted to original labels using label_encoders.pkl ```

bdambrosio/Mixtral-8x22b-instruct-oh-6.0bpw-exl2

bdambrosio

transformers

text-generation

--- license: apache-2.0 --- 6.0 bit exl2 quant (8 vut head) of Fireworks Hermes 2.5 fine tune of Mixtral-8x22b Use Vicuna prompt template needs ~ 120GB vRam (2xA100 or 3X RTX 6000)

ehristoforu/plms-0002

ehristoforu

Entry not found

Aviral2412/mini_model

Aviral2412

transformers

Entry not found

Mou11209203/distilbert-base-multilingual-cased_regression_finetuned_mobile01_all

Mou11209203

transformers

text-classification

--- license: apache-2.0 base_model: distilbert/distilbert-base-multilingual-cased tags: - generated_from_trainer model-index: - name: distilbert-base-multilingual-cased_regression_finetuned_mobile01_all results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-multilingual-cased_regression_finetuned_mobile01_all This model is a fine-tuned version of [distilbert/distilbert-base-multilingual-cased](https://huggingface.co/distilbert/distilbert-base-multilingual-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.8363 - Mse: 1.8363 - Mae: 0.9819 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 500 - num_epochs: 10 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Mse | Mae | |:-------------:|:-----:|:-----:|:---------------:|:------:|:------:| | 1.7467 | 1.0 | 7789 | 1.8329 | 1.8329 | 0.9821 | | 1.9294 | 2.0 | 15578 | 1.8363 | 1.8363 | 0.9819 | | 1.6483 | 3.0 | 23367 | 1.8282 | 1.8282 | 0.9856 | | 1.9471 | 4.0 | 31156 | 1.8284 | 1.8284 | 0.9848 | ### Framework versions - Transformers 4.39.3 - Pytorch 2.2.1 - Datasets 2.18.0 - Tokenizers 0.15.2

abbenedek/whisper-base-finetuned

abbenedek

transformers

--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

toghrultahirov/gemma-1.1-7b-it-CausalFT

toghrultahirov

transformers

--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

RikudouSage/Chronos-Mistral-7B-GGUF

RikudouSage

--- base_model: elinas/chronos-mistral-7b inference: false language: - en license: apache-2.0 model_type: mistral tags: - mistral - chatbot - storywriting - gguf --- ## Description This repo contains GGUF files for the original model. ### Files - [Chronos-Mistral-7b_Q2_K.gguf](Chronos-Mistral-7b_Q2_K.gguf) (2.72 GB) - smallest, significant quality loss - not recommended for most purposes - [Chronos-Mistral-7b_Q3_K_S.gguf](Chronos-Mistral-7b_Q3_K_S.gguf) (3.16 GB) - very small, high quality loss - [Chronos-Mistral-7b_Q3_K_M.gguf](Chronos-Mistral-7b_Q3_K_M.gguf) (3.52 GB) - very small, high quality loss - [Chronos-Mistral-7b_Q3_K_L.gguf](Chronos-Mistral-7b_Q3_K_L.gguf) (3.82 GB) - small, substantial quality loss - [Chronos-Mistral-7b_Q4_K_S.gguf](Chronos-Mistral-7b_Q4_K_S.gguf) (4.14 GB) - small, greater quality loss - [Chronos-Mistral-7b_Q4_K_M.gguf](Chronos-Mistral-7b_Q4_K_M.gguf) (4.37 GB) - medium, balanced quality - recommended - [Chronos-Mistral-7b_Q5_K_S.gguf](Chronos-Mistral-7b_Q5_K_S.gguf) (5 GB) - large, low quality loss - recommended - [Chronos-Mistral-7b_Q5_K_M.gguf](Chronos-Mistral-7b_Q5_K_M.gguf) (5.13 GB) - large, very low quality loss - recommended - [Chronos-Mistral-7b_Q6_K.gguf](Chronos-Mistral-7b_Q6_K.gguf) (5.94 GB) - very large, extremely low quality loss - [Chronos-Mistral-7b_Q8_0.gguf](Chronos-Mistral-7b_Q8_0.gguf) (7.7 GB) - very large, extremely low quality loss - not recommended ## Original model description This model is primarily focused on chat, roleplay, storywriting, with good reasoning and logic. Chronos can generate very long outputs with coherent text, largely due to the human inputs it was trained on, and it supports context length up to 4096 tokens **Up to 16384 with RoPE** with solid coherency. This model uses Alpaca formatting, so for optimal model performance, use it to start the dialogue or story, and if you use a frontend like SillyTavern ENABLE instruction mode: ``` ### Instruction: {Your instruction or question here.} ### Response: ``` Not using the format will make the model perform significantly worse than intended unless it is merged.

zakilj3/AnsemBilly

zakilj3

Entry not found

ymoslem/whisper-small-ga2en-v2

ymoslem

transformers

automatic-speech-recognition

Entry not found

worachot-n/dialogsum_7_contrastive_margin_top_04_random_01_alpha_05

worachot-n

transformers

text2text-generation

Entry not found

imi2/Mixtral-8x22B-v0.1-exl2-3.85bpw

imi2

Entry not found

Homiebear/VenomSM2

Homiebear

--- license: openrail ---

raejeong/vsft_llava_qlora

raejeong

Entry not found

daanjiri/Biomistral_7b_bhc_10

daanjiri

transformers

text-generation

--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

saraataryy/distilbert-base-uncased-finetuned-emotion

saraataryy

transformers

text-classification

--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer datasets: - emotion metrics: - accuracy - f1 model-index: - name: distilbert-base-uncased-finetuned-emotion results: - task: name: Text Classification type: text-classification dataset: name: emotion type: emotion config: split split: validation args: split metrics: - name: Accuracy type: accuracy value: 0.929 - name: F1 type: f1 value: 0.9290812884807271 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-emotion This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.2040 - Accuracy: 0.929 - F1: 0.9291 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.807 | 1.0 | 250 | 0.2902 | 0.915 | 0.9147 | | 0.2325 | 2.0 | 500 | 0.2040 | 0.929 | 0.9291 | ### Framework versions - Transformers 4.38.2 - Pytorch 2.2.1+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2

toghrultahirov/gemma-1.1-7b-it-CausalFT-merged

toghrultahirov

transformers

text-generation

--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Plasmati/AIDRIVEORNOTMODELS8

Plasmati

Entry not found

CodeJesus77/MistralProdFanyi

CodeJesus77

peft

--- library_name: peft base_model: mistralai/Mistral-7B-v0.1 widget: - messages: - role: user content: 高品质的皮革结构在坚韧的凸耳鞋底上打造出一款结实的靴子 pipeline_tag: text-generation --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.10.0

RichardErkhov/databricks_-_dolly-v2-7b-4bits

RichardErkhov

transformers

text-generation

Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) dolly-v2-7b - bnb 4bits - Model creator: https://huggingface.co/databricks/ - Original model: https://huggingface.co/databricks/dolly-v2-7b/ Original model description: --- license: mit language: - en library_name: transformers inference: false datasets: - databricks/databricks-dolly-15k --- # dolly-v2-7b Model Card ## Summary Databricks' `dolly-v2-7b`, an instruction-following large language model trained on the Databricks machine learning platform that is licensed for commercial use. Based on `pythia-6.9b`, Dolly is trained on ~15k instruction/response fine tuning records [`databricks-dolly-15k`](https://github.com/databrickslabs/dolly/tree/master/data) generated by Databricks employees in capability domains from the InstructGPT paper, including brainstorming, classification, closed QA, generation, information extraction, open QA and summarization. `dolly-v2-7b` is not a state-of-the-art model, but does exhibit surprisingly high quality instruction following behavior not characteristic of the foundation model on which it is based. Dolly v2 is also available in these other models sizes: * [dolly-v2-12b](https://huggingface.co/databricks/dolly-v2-12b), a 12 billion parameter based on `pythia-12b` * [dolly-v2-3b](https://huggingface.co/databricks/dolly-v2-3b), a 2.8 billion parameter based on `pythia-2.8b` Please refer to the [dolly GitHub repo](https://github.com/databrickslabs/dolly#getting-started-with-response-generation) for tips on running inference for various GPU configurations. **Owner**: Databricks, Inc. ## Model Overview `dolly-v2-7b` is a 6.9 billion parameter causal language model created by [Databricks](https://databricks.com/) that is derived from [EleutherAI's](https://www.eleuther.ai/) [Pythia-6.9b](https://huggingface.co/EleutherAI/pythia-6.9b) and fine-tuned on a [~15K record instruction corpus](https://github.com/databrickslabs/dolly/tree/master/data) generated by Databricks employees and released under a permissive license (CC-BY-SA) ## Usage To use the model with the `transformers` library on a machine with GPUs, first make sure you have the `transformers` and `accelerate` libraries installed. In a Databricks notebook you could run: ```python %pip install "accelerate>=0.16.0,<1" "transformers[torch]>=4.28.1,<5" "torch>=1.13.1,<2" ``` The instruction following pipeline can be loaded using the `pipeline` function as shown below. This loads a custom `InstructionTextGenerationPipeline` found in the model repo [here](https://huggingface.co/databricks/dolly-v2-3b/blob/main/instruct_pipeline.py), which is why `trust_remote_code=True` is required. Including `torch_dtype=torch.bfloat16` is generally recommended if this type is supported in order to reduce memory usage. It does not appear to impact output quality. It is also fine to remove it if there is sufficient memory. ```python import torch from transformers import pipeline generate_text = pipeline(model="databricks/dolly-v2-7b", torch_dtype=torch.bfloat16, trust_remote_code=True, device_map="auto") ``` You can then use the pipeline to answer instructions: ```python res = generate_text("Explain to me the difference between nuclear fission and fusion.") print(res[0]["generated_text"]) ``` Alternatively, if you prefer to not use `trust_remote_code=True` you can download [instruct_pipeline.py](https://huggingface.co/databricks/dolly-v2-3b/blob/main/instruct_pipeline.py), store it alongside your notebook, and construct the pipeline yourself from the loaded model and tokenizer: ```python import torch from instruct_pipeline import InstructionTextGenerationPipeline from transformers import AutoModelForCausalLM, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("databricks/dolly-v2-7b", padding_side="left") model = AutoModelForCausalLM.from_pretrained("databricks/dolly-v2-7b", device_map="auto", torch_dtype=torch.bfloat16) generate_text = InstructionTextGenerationPipeline(model=model, tokenizer=tokenizer) ``` ### LangChain Usage To use the pipeline with LangChain, you must set `return_full_text=True`, as LangChain expects the full text to be returned and the default for the pipeline is to only return the new text. ```python import torch from transformers import pipeline generate_text = pipeline(model="databricks/dolly-v2-7b", torch_dtype=torch.bfloat16, trust_remote_code=True, device_map="auto", return_full_text=True) ``` You can create a prompt that either has only an instruction or has an instruction with context: ```python from langchain import PromptTemplate, LLMChain from langchain.llms import HuggingFacePipeline # template for an instrution with no input prompt = PromptTemplate( input_variables=["instruction"], template="{instruction}") # template for an instruction with input prompt_with_context = PromptTemplate( input_variables=["instruction", "context"], template="{instruction}\n\nInput:\n{context}") hf_pipeline = HuggingFacePipeline(pipeline=generate_text) llm_chain = LLMChain(llm=hf_pipeline, prompt=prompt) llm_context_chain = LLMChain(llm=hf_pipeline, prompt=prompt_with_context) ``` Example predicting using a simple instruction: ```python print(llm_chain.predict(instruction="Explain to me the difference between nuclear fission and fusion.").lstrip()) ``` Example predicting using an instruction with context: ```python context = """George Washington (February 22, 1732[b] - December 14, 1799) was an American military officer, statesman, and Founding Father who served as the first president of the United States from 1789 to 1797.""" print(llm_context_chain.predict(instruction="When was George Washington president?", context=context).lstrip()) ``` ## Known Limitations ### Performance Limitations **`dolly-v2-7b` is not a state-of-the-art generative language model** and, though quantitative benchmarking is ongoing, is not designed to perform competitively with more modern model architectures or models subject to larger pretraining corpuses. The Dolly model family is under active development, and so any list of shortcomings is unlikely to be exhaustive, but we include known limitations and misfires here as a means to document and share our preliminary findings with the community. In particular, `dolly-v2-7b` struggles with: syntactically complex prompts, programming problems, mathematical operations, factual errors, dates and times, open-ended question answering, hallucination, enumerating lists of specific length, stylistic mimicry, having a sense of humor, etc. Moreover, we find that `dolly-v2-7b` does not have some capabilities, such as well-formatted letter writing, present in the original model. ### Dataset Limitations Like all language models, `dolly-v2-7b` reflects the content and limitations of its training corpuses. - **The Pile**: GPT-J's pre-training corpus contains content mostly collected from the public internet, and like most web-scale datasets, it contains content many users would find objectionable. As such, the model is likely to reflect these shortcomings, potentially overtly in the case it is explicitly asked to produce objectionable content, and sometimes subtly, as in the case of biased or harmful implicit associations. - **`databricks-dolly-15k`**: The training data on which `dolly-v2-7b` is instruction tuned represents natural language instructions generated by Databricks employees during a period spanning March and April 2023 and includes passages from Wikipedia as references passages for instruction categories like closed QA and summarization. To our knowledge it does not contain obscenity, intellectual property or personally identifying information about non-public figures, but it may contain typos and factual errors. The dataset may also reflect biases found in Wikipedia. Finally, the dataset likely reflects the interests and semantic choices of Databricks employees, a demographic which is not representative of the global population at large. Databricks is committed to ongoing research and development efforts to develop helpful, honest and harmless AI technologies that maximize the potential of all individuals and organizations. ### Benchmark Metrics Below you'll find various models benchmark performance on the [EleutherAI LLM Evaluation Harness](https://github.com/EleutherAI/lm-evaluation-harness); model results are sorted by geometric mean to produce an intelligible ordering. As outlined above, these results demonstrate that `dolly-v2-7b` is not state of the art, and in fact underperforms `dolly-v1-6b` in some evaluation benchmarks. We believe this owes to the composition and size of the underlying fine tuning datasets, but a robust statement as to the sources of these variations requires further study. | model | openbookqa | arc_easy | winogrande | hellaswag | arc_challenge | piqa | boolq | gmean | | --------------------------------- | ------------ | ---------- | ------------ | ----------- | --------------- | -------- | -------- | ---------| | EleutherAI/pythia-2.8b | 0.348 | 0.585859 | 0.589582 | 0.591217 | 0.323379 | 0.73395 | 0.638226 | 0.523431 | | EleutherAI/pythia-6.9b | 0.368 | 0.604798 | 0.608524 | 0.631548 | 0.343857 | 0.761153 | 0.6263 | 0.543567 | | databricks/dolly-v2-3b | 0.384 | 0.611532 | 0.589582 | 0.650767 | 0.370307 | 0.742655 | 0.575535 | 0.544886 | | EleutherAI/pythia-12b | 0.364 | 0.627104 | 0.636148 | 0.668094 | 0.346416 | 0.760065 | 0.673394 | 0.559676 | | EleutherAI/gpt-j-6B | 0.382 | 0.621633 | 0.651144 | 0.662617 | 0.363481 | 0.761153 | 0.655963 | 0.565936 | | databricks/dolly-v2-12b | 0.408 | 0.63931 | 0.616417 | 0.707927 | 0.388225 | 0.757889 | 0.568196 | 0.56781 | | databricks/dolly-v2-7b | 0.392 | 0.633838 | 0.607735 | 0.686517 | 0.406997 | 0.750816 | 0.644037 | 0.573487 | | databricks/dolly-v1-6b | 0.41 | 0.62963 | 0.643252 | 0.676758 | 0.384812 | 0.773667 | 0.687768 | 0.583431 | | EleutherAI/gpt-neox-20b | 0.402 | 0.683923 | 0.656669 | 0.7142 | 0.408703 | 0.784004 | 0.695413 | 0.602236 | # Citation ``` @online{DatabricksBlog2023DollyV2, author = {Mike Conover and Matt Hayes and Ankit Mathur and Jianwei Xie and Jun Wan and Sam Shah and Ali Ghodsi and Patrick Wendell and Matei Zaharia and Reynold Xin}, title = {Free Dolly: Introducing the World's First Truly Open Instruction-Tuned LLM}, year = {2023}, url = {https://www.databricks.com/blog/2023/04/12/dolly-first-open-commercially-viable-instruction-tuned-llm}, urldate = {2023-06-30} } ``` # Happy Hacking!

notresort/llama-2-7b-miniguanaco-ak-v4-w-christian

notresort

transformers

text-generation

Entry not found

rfhuang/maui-no-vest-800

rfhuang

diffusers

text-to-image

--- tags: - autotrain - stable-diffusion-xl - stable-diffusion-xl-diffusers - text-to-image - diffusers - lora - template:sd-lora base_model: stabilityai/stable-diffusion-xl-base-1.0 instance_prompt: A photo of a dog named Maui in random situations, taken from a smartphone camera license: openrail++ --- # AutoTrain SDXL LoRA DreamBooth - rfhuang/maui-no-vest-800 <Gallery /> ## Model description These are rfhuang/maui-no-vest-800 LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were trained using [DreamBooth](https://dreambooth.github.io/). LoRA for the text encoder was enabled: False. Special VAE used for training: None. ## Trigger words You should use A photo of a dog named Maui in random situations, taken from a smartphone camera to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](rfhuang/maui-no-vest-800/tree/main) them in the Files & versions tab.

VivekMalipatel23/mDeBERTa-v3-base-text-emotion-classification

VivekMalipatel23

transformers

zero-shot-classification

--- language: - multilingual - zh - ja - ar - ko - de - fr - es - pt - hi - id - it - tr - ru - bn - ur - mr - ta - vi - fa - pl - uk - nl - sv - he - sw - ps tags: - text-classification - zero-shot-classification - nli - pytorch pipeline_tag: zero-shot-classification library_name: transformers license: mit base_model: MoritzLaurer/mDeBERTa-v3-base-xnli-multilingual-nli-2mil7 --- Multilingual mDeBERTa base model fineted on Text_emotions dataset. Dataset link : https://www.kaggle.com/datasets/nelgiriyewithana/emotions/data Finetuned for classifying text into sadness (0) joy (1) love (2) anger (3) fear (4) and surprise (5) emotions.

RichardErkhov/databricks_-_dolly-v2-7b-8bits

RichardErkhov

transformers

text-generation

Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) dolly-v2-7b - bnb 8bits - Model creator: https://huggingface.co/databricks/ - Original model: https://huggingface.co/databricks/dolly-v2-7b/ Original model description: --- license: mit language: - en library_name: transformers inference: false datasets: - databricks/databricks-dolly-15k --- # dolly-v2-7b Model Card ## Summary Databricks' `dolly-v2-7b`, an instruction-following large language model trained on the Databricks machine learning platform that is licensed for commercial use. Based on `pythia-6.9b`, Dolly is trained on ~15k instruction/response fine tuning records [`databricks-dolly-15k`](https://github.com/databrickslabs/dolly/tree/master/data) generated by Databricks employees in capability domains from the InstructGPT paper, including brainstorming, classification, closed QA, generation, information extraction, open QA and summarization. `dolly-v2-7b` is not a state-of-the-art model, but does exhibit surprisingly high quality instruction following behavior not characteristic of the foundation model on which it is based. Dolly v2 is also available in these other models sizes: * [dolly-v2-12b](https://huggingface.co/databricks/dolly-v2-12b), a 12 billion parameter based on `pythia-12b` * [dolly-v2-3b](https://huggingface.co/databricks/dolly-v2-3b), a 2.8 billion parameter based on `pythia-2.8b` Please refer to the [dolly GitHub repo](https://github.com/databrickslabs/dolly#getting-started-with-response-generation) for tips on running inference for various GPU configurations. **Owner**: Databricks, Inc. ## Model Overview `dolly-v2-7b` is a 6.9 billion parameter causal language model created by [Databricks](https://databricks.com/) that is derived from [EleutherAI's](https://www.eleuther.ai/) [Pythia-6.9b](https://huggingface.co/EleutherAI/pythia-6.9b) and fine-tuned on a [~15K record instruction corpus](https://github.com/databrickslabs/dolly/tree/master/data) generated by Databricks employees and released under a permissive license (CC-BY-SA) ## Usage To use the model with the `transformers` library on a machine with GPUs, first make sure you have the `transformers` and `accelerate` libraries installed. In a Databricks notebook you could run: ```python %pip install "accelerate>=0.16.0,<1" "transformers[torch]>=4.28.1,<5" "torch>=1.13.1,<2" ``` The instruction following pipeline can be loaded using the `pipeline` function as shown below. This loads a custom `InstructionTextGenerationPipeline` found in the model repo [here](https://huggingface.co/databricks/dolly-v2-3b/blob/main/instruct_pipeline.py), which is why `trust_remote_code=True` is required. Including `torch_dtype=torch.bfloat16` is generally recommended if this type is supported in order to reduce memory usage. It does not appear to impact output quality. It is also fine to remove it if there is sufficient memory. ```python import torch from transformers import pipeline generate_text = pipeline(model="databricks/dolly-v2-7b", torch_dtype=torch.bfloat16, trust_remote_code=True, device_map="auto") ``` You can then use the pipeline to answer instructions: ```python res = generate_text("Explain to me the difference between nuclear fission and fusion.") print(res[0]["generated_text"]) ``` Alternatively, if you prefer to not use `trust_remote_code=True` you can download [instruct_pipeline.py](https://huggingface.co/databricks/dolly-v2-3b/blob/main/instruct_pipeline.py), store it alongside your notebook, and construct the pipeline yourself from the loaded model and tokenizer: ```python import torch from instruct_pipeline import InstructionTextGenerationPipeline from transformers import AutoModelForCausalLM, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("databricks/dolly-v2-7b", padding_side="left") model = AutoModelForCausalLM.from_pretrained("databricks/dolly-v2-7b", device_map="auto", torch_dtype=torch.bfloat16) generate_text = InstructionTextGenerationPipeline(model=model, tokenizer=tokenizer) ``` ### LangChain Usage To use the pipeline with LangChain, you must set `return_full_text=True`, as LangChain expects the full text to be returned and the default for the pipeline is to only return the new text. ```python import torch from transformers import pipeline generate_text = pipeline(model="databricks/dolly-v2-7b", torch_dtype=torch.bfloat16, trust_remote_code=True, device_map="auto", return_full_text=True) ``` You can create a prompt that either has only an instruction or has an instruction with context: ```python from langchain import PromptTemplate, LLMChain from langchain.llms import HuggingFacePipeline # template for an instrution with no input prompt = PromptTemplate( input_variables=["instruction"], template="{instruction}") # template for an instruction with input prompt_with_context = PromptTemplate( input_variables=["instruction", "context"], template="{instruction}\n\nInput:\n{context}") hf_pipeline = HuggingFacePipeline(pipeline=generate_text) llm_chain = LLMChain(llm=hf_pipeline, prompt=prompt) llm_context_chain = LLMChain(llm=hf_pipeline, prompt=prompt_with_context) ``` Example predicting using a simple instruction: ```python print(llm_chain.predict(instruction="Explain to me the difference between nuclear fission and fusion.").lstrip()) ``` Example predicting using an instruction with context: ```python context = """George Washington (February 22, 1732[b] - December 14, 1799) was an American military officer, statesman, and Founding Father who served as the first president of the United States from 1789 to 1797.""" print(llm_context_chain.predict(instruction="When was George Washington president?", context=context).lstrip()) ``` ## Known Limitations ### Performance Limitations **`dolly-v2-7b` is not a state-of-the-art generative language model** and, though quantitative benchmarking is ongoing, is not designed to perform competitively with more modern model architectures or models subject to larger pretraining corpuses. The Dolly model family is under active development, and so any list of shortcomings is unlikely to be exhaustive, but we include known limitations and misfires here as a means to document and share our preliminary findings with the community. In particular, `dolly-v2-7b` struggles with: syntactically complex prompts, programming problems, mathematical operations, factual errors, dates and times, open-ended question answering, hallucination, enumerating lists of specific length, stylistic mimicry, having a sense of humor, etc. Moreover, we find that `dolly-v2-7b` does not have some capabilities, such as well-formatted letter writing, present in the original model. ### Dataset Limitations Like all language models, `dolly-v2-7b` reflects the content and limitations of its training corpuses. - **The Pile**: GPT-J's pre-training corpus contains content mostly collected from the public internet, and like most web-scale datasets, it contains content many users would find objectionable. As such, the model is likely to reflect these shortcomings, potentially overtly in the case it is explicitly asked to produce objectionable content, and sometimes subtly, as in the case of biased or harmful implicit associations. - **`databricks-dolly-15k`**: The training data on which `dolly-v2-7b` is instruction tuned represents natural language instructions generated by Databricks employees during a period spanning March and April 2023 and includes passages from Wikipedia as references passages for instruction categories like closed QA and summarization. To our knowledge it does not contain obscenity, intellectual property or personally identifying information about non-public figures, but it may contain typos and factual errors. The dataset may also reflect biases found in Wikipedia. Finally, the dataset likely reflects the interests and semantic choices of Databricks employees, a demographic which is not representative of the global population at large. Databricks is committed to ongoing research and development efforts to develop helpful, honest and harmless AI technologies that maximize the potential of all individuals and organizations. ### Benchmark Metrics Below you'll find various models benchmark performance on the [EleutherAI LLM Evaluation Harness](https://github.com/EleutherAI/lm-evaluation-harness); model results are sorted by geometric mean to produce an intelligible ordering. As outlined above, these results demonstrate that `dolly-v2-7b` is not state of the art, and in fact underperforms `dolly-v1-6b` in some evaluation benchmarks. We believe this owes to the composition and size of the underlying fine tuning datasets, but a robust statement as to the sources of these variations requires further study. | model | openbookqa | arc_easy | winogrande | hellaswag | arc_challenge | piqa | boolq | gmean | | --------------------------------- | ------------ | ---------- | ------------ | ----------- | --------------- | -------- | -------- | ---------| | EleutherAI/pythia-2.8b | 0.348 | 0.585859 | 0.589582 | 0.591217 | 0.323379 | 0.73395 | 0.638226 | 0.523431 | | EleutherAI/pythia-6.9b | 0.368 | 0.604798 | 0.608524 | 0.631548 | 0.343857 | 0.761153 | 0.6263 | 0.543567 | | databricks/dolly-v2-3b | 0.384 | 0.611532 | 0.589582 | 0.650767 | 0.370307 | 0.742655 | 0.575535 | 0.544886 | | EleutherAI/pythia-12b | 0.364 | 0.627104 | 0.636148 | 0.668094 | 0.346416 | 0.760065 | 0.673394 | 0.559676 | | EleutherAI/gpt-j-6B | 0.382 | 0.621633 | 0.651144 | 0.662617 | 0.363481 | 0.761153 | 0.655963 | 0.565936 | | databricks/dolly-v2-12b | 0.408 | 0.63931 | 0.616417 | 0.707927 | 0.388225 | 0.757889 | 0.568196 | 0.56781 | | databricks/dolly-v2-7b | 0.392 | 0.633838 | 0.607735 | 0.686517 | 0.406997 | 0.750816 | 0.644037 | 0.573487 | | databricks/dolly-v1-6b | 0.41 | 0.62963 | 0.643252 | 0.676758 | 0.384812 | 0.773667 | 0.687768 | 0.583431 | | EleutherAI/gpt-neox-20b | 0.402 | 0.683923 | 0.656669 | 0.7142 | 0.408703 | 0.784004 | 0.695413 | 0.602236 | # Citation ``` @online{DatabricksBlog2023DollyV2, author = {Mike Conover and Matt Hayes and Ankit Mathur and Jianwei Xie and Jun Wan and Sam Shah and Ali Ghodsi and Patrick Wendell and Matei Zaharia and Reynold Xin}, title = {Free Dolly: Introducing the World's First Truly Open Instruction-Tuned LLM}, year = {2023}, url = {https://www.databricks.com/blog/2023/04/12/dolly-first-open-commercially-viable-instruction-tuned-llm}, urldate = {2023-06-30} } ``` # Happy Hacking!

ashishp-wiai/ClipArt_LoRA_40-2024-04-14

ashishp-wiai

Entry not found

SpideyDLK/wav2vec2-large-xls-r-300m-sinhala-aug-data-with-original-split-part2

SpideyDLK

transformers

automatic-speech-recognition

--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

feizhe/vit-base-patch16-224-in21k-pheno-run5

feizhe

transformers

image-classification

--- license: apache-2.0 base_model: google/vit-base-patch16-224-in21k tags: - generated_from_keras_callback model-index: - name: feizhe/vit-base-patch16-224-in21k-pheno-run5 results: [] --- <!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # feizhe/vit-base-patch16-224-in21k-pheno-run5 This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.0782 - Train Accuracy: 0.9985 - Train Top-3-accuracy: 1.0 - Validation Loss: 1.4406 - Validation Accuracy: 0.5731 - Validation Top-3-accuracy: 0.9298 - Epoch: 2 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'inner_optimizer': {'class_name': 'AdamWeightDecay', 'config': {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 1615, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}}, 'dynamic': True, 'initial_scale': 32768.0, 'dynamic_growth_steps': 2000} - training_precision: mixed_float16 ### Training results | Train Loss | Train Accuracy | Train Top-3-accuracy | Validation Loss | Validation Accuracy | Validation Top-3-accuracy | Epoch | |:----------:|:--------------:|:--------------------:|:---------------:|:-------------------:|:-------------------------:|:-----:| | 0.7826 | 0.7716 | 0.9705 | 1.1364 | 0.5965 | 0.9532 | 0 | | 0.1564 | 0.9891 | 1.0 | 1.3742 | 0.5731 | 0.9181 | 1 | | 0.0782 | 0.9985 | 1.0 | 1.4406 | 0.5731 | 0.9298 | 2 | ### Framework versions - Transformers 4.31.0 - TensorFlow 2.10.0 - Datasets 2.18.0 - Tokenizers 0.13.3

RichardErkhov/databricks_-_dolly-v2-3b-4bits

RichardErkhov

transformers

text-generation

Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) dolly-v2-3b - bnb 4bits - Model creator: https://huggingface.co/databricks/ - Original model: https://huggingface.co/databricks/dolly-v2-3b/ Original model description: --- license: mit language: - en library_name: transformers inference: false datasets: - databricks/databricks-dolly-15k --- # dolly-v2-3b Model Card ## Summary Databricks' `dolly-v2-3b`, an instruction-following large language model trained on the Databricks machine learning platform that is licensed for commercial use. Based on `pythia-2.8b`, Dolly is trained on ~15k instruction/response fine tuning records [`databricks-dolly-15k`](https://github.com/databrickslabs/dolly/tree/master/data) generated by Databricks employees in capability domains from the InstructGPT paper, including brainstorming, classification, closed QA, generation, information extraction, open QA and summarization. `dolly-v2-3b` is not a state-of-the-art model, but does exhibit surprisingly high quality instruction following behavior not characteristic of the foundation model on which it is based. Dolly v2 is also available in these larger models sizes: * [dolly-v2-12b](https://huggingface.co/databricks/dolly-v2-12b), a 12 billion parameter based on `pythia-12b` * [dolly-v2-7b](https://huggingface.co/databricks/dolly-v2-7b), a 6.9 billion parameter based on `pythia-6.9b` Please refer to the [dolly GitHub repo](https://github.com/databrickslabs/dolly#getting-started-with-response-generation) for tips on running inference for various GPU configurations. **Owner**: Databricks, Inc. ## Model Overview `dolly-v2-3b` is a 2.8 billion parameter causal language model created by [Databricks](https://databricks.com/) that is derived from [EleutherAI's](https://www.eleuther.ai/) [Pythia-2.8b](https://huggingface.co/EleutherAI/pythia-2.8b) and fine-tuned on a [~15K record instruction corpus](https://github.com/databrickslabs/dolly/tree/master/data) generated by Databricks employees and released under a permissive license (CC-BY-SA) ## Usage To use the model with the `transformers` library on a machine with GPUs, first make sure you have the `transformers` and `accelerate` libraries installed. In a Databricks notebook you could run: ```python %pip install "accelerate>=0.16.0,<1" "transformers[torch]>=4.28.1,<5" "torch>=1.13.1,<2" ``` The instruction following pipeline can be loaded using the `pipeline` function as shown below. This loads a custom `InstructionTextGenerationPipeline` found in the model repo [here](https://huggingface.co/databricks/dolly-v2-3b/blob/main/instruct_pipeline.py), which is why `trust_remote_code=True` is required. Including `torch_dtype=torch.bfloat16` is generally recommended if this type is supported in order to reduce memory usage. It does not appear to impact output quality. It is also fine to remove it if there is sufficient memory. ```python import torch from transformers import pipeline generate_text = pipeline(model="databricks/dolly-v2-3b", torch_dtype=torch.bfloat16, trust_remote_code=True, device_map="auto") ``` You can then use the pipeline to answer instructions: ```python res = generate_text("Explain to me the difference between nuclear fission and fusion.") print(res[0]["generated_text"]) ``` Alternatively, if you prefer to not use `trust_remote_code=True` you can download [instruct_pipeline.py](https://huggingface.co/databricks/dolly-v2-3b/blob/main/instruct_pipeline.py), store it alongside your notebook, and construct the pipeline yourself from the loaded model and tokenizer: ```python import torch from instruct_pipeline import InstructionTextGenerationPipeline from transformers import AutoModelForCausalLM, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("databricks/dolly-v2-3b", padding_side="left") model = AutoModelForCausalLM.from_pretrained("databricks/dolly-v2-3b", device_map="auto", torch_dtype=torch.bfloat16) generate_text = InstructionTextGenerationPipeline(model=model, tokenizer=tokenizer) ``` ### LangChain Usage To use the pipeline with LangChain, you must set `return_full_text=True`, as LangChain expects the full text to be returned and the default for the pipeline is to only return the new text. ```python import torch from transformers import pipeline generate_text = pipeline(model="databricks/dolly-v2-3b", torch_dtype=torch.bfloat16, trust_remote_code=True, device_map="auto", return_full_text=True) ``` You can create a prompt that either has only an instruction or has an instruction with context: ```python from langchain import PromptTemplate, LLMChain from langchain.llms import HuggingFacePipeline # template for an instrution with no input prompt = PromptTemplate( input_variables=["instruction"], template="{instruction}") # template for an instruction with input prompt_with_context = PromptTemplate( input_variables=["instruction", "context"], template="{instruction}\n\nInput:\n{context}") hf_pipeline = HuggingFacePipeline(pipeline=generate_text) llm_chain = LLMChain(llm=hf_pipeline, prompt=prompt) llm_context_chain = LLMChain(llm=hf_pipeline, prompt=prompt_with_context) ``` Example predicting using a simple instruction: ```python print(llm_chain.predict(instruction="Explain to me the difference between nuclear fission and fusion.").lstrip()) ``` Example predicting using an instruction with context: ```python context = """George Washington (February 22, 1732[b] - December 14, 1799) was an American military officer, statesman, and Founding Father who served as the first president of the United States from 1789 to 1797.""" print(llm_context_chain.predict(instruction="When was George Washington president?", context=context).lstrip()) ``` ## Known Limitations ### Performance Limitations **`dolly-v2-3b` is not a state-of-the-art generative language model** and, though quantitative benchmarking is ongoing, is not designed to perform competitively with more modern model architectures or models subject to larger pretraining corpuses. The Dolly model family is under active development, and so any list of shortcomings is unlikely to be exhaustive, but we include known limitations and misfires here as a means to document and share our preliminary findings with the community. In particular, `dolly-v2-3b` struggles with: syntactically complex prompts, programming problems, mathematical operations, factual errors, dates and times, open-ended question answering, hallucination, enumerating lists of specific length, stylistic mimicry, having a sense of humor, etc. Moreover, we find that `dolly-v2-3b` does not have some capabilities, such as well-formatted letter writing, present in the original model. ### Dataset Limitations Like all language models, `dolly-v2-3b` reflects the content and limitations of its training corpuses. - **The Pile**: GPT-J's pre-training corpus contains content mostly collected from the public internet, and like most web-scale datasets, it contains content many users would find objectionable. As such, the model is likely to reflect these shortcomings, potentially overtly in the case it is explicitly asked to produce objectionable content, and sometimes subtly, as in the case of biased or harmful implicit associations. - **`databricks-dolly-15k`**: The training data on which `dolly-v2-3b` is instruction tuned represents natural language instructions generated by Databricks employees during a period spanning March and April 2023 and includes passages from Wikipedia as references passages for instruction categories like closed QA and summarization. To our knowledge it does not contain obscenity, intellectual property or personally identifying information about non-public figures, but it may contain typos and factual errors. The dataset may also reflect biases found in Wikipedia. Finally, the dataset likely reflects the interests and semantic choices of Databricks employees, a demographic which is not representative of the global population at large. Databricks is committed to ongoing research and development efforts to develop helpful, honest and harmless AI technologies that maximize the potential of all individuals and organizations. ### Benchmark Metrics Below you'll find various models benchmark performance on the [EleutherAI LLM Evaluation Harness](https://github.com/EleutherAI/lm-evaluation-harness); model results are sorted by geometric mean to produce an intelligible ordering. As outlined above, these results demonstrate that `dolly-v2-3b` is not state of the art. It underperforms `dolly-v1-6b` in the evaluation benchmarks, which is not surprising considering it has half the number of parameters. | model | openbookqa | arc_easy | winogrande | hellaswag | arc_challenge | piqa | boolq | gmean | | --------------------------------- | ------------ | ---------- | ------------ | ----------- | --------------- | -------- | -------- | ---------| | EleutherAI/pythia-2.8b | 0.348 | 0.585859 | 0.589582 | 0.591217 | 0.323379 | 0.73395 | 0.638226 | 0.523431 | | EleutherAI/pythia-6.9b | 0.368 | 0.604798 | 0.608524 | 0.631548 | 0.343857 | 0.761153 | 0.6263 | 0.543567 | | databricks/dolly-v2-3b | 0.384 | 0.611532 | 0.589582 | 0.650767 | 0.370307 | 0.742655 | 0.575535 | 0.544886 | | EleutherAI/pythia-12b | 0.364 | 0.627104 | 0.636148 | 0.668094 | 0.346416 | 0.760065 | 0.673394 | 0.559676 | | EleutherAI/gpt-j-6B | 0.382 | 0.621633 | 0.651144 | 0.662617 | 0.363481 | 0.761153 | 0.655963 | 0.565936 | | databricks/dolly-v2-12b | 0.408 | 0.63931 | 0.616417 | 0.707927 | 0.388225 | 0.757889 | 0.568196 | 0.56781 | | databricks/dolly-v2-7b | 0.392 | 0.633838 | 0.607735 | 0.686517 | 0.406997 | 0.750816 | 0.644037 | 0.573487 | | databricks/dolly-v1-6b | 0.41 | 0.62963 | 0.643252 | 0.676758 | 0.384812 | 0.773667 | 0.687768 | 0.583431 | | EleutherAI/gpt-neox-20b | 0.402 | 0.683923 | 0.656669 | 0.7142 | 0.408703 | 0.784004 | 0.695413 | 0.602236 | # Citation ``` @online{DatabricksBlog2023DollyV2, author = {Mike Conover and Matt Hayes and Ankit Mathur and Jianwei Xie and Jun Wan and Sam Shah and Ali Ghodsi and Patrick Wendell and Matei Zaharia and Reynold Xin}, title = {Free Dolly: Introducing the World's First Truly Open Instruction-Tuned LLM}, year = {2023}, url = {https://www.databricks.com/blog/2023/04/12/dolly-first-open-commercially-viable-instruction-tuned-llm}, urldate = {2023-06-30} } ``` # Happy Hacking!

RichardErkhov/databricks_-_dolly-v2-3b-8bits

RichardErkhov

transformers

text-generation

Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) dolly-v2-3b - bnb 8bits - Model creator: https://huggingface.co/databricks/ - Original model: https://huggingface.co/databricks/dolly-v2-3b/ Original model description: --- license: mit language: - en library_name: transformers inference: false datasets: - databricks/databricks-dolly-15k --- # dolly-v2-3b Model Card ## Summary Databricks' `dolly-v2-3b`, an instruction-following large language model trained on the Databricks machine learning platform that is licensed for commercial use. Based on `pythia-2.8b`, Dolly is trained on ~15k instruction/response fine tuning records [`databricks-dolly-15k`](https://github.com/databrickslabs/dolly/tree/master/data) generated by Databricks employees in capability domains from the InstructGPT paper, including brainstorming, classification, closed QA, generation, information extraction, open QA and summarization. `dolly-v2-3b` is not a state-of-the-art model, but does exhibit surprisingly high quality instruction following behavior not characteristic of the foundation model on which it is based. Dolly v2 is also available in these larger models sizes: * [dolly-v2-12b](https://huggingface.co/databricks/dolly-v2-12b), a 12 billion parameter based on `pythia-12b` * [dolly-v2-7b](https://huggingface.co/databricks/dolly-v2-7b), a 6.9 billion parameter based on `pythia-6.9b` Please refer to the [dolly GitHub repo](https://github.com/databrickslabs/dolly#getting-started-with-response-generation) for tips on running inference for various GPU configurations. **Owner**: Databricks, Inc. ## Model Overview `dolly-v2-3b` is a 2.8 billion parameter causal language model created by [Databricks](https://databricks.com/) that is derived from [EleutherAI's](https://www.eleuther.ai/) [Pythia-2.8b](https://huggingface.co/EleutherAI/pythia-2.8b) and fine-tuned on a [~15K record instruction corpus](https://github.com/databrickslabs/dolly/tree/master/data) generated by Databricks employees and released under a permissive license (CC-BY-SA) ## Usage To use the model with the `transformers` library on a machine with GPUs, first make sure you have the `transformers` and `accelerate` libraries installed. In a Databricks notebook you could run: ```python %pip install "accelerate>=0.16.0,<1" "transformers[torch]>=4.28.1,<5" "torch>=1.13.1,<2" ``` The instruction following pipeline can be loaded using the `pipeline` function as shown below. This loads a custom `InstructionTextGenerationPipeline` found in the model repo [here](https://huggingface.co/databricks/dolly-v2-3b/blob/main/instruct_pipeline.py), which is why `trust_remote_code=True` is required. Including `torch_dtype=torch.bfloat16` is generally recommended if this type is supported in order to reduce memory usage. It does not appear to impact output quality. It is also fine to remove it if there is sufficient memory. ```python import torch from transformers import pipeline generate_text = pipeline(model="databricks/dolly-v2-3b", torch_dtype=torch.bfloat16, trust_remote_code=True, device_map="auto") ``` You can then use the pipeline to answer instructions: ```python res = generate_text("Explain to me the difference between nuclear fission and fusion.") print(res[0]["generated_text"]) ``` Alternatively, if you prefer to not use `trust_remote_code=True` you can download [instruct_pipeline.py](https://huggingface.co/databricks/dolly-v2-3b/blob/main/instruct_pipeline.py), store it alongside your notebook, and construct the pipeline yourself from the loaded model and tokenizer: ```python import torch from instruct_pipeline import InstructionTextGenerationPipeline from transformers import AutoModelForCausalLM, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("databricks/dolly-v2-3b", padding_side="left") model = AutoModelForCausalLM.from_pretrained("databricks/dolly-v2-3b", device_map="auto", torch_dtype=torch.bfloat16) generate_text = InstructionTextGenerationPipeline(model=model, tokenizer=tokenizer) ``` ### LangChain Usage To use the pipeline with LangChain, you must set `return_full_text=True`, as LangChain expects the full text to be returned and the default for the pipeline is to only return the new text. ```python import torch from transformers import pipeline generate_text = pipeline(model="databricks/dolly-v2-3b", torch_dtype=torch.bfloat16, trust_remote_code=True, device_map="auto", return_full_text=True) ``` You can create a prompt that either has only an instruction or has an instruction with context: ```python from langchain import PromptTemplate, LLMChain from langchain.llms import HuggingFacePipeline # template for an instrution with no input prompt = PromptTemplate( input_variables=["instruction"], template="{instruction}") # template for an instruction with input prompt_with_context = PromptTemplate( input_variables=["instruction", "context"], template="{instruction}\n\nInput:\n{context}") hf_pipeline = HuggingFacePipeline(pipeline=generate_text) llm_chain = LLMChain(llm=hf_pipeline, prompt=prompt) llm_context_chain = LLMChain(llm=hf_pipeline, prompt=prompt_with_context) ``` Example predicting using a simple instruction: ```python print(llm_chain.predict(instruction="Explain to me the difference between nuclear fission and fusion.").lstrip()) ``` Example predicting using an instruction with context: ```python context = """George Washington (February 22, 1732[b] - December 14, 1799) was an American military officer, statesman, and Founding Father who served as the first president of the United States from 1789 to 1797.""" print(llm_context_chain.predict(instruction="When was George Washington president?", context=context).lstrip()) ``` ## Known Limitations ### Performance Limitations **`dolly-v2-3b` is not a state-of-the-art generative language model** and, though quantitative benchmarking is ongoing, is not designed to perform competitively with more modern model architectures or models subject to larger pretraining corpuses. The Dolly model family is under active development, and so any list of shortcomings is unlikely to be exhaustive, but we include known limitations and misfires here as a means to document and share our preliminary findings with the community. In particular, `dolly-v2-3b` struggles with: syntactically complex prompts, programming problems, mathematical operations, factual errors, dates and times, open-ended question answering, hallucination, enumerating lists of specific length, stylistic mimicry, having a sense of humor, etc. Moreover, we find that `dolly-v2-3b` does not have some capabilities, such as well-formatted letter writing, present in the original model. ### Dataset Limitations Like all language models, `dolly-v2-3b` reflects the content and limitations of its training corpuses. - **The Pile**: GPT-J's pre-training corpus contains content mostly collected from the public internet, and like most web-scale datasets, it contains content many users would find objectionable. As such, the model is likely to reflect these shortcomings, potentially overtly in the case it is explicitly asked to produce objectionable content, and sometimes subtly, as in the case of biased or harmful implicit associations. - **`databricks-dolly-15k`**: The training data on which `dolly-v2-3b` is instruction tuned represents natural language instructions generated by Databricks employees during a period spanning March and April 2023 and includes passages from Wikipedia as references passages for instruction categories like closed QA and summarization. To our knowledge it does not contain obscenity, intellectual property or personally identifying information about non-public figures, but it may contain typos and factual errors. The dataset may also reflect biases found in Wikipedia. Finally, the dataset likely reflects the interests and semantic choices of Databricks employees, a demographic which is not representative of the global population at large. Databricks is committed to ongoing research and development efforts to develop helpful, honest and harmless AI technologies that maximize the potential of all individuals and organizations. ### Benchmark Metrics Below you'll find various models benchmark performance on the [EleutherAI LLM Evaluation Harness](https://github.com/EleutherAI/lm-evaluation-harness); model results are sorted by geometric mean to produce an intelligible ordering. As outlined above, these results demonstrate that `dolly-v2-3b` is not state of the art. It underperforms `dolly-v1-6b` in the evaluation benchmarks, which is not surprising considering it has half the number of parameters. | model | openbookqa | arc_easy | winogrande | hellaswag | arc_challenge | piqa | boolq | gmean | | --------------------------------- | ------------ | ---------- | ------------ | ----------- | --------------- | -------- | -------- | ---------| | EleutherAI/pythia-2.8b | 0.348 | 0.585859 | 0.589582 | 0.591217 | 0.323379 | 0.73395 | 0.638226 | 0.523431 | | EleutherAI/pythia-6.9b | 0.368 | 0.604798 | 0.608524 | 0.631548 | 0.343857 | 0.761153 | 0.6263 | 0.543567 | | databricks/dolly-v2-3b | 0.384 | 0.611532 | 0.589582 | 0.650767 | 0.370307 | 0.742655 | 0.575535 | 0.544886 | | EleutherAI/pythia-12b | 0.364 | 0.627104 | 0.636148 | 0.668094 | 0.346416 | 0.760065 | 0.673394 | 0.559676 | | EleutherAI/gpt-j-6B | 0.382 | 0.621633 | 0.651144 | 0.662617 | 0.363481 | 0.761153 | 0.655963 | 0.565936 | | databricks/dolly-v2-12b | 0.408 | 0.63931 | 0.616417 | 0.707927 | 0.388225 | 0.757889 | 0.568196 | 0.56781 | | databricks/dolly-v2-7b | 0.392 | 0.633838 | 0.607735 | 0.686517 | 0.406997 | 0.750816 | 0.644037 | 0.573487 | | databricks/dolly-v1-6b | 0.41 | 0.62963 | 0.643252 | 0.676758 | 0.384812 | 0.773667 | 0.687768 | 0.583431 | | EleutherAI/gpt-neox-20b | 0.402 | 0.683923 | 0.656669 | 0.7142 | 0.408703 | 0.784004 | 0.695413 | 0.602236 | # Citation ``` @online{DatabricksBlog2023DollyV2, author = {Mike Conover and Matt Hayes and Ankit Mathur and Jianwei Xie and Jun Wan and Sam Shah and Ali Ghodsi and Patrick Wendell and Matei Zaharia and Reynold Xin}, title = {Free Dolly: Introducing the World's First Truly Open Instruction-Tuned LLM}, year = {2023}, url = {https://www.databricks.com/blog/2023/04/12/dolly-first-open-commercially-viable-instruction-tuned-llm}, urldate = {2023-06-30} } ``` # Happy Hacking!

stinoco/whisper-large-v3-clinical-assistance

stinoco

Entry not found

Pepus/results

Pepus

Entry not found

lanzv/ClinicalBERTQA_83_54

lanzv

transformers

question-answering

Entry not found

Mou11209203/distilbert-base-multilingual-cased_regression_finetuned_ptt

Mou11209203

transformers

text-classification

--- license: apache-2.0 base_model: distilbert/distilbert-base-multilingual-cased tags: - generated_from_trainer model-index: - name: distilbert-base-multilingual-cased_regression_finetuned_ptt results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-multilingual-cased_regression_finetuned_ptt This model is a fine-tuned version of [distilbert/distilbert-base-multilingual-cased](https://huggingface.co/distilbert/distilbert-base-multilingual-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.8825 - Mse: 1.8825 - Mae: 1.0172 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 500 - num_epochs: 10 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Mse | Mae | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:| | 1.8977 | 1.0 | 2062 | 1.8815 | 1.8815 | 1.0164 | | 1.935 | 2.0 | 4124 | 1.8825 | 1.8825 | 1.0172 | | 1.8602 | 3.0 | 6186 | 1.8810 | 1.8810 | 1.0155 | | 1.7752 | 4.0 | 8248 | 1.8811 | 1.8811 | 1.0147 | ### Framework versions - Transformers 4.39.3 - Pytorch 2.2.1 - Datasets 2.18.0 - Tokenizers 0.15.2

kthammana/MLMA-Lab8-FinetunedBioGPT

kthammana

transformers

token-classification

--- license: mit base_model: microsoft/biogpt tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: MLMA-Lab8-FinetunedBioGPT results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # MLMA-Lab8-FinetunedBioGPT This model is a fine-tuned version of [microsoft/biogpt](https://huggingface.co/microsoft/biogpt) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.1493 - Precision: 0.4385 - Recall: 0.5438 - F1: 0.4855 - Accuracy: 0.9567 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.2947 | 1.0 | 679 | 0.1679 | 0.3119 | 0.4307 | 0.3618 | 0.9454 | | 0.1696 | 2.0 | 1358 | 0.1627 | 0.3868 | 0.5273 | 0.4462 | 0.9518 | | 0.0969 | 3.0 | 2037 | 0.1493 | 0.4385 | 0.5438 | 0.4855 | 0.9567 | ### Framework versions - Transformers 4.38.2 - Pytorch 2.2.1+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2

boxomcfoxo/YiffyEstopianMaid-13B-GGUF

boxomcfoxo

--- tags: - llama-cpp - gguf-my-repo license: llama2 --- # boxomcfoxo/YiffyEstopianMaid-13B-Q5_K_M-GGUF This model was converted to GGUF format from [`KatyMergeTesting/YiffyEstopianMaid-13B`](https://huggingface.co/KatyMergeTesting/YiffyEstopianMaid-13B) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/KatyMergeTesting/YiffyEstopianMaid-13B) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew. ```bash brew install ggerganov/ggerganov/llama.cpp ``` Invoke the llama.cpp server or the CLI. CLI: ```bash llama-cli --hf-repo boxomcfoxo/YiffyEstopianMaid-13B-Q5_K_M-GGUF --model yiffyestopianmaid-13b.Q5_K_M.gguf -p "The meaning to life and the universe is" ``` Server: ```bash llama-server --hf-repo boxomcfoxo/YiffyEstopianMaid-13B-Q5_K_M-GGUF --model yiffyestopianmaid-13b.Q5_K_M.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. ``` git clone https://github.com/ggerganov/llama.cpp && cd llama.cpp && make && ./main -m yiffyestopianmaid-13b.Q5_K_M.gguf -n 128 ```

cstr/Spaetzle-v60-7b

cstr

transformers

text-generation

--- tags: - merge - mergekit - lazymergekit - abideen/AlphaMonarch-dora base_model: - abideen/AlphaMonarch-dora --- # Spaetzle-v60-7b Spaetzle-v60-7b is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing): * [abideen/AlphaMonarch-dora](https://huggingface.co/abideen/AlphaMonarch-dora) ## 🧩 Configuration ```yaml models: - model: cstr/Spaetzle-v58-7b # no parameters necessary for base model - model: abideen/AlphaMonarch-dora parameters: density: 0.60 weight: 0.30 merge_method: dare_ties base_model: cstr/Spaetzle-v58-7b parameters: int8_mask: true dtype: bfloat16 random_seed: 0 tokenizer_source: base ``` ## 💻 Usage ```python !pip install -qU transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "cstr/Spaetzle-v60-7b" messages = [{"role": "user", "content": "What is a large language model?"}] tokenizer = AutoTokenizer.from_pretrained(model) prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) pipeline = transformers.pipeline( "text-generation", model=model, torch_dtype=torch.float16, device_map="auto", ) outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95) print(outputs[0]["generated_text"]) ```

RichardErkhov/databricks_-_dolly-v2-12b-4bits

RichardErkhov

transformers

text-generation

Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) dolly-v2-12b - bnb 4bits - Model creator: https://huggingface.co/databricks/ - Original model: https://huggingface.co/databricks/dolly-v2-12b/ Original model description: --- license: mit language: - en library_name: transformers inference: false datasets: - databricks/databricks-dolly-15k --- # dolly-v2-12b Model Card ## Summary Databricks' `dolly-v2-12b`, an instruction-following large language model trained on the Databricks machine learning platform that is licensed for commercial use. Based on `pythia-12b`, Dolly is trained on ~15k instruction/response fine tuning records [`databricks-dolly-15k`](https://github.com/databrickslabs/dolly/tree/master/data) generated by Databricks employees in capability domains from the InstructGPT paper, including brainstorming, classification, closed QA, generation, information extraction, open QA and summarization. `dolly-v2-12b` is not a state-of-the-art model, but does exhibit surprisingly high quality instruction following behavior not characteristic of the foundation model on which it is based. Dolly v2 is also available in these smaller models sizes: * [dolly-v2-7b](https://huggingface.co/databricks/dolly-v2-7b), a 6.9 billion parameter based on `pythia-6.9b` * [dolly-v2-3b](https://huggingface.co/databricks/dolly-v2-3b), a 2.8 billion parameter based on `pythia-2.8b` Please refer to the [dolly GitHub repo](https://github.com/databrickslabs/dolly#getting-started-with-response-generation) for tips on running inference for various GPU configurations. **Owner**: Databricks, Inc. ## Model Overview `dolly-v2-12b` is a 12 billion parameter causal language model created by [Databricks](https://databricks.com/) that is derived from [EleutherAI's](https://www.eleuther.ai/) [Pythia-12b](https://huggingface.co/EleutherAI/pythia-12b) and fine-tuned on a [~15K record instruction corpus](https://github.com/databrickslabs/dolly/tree/master/data) generated by Databricks employees and released under a permissive license (CC-BY-SA) ## Usage To use the model with the `transformers` library on a machine with GPUs, first make sure you have the `transformers` and `accelerate` libraries installed. In a Databricks notebook you could run: ```python %pip install "accelerate>=0.16.0,<1" "transformers[torch]>=4.28.1,<5" "torch>=1.13.1,<2" ``` The instruction following pipeline can be loaded using the `pipeline` function as shown below. This loads a custom `InstructionTextGenerationPipeline` found in the model repo [here](https://huggingface.co/databricks/dolly-v2-3b/blob/main/instruct_pipeline.py), which is why `trust_remote_code=True` is required. Including `torch_dtype=torch.bfloat16` is generally recommended if this type is supported in order to reduce memory usage. It does not appear to impact output quality. It is also fine to remove it if there is sufficient memory. ```python import torch from transformers import pipeline generate_text = pipeline(model="databricks/dolly-v2-12b", torch_dtype=torch.bfloat16, trust_remote_code=True, device_map="auto") ``` You can then use the pipeline to answer instructions: ```python res = generate_text("Explain to me the difference between nuclear fission and fusion.") print(res[0]["generated_text"]) ``` Alternatively, if you prefer to not use `trust_remote_code=True` you can download [instruct_pipeline.py](https://huggingface.co/databricks/dolly-v2-3b/blob/main/instruct_pipeline.py), store it alongside your notebook, and construct the pipeline yourself from the loaded model and tokenizer: ```python import torch from instruct_pipeline import InstructionTextGenerationPipeline from transformers import AutoModelForCausalLM, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("databricks/dolly-v2-12b", padding_side="left") model = AutoModelForCausalLM.from_pretrained("databricks/dolly-v2-12b", device_map="auto", torch_dtype=torch.bfloat16) generate_text = InstructionTextGenerationPipeline(model=model, tokenizer=tokenizer) ``` ### LangChain Usage To use the pipeline with LangChain, you must set `return_full_text=True`, as LangChain expects the full text to be returned and the default for the pipeline is to only return the new text. ```python import torch from transformers import pipeline generate_text = pipeline(model="databricks/dolly-v2-12b", torch_dtype=torch.bfloat16, trust_remote_code=True, device_map="auto", return_full_text=True) ``` You can create a prompt that either has only an instruction or has an instruction with context: ```python from langchain import PromptTemplate, LLMChain from langchain.llms import HuggingFacePipeline # template for an instrution with no input prompt = PromptTemplate( input_variables=["instruction"], template="{instruction}") # template for an instruction with input prompt_with_context = PromptTemplate( input_variables=["instruction", "context"], template="{instruction}\n\nInput:\n{context}") hf_pipeline = HuggingFacePipeline(pipeline=generate_text) llm_chain = LLMChain(llm=hf_pipeline, prompt=prompt) llm_context_chain = LLMChain(llm=hf_pipeline, prompt=prompt_with_context) ``` Example predicting using a simple instruction: ```python print(llm_chain.predict(instruction="Explain to me the difference between nuclear fission and fusion.").lstrip()) ``` Example predicting using an instruction with context: ```python context = """George Washington (February 22, 1732[b] - December 14, 1799) was an American military officer, statesman, and Founding Father who served as the first president of the United States from 1789 to 1797.""" print(llm_context_chain.predict(instruction="When was George Washington president?", context=context).lstrip()) ``` ## Known Limitations ### Performance Limitations **`dolly-v2-12b` is not a state-of-the-art generative language model** and, though quantitative benchmarking is ongoing, is not designed to perform competitively with more modern model architectures or models subject to larger pretraining corpuses. The Dolly model family is under active development, and so any list of shortcomings is unlikely to be exhaustive, but we include known limitations and misfires here as a means to document and share our preliminary findings with the community. In particular, `dolly-v2-12b` struggles with: syntactically complex prompts, programming problems, mathematical operations, factual errors, dates and times, open-ended question answering, hallucination, enumerating lists of specific length, stylistic mimicry, having a sense of humor, etc. Moreover, we find that `dolly-v2-12b` does not have some capabilities, such as well-formatted letter writing, present in the original model. ### Dataset Limitations Like all language models, `dolly-v2-12b` reflects the content and limitations of its training corpuses. - **The Pile**: GPT-J's pre-training corpus contains content mostly collected from the public internet, and like most web-scale datasets, it contains content many users would find objectionable. As such, the model is likely to reflect these shortcomings, potentially overtly in the case it is explicitly asked to produce objectionable content, and sometimes subtly, as in the case of biased or harmful implicit associations. - **`databricks-dolly-15k`**: The training data on which `dolly-v2-12b` is instruction tuned represents natural language instructions generated by Databricks employees during a period spanning March and April 2023 and includes passages from Wikipedia as references passages for instruction categories like closed QA and summarization. To our knowledge it does not contain obscenity, intellectual property or personally identifying information about non-public figures, but it may contain typos and factual errors. The dataset may also reflect biases found in Wikipedia. Finally, the dataset likely reflects the interests and semantic choices of Databricks employees, a demographic which is not representative of the global population at large. Databricks is committed to ongoing research and development efforts to develop helpful, honest and harmless AI technologies that maximize the potential of all individuals and organizations. ### Benchmark Metrics Below you'll find various models benchmark performance on the [EleutherAI LLM Evaluation Harness](https://github.com/EleutherAI/lm-evaluation-harness); model results are sorted by geometric mean to produce an intelligible ordering. As outlined above, these results demonstrate that `dolly-v2-12b` is not state of the art, and in fact underperforms `dolly-v1-6b` in some evaluation benchmarks. We believe this owes to the composition and size of the underlying fine tuning datasets, but a robust statement as to the sources of these variations requires further study. | model | openbookqa | arc_easy | winogrande | hellaswag | arc_challenge | piqa | boolq | gmean | | --------------------------------- | ------------ | ---------- | ------------ | ----------- | --------------- | -------- | -------- | ---------| | EleutherAI/pythia-2.8b | 0.348 | 0.585859 | 0.589582 | 0.591217 | 0.323379 | 0.73395 | 0.638226 | 0.523431 | | EleutherAI/pythia-6.9b | 0.368 | 0.604798 | 0.608524 | 0.631548 | 0.343857 | 0.761153 | 0.6263 | 0.543567 | | databricks/dolly-v2-3b | 0.384 | 0.611532 | 0.589582 | 0.650767 | 0.370307 | 0.742655 | 0.575535 | 0.544886 | | EleutherAI/pythia-12b | 0.364 | 0.627104 | 0.636148 | 0.668094 | 0.346416 | 0.760065 | 0.673394 | 0.559676 | | EleutherAI/gpt-j-6B | 0.382 | 0.621633 | 0.651144 | 0.662617 | 0.363481 | 0.761153 | 0.655963 | 0.565936 | | databricks/dolly-v2-12b | 0.408 | 0.63931 | 0.616417 | 0.707927 | 0.388225 | 0.757889 | 0.568196 | 0.56781 | | databricks/dolly-v2-7b | 0.392 | 0.633838 | 0.607735 | 0.686517 | 0.406997 | 0.750816 | 0.644037 | 0.573487 | | databricks/dolly-v1-6b | 0.41 | 0.62963 | 0.643252 | 0.676758 | 0.384812 | 0.773667 | 0.687768 | 0.583431 | | EleutherAI/gpt-neox-20b | 0.402 | 0.683923 | 0.656669 | 0.7142 | 0.408703 | 0.784004 | 0.695413 | 0.602236 | # Citation ``` @online{DatabricksBlog2023DollyV2, author = {Mike Conover and Matt Hayes and Ankit Mathur and Jianwei Xie and Jun Wan and Sam Shah and Ali Ghodsi and Patrick Wendell and Matei Zaharia and Reynold Xin}, title = {Free Dolly: Introducing the World's First Truly Open Instruction-Tuned LLM}, year = {2023}, url = {https://www.databricks.com/blog/2023/04/12/dolly-first-open-commercially-viable-instruction-tuned-llm}, urldate = {2023-06-30} } ``` # Happy Hacking!

K00B404/Merged_Beowolx-CodePro_Medusa2-7B-Mistral-I-v0-2

K00B404

transformers

text-generation

--- base_model: - beowolx/MistralHermes-CodePro-7B-v1 - EmbeddedLLM/Medusa2-Mistral-7B-Instruct-v0.2 library_name: transformers tags: - mergekit - merge --- # merge This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). ## Merge Details ### Merge Method This model was merged using the SLERP merge method. ### Models Merged The following models were included in the merge: * [beowolx/MistralHermes-CodePro-7B-v1](https://huggingface.co/beowolx/MistralHermes-CodePro-7B-v1) * [EmbeddedLLM/Medusa2-Mistral-7B-Instruct-v0.2](https://huggingface.co/EmbeddedLLM/Medusa2-Mistral-7B-Instruct-v0.2) ### Configuration The following YAML configuration was used to produce this model: ```yaml slices: - sources: - model: beowolx/MistralHermes-CodePro-7B-v1 layer_range: [0, 32] - model: EmbeddedLLM/Medusa2-Mistral-7B-Instruct-v0.2 layer_range: [0, 32] merge_method: slerp base_model: beowolx/MistralHermes-CodePro-7B-v1 parameters: t: - filter: self_attn value: [0, 0.5, 0.3, 0.7, 1] - filter: mlp value: [1, 0.5, 0.7, 0.3, 0] - value: 0.5 dtype: bfloat16 ```

alexseen/Fashion

alexseen

--- license: openrail ---

raejeong/vsft-llava-1.5-7b-hf

raejeong

Entry not found

Mou11209203/distilbert-base-multilingual-cased_regression_finetuned_news_all

Mou11209203

transformers

text-classification

--- license: apache-2.0 base_model: distilbert/distilbert-base-multilingual-cased tags: - generated_from_trainer model-index: - name: distilbert-base-multilingual-cased_regression_finetuned_news_all results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-multilingual-cased_regression_finetuned_news_all This model is a fine-tuned version of [distilbert/distilbert-base-multilingual-cased](https://huggingface.co/distilbert/distilbert-base-multilingual-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.7173 - Mse: 1.7173 - Mae: 0.9613 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 500 - num_epochs: 10 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Mse | Mae | |:-------------:|:-----:|:------:|:---------------:|:------:|:------:| | 1.815 | 1.0 | 21755 | 1.7168 | 1.7168 | 0.9622 | | 1.6414 | 2.0 | 43510 | 1.7167 | 1.7167 | 0.9620 | | 1.6485 | 3.0 | 65265 | 1.7171 | 1.7171 | 0.9614 | | 1.7803 | 4.0 | 87020 | 1.7168 | 1.7168 | 0.9619 | | 1.6427 | 5.0 | 108775 | 1.7173 | 1.7173 | 0.9613 | | 1.6436 | 6.0 | 130530 | 1.7170 | 1.7170 | 0.9615 | | 1.7037 | 7.0 | 152285 | 1.7168 | 1.7168 | 0.9618 | ### Framework versions - Transformers 4.39.3 - Pytorch 2.2.1 - Datasets 2.18.0 - Tokenizers 0.15.2

ChaAn/Llama-2-7b-hf-GGUF

ChaAn

Entry not found

ehristoforu/MinMax-1b-v0.1

ehristoforu

Entry not found

tmester/llama-2-7b-stocknet-fine-tune-3.0

tmester

transformers

text-generation

Entry not found

Virus-Proton/mistral_finetuned

Virus-Proton

peft

--- license: apache-2.0 library_name: peft tags: - generated_from_trainer base_model: hishab/titulm-1b-enbn-v1 model-index: - name: mistral_finetuned results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # mistral_finetuned This model is a fine-tuned version of [hishab/titulm-1b-enbn-v1](https://huggingface.co/hishab/titulm-1b-enbn-v1) on an unknown dataset. It achieves the following results on the evaluation set: - eval_loss: 2.1066 - eval_runtime: 18.7031 - eval_samples_per_second: 5.347 - eval_steps_per_second: 2.673 - epoch: 0.0 - step: 100 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 0.03 - training_steps: 10000 ### Framework versions - PEFT 0.10.0 - Transformers 4.38.2 - Pytorch 2.2.1+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2

RichardErkhov/databricks_-_dolly-v1-6b-4bits

RichardErkhov

transformers

text-generation

Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) dolly-v1-6b - bnb 4bits - Model creator: https://huggingface.co/databricks/ - Original model: https://huggingface.co/databricks/dolly-v1-6b/ Original model description: --- license: cc-by-nc-4.0 datasets: - tatsu-lab/alpaca language: - en library_name: transformers inference: false --- # dolly-v1-6b Model Card ## Dolly v2 Is Out! Please try Dolly v2 instead: - https://huggingface.co/databricks/dolly-v2-12b - https://huggingface.co/databricks/dolly-v2-7b - https://huggingface.co/databricks/dolly-v2-3b ## Summary Databricks’ `dolly-v1-6b`, a large language model ([blog post](https://www.databricks.com/blog/2023/03/24/hello-dolly-democratizing-magic-chatgpt-open-models.html)) trained on the Databricks machine learning platform, demonstrates that a two-years-old [open source model](https://huggingface.co/EleutherAI/gpt-j-6B) can, when subjected to just 30 minutes of fine tuning on a focused corpus of 50k records ([Stanford Alpaca](https://crfm.stanford.edu/2023/03/13/alpaca.html)), exhibit surprisingly high quality instruction following behavior not characteristic of the foundation model on which it is based. We believe this finding is important because it demonstrates that the ability to create powerful artificial intelligence technologies is vastly more accessible than previously realized. Databricks is committed to ensuring that every organization and individual benefits from the transformative power of artificial intelligence. The Dolly model family represents our first steps along this journey, and we’re excited to share this technology with the world. **Owner**: Databricks, Inc. ## Model Overview `dolly-v1-6b` is a 6 billion parameter causal language model created by [Databricks](https://databricks.com/) that is derived from [EleutherAI’s](https://www.eleuther.ai/) [GPT-J](https://huggingface.co/EleutherAI/gpt-j-6B) (released June 2021) and fine-tuned on a ~52K record instruction corpus ([Stanford Alpaca](https://crfm.stanford.edu/2023/03/13/alpaca.html)) (CC-NC-BY-4.0) consisting of question/answer pairs generated using the techniques outlined in the [Self-Instruct](https://arxiv.org/abs/2212.10560) paper. The [original version](https://www.databricks.com/blog/2023/03/24/hello-dolly-democratizing-magic-chatgpt-open-models.html) of was Dolly was trained using [deepspeed](https://github.com/microsoft/DeepSpeed) [ZeRO 3](https://github.com/microsoft/DeepSpeed/blob/master/docs/code-docs/source/zero3.rst) on the [Databricks Machine Learning Platform](https://www.databricks.com/product/machine-learning) in just 30 minutes (1 epoch) using a single [NDasrA100_v4](https://learn.microsoft.com/en-us/azure/virtual-machines/nda100-v4-series) machine with 8x A100 40GB GPUs. The most recent `dolly-v1-6b` checkpoint was trained for 10 epochs on the same hardware. Like its base model, `dolly-v1-6b` has six billion parameters consisting of 28 transformer layers with 16 attention heads each. It employs [Rotary Position Embedding](https://arxiv.org/abs/2104.09864) (RoPE) and shares the same tokenizer as GPT-3. GPT-J was trained on [The Pile](https://huggingface.co/datasets/the_pile), a 400B token dataset of diverse documents designed primarily for text generation tasks. ## Known Limitations **`dolly-v1-6b` is not a state-of-the-art generative language model** and, though quantitative benchmarking is ongoing, is not designed to perform competitively with more modern model architectures or models subject to larger pretraining corpuses. **It is designed for academic or research purposes, and to encourage model and engineering experimentation.** The Dolly model family is under active development, and so any list of shortcomings is unlikely to be exhaustive, but we include known limitations and misfires here as a means to document and share our preliminary findings with the community. In particular, `dolly-v1-6b` struggles with: syntactically complex prompts, programming problems, mathematical operations, factual errors, dates and times, open-ended question answering, hallucination, enumerating lists of specific length, stylistic mimicry, having a sense of humor, etc. ## Training Data, Bias & Objectionable Content Like all language models, `dolly-v1-6b` reflects the content and limitations of its training corpuses. - **The Pile**: GPT-J’s pre-training corpus contains content mostly collected from the public internet, and like most web-scale datasets, it contains content many users would find objectionable. As such, the model is likely to reflect these shortcomings, potentially overtly in the case it is explicitly asked to produce objectionable content, and sometimes subtly, as in the case of biased or harmful implicit associations. - **Stanford Alpaca**: The instruction tuning corpus for dolly-6b can be assumed to share many of the limitations. In addition, it is known to contain factual inaccuracies, semantic and syntactic irregularities, nonsensical responses, and incorrect mathematical calculations, among other data shortcomings. The model outputs will reflect these limitations. Databricks is committed to ongoing research and development efforts to develop helpful, honest and harmless AI technologies that maximize the potential of all individuals and organizations. ## Intended Uses **`dolly-v1-6b` is intended exclusively for research purposes. We do not recommend using `dolly-v1-6b` in high-risk applications (e.g., educational or vocational training, product safety components, or other uses that may impact the well-being of individuals.)** `dolly-v1-6b` is differentiated from its predecessor, GPT-J, in the extent to which it exhibits coherent responses to natural language questions and instruction prompts. `dolly-v1-6b` appears to exhibit reasonable (but categorically not state-of-the-art) behavior across a range of capabilities, including many of those outlined in the [InstructGPT](https://arxiv.org/abs/2203.02155) paper. We include evocative examples below, but invite the reader to observe that they contain factual inaccuracies or semantic misfires that illustrate some of the model’s shortcomings. ### Brainstorming **What are considerations I should keep in mind when planning a backcountry backpacking trip?** When planning a backcountry backpacking trip, there are several considerations to keep in mind. First, make sure you are prepared for any potential weather conditions, including extreme temperatures and lightning storms. Second, consider the route you will take and the terrain that you will be traversing. Third, make sure that you have enough food, water and gear to stay safe and comfortable during your backcountry journey. Fourth, plan for potential wildlife encounters and make sure you have the appropriate safety measures in place. Finally, have an emergency plan in place in case of a medical emergency. ### Classification **Label each of the following as either a scientific concept or a product: Nikon D750, quantum entanglement, CRISPR, and a Macbook Pro.** Nikon D750: Product Quantum entanglement: Scientific Concept CRISPR: Product Macbook Pro: Product ### Extraction **Extract all the people and places from the following passage: Basquiat was born on December 22, 1960, in Park Slope, Brooklyn, New York City, the second of four children to Matilde Basquiat (née Andrades, 1934–2008) and Gérard Basquiat (1930–2013). He had an older brother, Max, who died shortly before his birth, and two younger sisters, Lisane (b. 1964) and Jeanine (b. 1967). His father was born in Port-au-Prince, Haiti and his mother was born in Brooklyn to Puerto Rican parents. He was raised Catholic.** People: Basquiat, Matilde Basquiat, Gérard Basquiat, Max Basquiat, Lisane Basquiat, Jeanine Basquiat. Place: Park Slope, Brooklyn, New York City. ### Generation **Write a press release declaring the completion of Atlantis II, a facility designed for long-term human habitation at the bottom of the ocean. Be sure to mention some of its advanced technological features.** Atlantis II, a new facility designed for long-term human habitation at the bottom of the ocean, is now complete! With advanced technological features, it will be a safe and comfortable living environment for generations to come. From self-sustaining ecosystems to advanced robotics, the facility offers a range of opportunities for exploration, discovery and exploration. Atlantis II will be a home for a wide range of species and will serve as a hub for ocean research, exploration and exploration, bringing together a diverse team of scientists, engineers, biologists, and divers to uncover the mysteries of the deep. ### Summarization **Give me a one line summary of this: Coffee is one of the most widely consumed beverages in the world. It has primarily consumed due to its stimulant effect and unique taste since the ancient times. Afterwards, its consumption has been historically associated with a lower risk of some diseases such as type 2 diabetes mellitus, obesity, cardiovascular disease and some type of cancer and thus it has also consumed due to health benefits. It contains many bioactive compounds such as caffeine, chlorogenic acids and diterpenoid alcohols which have so far been associated with many potential health benefits. For example, caffeine reduces risk of developing neurodegenerative disease and chlorogenic acids (CGA) and diterpene alcohols have many health benefits such as antioxidant and chemo-preventive. Coffee also have harmful effects. For example, diterpenoid alcohols increases serum homocysteine and cholesterol levels and thus it has adverse effects on cardiovascular system. Overall, the study that supports the health benefits of coffee is increasing. But, it is thought-provoking that the association with health benefits of coffee consumption and frequency at different levels in each study. For this reason, we aimed to examine the health effect of the coffee and how much consumption is to investigate whether it meets the claimed health benefits.** Coffee is one of the most widely consumed beverages in the world and has historically been associated with a lower risk of some diseases such as type 2 diabetes mellitus, obesity, cardiovascular disease and some type of cancer. However, there are mixed results in the literature and further research is required to determine the health benefits of coffee consumption. ### Rephrasing **Give me a different way to say the following to a 4 year old: "Son, this is the last time I'm going to tell you. Go to bed!"** "Goodnight, Son. Sleep tight and I'll see you in the morning!" ### Closed QA **I'm going to give you a passage from the book Neuromancer and I'd like you to answer the following question: What is the tool that allows Case to access the matrix? Case was twenty-four. At twenty-two, he'd been a cowboy, a rustler, one of the best in the Sprawl. He'd been trained by the best, by McCoy Pauley and Bobby Quine, legends in the biz. He'd operated on an almost permanent adrenaline high, a byproduct of youth and proficiency, jacked into a custom cyberspace deck that projected his disembodied consciousness into the consensual hallucination that was the matrix.** The tool that allows Case to access the matrix is his cyberspace deck, which allows him to manipulate and interact with the virtual world around him. It enables him to access information from various sources and interact with the people in the matrix, and it is one of the main weapons of Case's cyberworld. ### Open QA **What is the literary style of Jorge Luis Borges?** Jorge Luis Borges's literary style is often described as magical realism. He often creates stories that combine fictional characters with real life elements and use the juxtaposition of these elements to create a sense of surrealism. He often uses allegory and symbolism to explore complex philosophical ideas and explore questions of human perception and understanding. His writing is characterized by a playful and inventive use of language, with characters often being metaphors or symbols for larger, more abstract concepts. ## Usage ### Load Model & Tokenizer ```python import numpy as np from transformers import ( AutoModelForCausalLM, AutoTokenizer, PreTrainedModel, PreTrainedTokenizer ) tokenizer = AutoTokenizer.from_pretrained("databricks/dolly-v1-6b", padding_side="left") model = AutoModelForCausalLM.from_pretrained("databricks/dolly-v1-6b", device_map="auto", trust_remote_code=True) ``` ### Generate Text ```python PROMPT_FORMAT = """Below is an instruction that describes a task. Write a response that appropriately completes the request. ### Instruction: {instruction} ### Response: """ def generate_response(instruction: str, *, model: PreTrainedModel, tokenizer: PreTrainedTokenizer, do_sample: bool = True, max_new_tokens: int = 256, top_p: float = 0.92, top_k: int = 0, **kwargs) -> str: input_ids = tokenizer(PROMPT_FORMAT.format(instruction=instruction), return_tensors="pt").input_ids.to("cuda") # each of these is encoded to a single token response_key_token_id = tokenizer.encode("### Response:")[0] end_key_token_id = tokenizer.encode("### End")[0] gen_tokens = model.generate(input_ids, pad_token_id=tokenizer.pad_token_id, eos_token_id=end_key_token_id, do_sample=do_sample, max_new_tokens=max_new_tokens, top_p=top_p, top_k=top_k, **kwargs)[0].cpu() # find where the response begins response_positions = np.where(gen_tokens == response_key_token_id)[0] if len(response_positions) >= 0: response_pos = response_positions[0] # find where the response ends end_pos = None end_positions = np.where(gen_tokens == end_key_token_id)[0] if len(end_positions) > 0: end_pos = end_positions[0] return tokenizer.decode(gen_tokens[response_pos + 1 : end_pos]).strip() return None # Sample similar to: "Excited to announce the release of Dolly, a powerful new language model from Databricks! #AI #Databricks" generate_response("Write a tweet announcing Dolly, a large language model from Databricks.", model=model, tokenizer=tokenizer) ``` ### Benchmark Metrics Below you'll find various models benchmark performance on the [EleutherAI LLM Evaluation Harness](https://github.com/EleutherAI/lm-evaluation-harness) model results are sorted by geometric mean to produce an intelligible ordering. These results demonstrate that Dolly is not state of the art, as we describe above, but also point to an interesting observation. Namely, Dolly is only marginally better (and in the case of Winogrande worse) and its basemodel GPT-J-6B. Despite this fact, the qualitative behavior of Dolly is materially different from the underlying model ([try it yourself](https://huggingface.co/EleutherAI/gpt-j-6B) on Hugging Face!), which points to meaningful limitations of the existing evaluation benchmarks for measuring the quality of generative models. | model | openbookqa | arc_easy | winogrande | hellaswag | arc_challenge | piqa | boolq | | --------------------------- | ------------ | ---------- | ------------ | ----------- | --------------- | -------- | ---------| | cerebras/Cerebras-GPT-13B | 0.36 | 0.598906 | 0.607735 | 0.593109 | 0.325939 | 0.749728 | 0.611621 | | EleutherAI/gpt-j-6B | 0.382 | 0.621633 | 0.651144 | 0.662617 | 0.363481 | 0.761153 | 0.655963 | | dolly-v1-6b (1 epoch) | 0.428 | 0.608586 | 0.633781 | 0.650568 | 0.377133 | 0.761697 | 0.69633 | | dolly-v1-6b (10 epochs) | 0.41 | 0.62963 | 0.643252 | 0.676758 | 0.384812 | 0.773667 | 0.687768 | | EleutherAI/gpt-neox-20b | 0.402 | 0.683923 | 0.656669 | 0.7142 | 0.408703 | 0.784004 | 0.695413 | # Citation ``` @online{DatabricksBlog2023DollyV1, author = {Mike Conover and Matt Hayes and Ankit Mathur and Xiangrui Meng and Jianwei Xie and Jun Wan and Ali Ghodsi and Patrick Wendell and Matei Zaharia}, title = {Hello Dolly: Democratizing the magic of ChatGPT with open models}, year = {2023}, url = {https://www.databricks.com/blog/2023/03/24/hello-dolly-democratizing-magic-chatgpt-open-models.html}, urldate = {2023-06-30} } ``` # Happy Hacking!

Grayx/sad_pepe_9

Grayx

transformers

text-generation

--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

Imadken/Mistral-7B-v0.1-platypus

Imadken

transformers

text-generation

--- license: apache-2.0 ---

RichardErkhov/databricks_-_dolly-v2-12b-8bits

RichardErkhov

transformers

text-generation

Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) dolly-v2-12b - bnb 8bits - Model creator: https://huggingface.co/databricks/ - Original model: https://huggingface.co/databricks/dolly-v2-12b/ Original model description: --- license: mit language: - en library_name: transformers inference: false datasets: - databricks/databricks-dolly-15k --- # dolly-v2-12b Model Card ## Summary Databricks' `dolly-v2-12b`, an instruction-following large language model trained on the Databricks machine learning platform that is licensed for commercial use. Based on `pythia-12b`, Dolly is trained on ~15k instruction/response fine tuning records [`databricks-dolly-15k`](https://github.com/databrickslabs/dolly/tree/master/data) generated by Databricks employees in capability domains from the InstructGPT paper, including brainstorming, classification, closed QA, generation, information extraction, open QA and summarization. `dolly-v2-12b` is not a state-of-the-art model, but does exhibit surprisingly high quality instruction following behavior not characteristic of the foundation model on which it is based. Dolly v2 is also available in these smaller models sizes: * [dolly-v2-7b](https://huggingface.co/databricks/dolly-v2-7b), a 6.9 billion parameter based on `pythia-6.9b` * [dolly-v2-3b](https://huggingface.co/databricks/dolly-v2-3b), a 2.8 billion parameter based on `pythia-2.8b` Please refer to the [dolly GitHub repo](https://github.com/databrickslabs/dolly#getting-started-with-response-generation) for tips on running inference for various GPU configurations. **Owner**: Databricks, Inc. ## Model Overview `dolly-v2-12b` is a 12 billion parameter causal language model created by [Databricks](https://databricks.com/) that is derived from [EleutherAI's](https://www.eleuther.ai/) [Pythia-12b](https://huggingface.co/EleutherAI/pythia-12b) and fine-tuned on a [~15K record instruction corpus](https://github.com/databrickslabs/dolly/tree/master/data) generated by Databricks employees and released under a permissive license (CC-BY-SA) ## Usage To use the model with the `transformers` library on a machine with GPUs, first make sure you have the `transformers` and `accelerate` libraries installed. In a Databricks notebook you could run: ```python %pip install "accelerate>=0.16.0,<1" "transformers[torch]>=4.28.1,<5" "torch>=1.13.1,<2" ``` The instruction following pipeline can be loaded using the `pipeline` function as shown below. This loads a custom `InstructionTextGenerationPipeline` found in the model repo [here](https://huggingface.co/databricks/dolly-v2-3b/blob/main/instruct_pipeline.py), which is why `trust_remote_code=True` is required. Including `torch_dtype=torch.bfloat16` is generally recommended if this type is supported in order to reduce memory usage. It does not appear to impact output quality. It is also fine to remove it if there is sufficient memory. ```python import torch from transformers import pipeline generate_text = pipeline(model="databricks/dolly-v2-12b", torch_dtype=torch.bfloat16, trust_remote_code=True, device_map="auto") ``` You can then use the pipeline to answer instructions: ```python res = generate_text("Explain to me the difference between nuclear fission and fusion.") print(res[0]["generated_text"]) ``` Alternatively, if you prefer to not use `trust_remote_code=True` you can download [instruct_pipeline.py](https://huggingface.co/databricks/dolly-v2-3b/blob/main/instruct_pipeline.py), store it alongside your notebook, and construct the pipeline yourself from the loaded model and tokenizer: ```python import torch from instruct_pipeline import InstructionTextGenerationPipeline from transformers import AutoModelForCausalLM, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("databricks/dolly-v2-12b", padding_side="left") model = AutoModelForCausalLM.from_pretrained("databricks/dolly-v2-12b", device_map="auto", torch_dtype=torch.bfloat16) generate_text = InstructionTextGenerationPipeline(model=model, tokenizer=tokenizer) ``` ### LangChain Usage To use the pipeline with LangChain, you must set `return_full_text=True`, as LangChain expects the full text to be returned and the default for the pipeline is to only return the new text. ```python import torch from transformers import pipeline generate_text = pipeline(model="databricks/dolly-v2-12b", torch_dtype=torch.bfloat16, trust_remote_code=True, device_map="auto", return_full_text=True) ``` You can create a prompt that either has only an instruction or has an instruction with context: ```python from langchain import PromptTemplate, LLMChain from langchain.llms import HuggingFacePipeline # template for an instrution with no input prompt = PromptTemplate( input_variables=["instruction"], template="{instruction}") # template for an instruction with input prompt_with_context = PromptTemplate( input_variables=["instruction", "context"], template="{instruction}\n\nInput:\n{context}") hf_pipeline = HuggingFacePipeline(pipeline=generate_text) llm_chain = LLMChain(llm=hf_pipeline, prompt=prompt) llm_context_chain = LLMChain(llm=hf_pipeline, prompt=prompt_with_context) ``` Example predicting using a simple instruction: ```python print(llm_chain.predict(instruction="Explain to me the difference between nuclear fission and fusion.").lstrip()) ``` Example predicting using an instruction with context: ```python context = """George Washington (February 22, 1732[b] - December 14, 1799) was an American military officer, statesman, and Founding Father who served as the first president of the United States from 1789 to 1797.""" print(llm_context_chain.predict(instruction="When was George Washington president?", context=context).lstrip()) ``` ## Known Limitations ### Performance Limitations **`dolly-v2-12b` is not a state-of-the-art generative language model** and, though quantitative benchmarking is ongoing, is not designed to perform competitively with more modern model architectures or models subject to larger pretraining corpuses. The Dolly model family is under active development, and so any list of shortcomings is unlikely to be exhaustive, but we include known limitations and misfires here as a means to document and share our preliminary findings with the community. In particular, `dolly-v2-12b` struggles with: syntactically complex prompts, programming problems, mathematical operations, factual errors, dates and times, open-ended question answering, hallucination, enumerating lists of specific length, stylistic mimicry, having a sense of humor, etc. Moreover, we find that `dolly-v2-12b` does not have some capabilities, such as well-formatted letter writing, present in the original model. ### Dataset Limitations Like all language models, `dolly-v2-12b` reflects the content and limitations of its training corpuses. - **The Pile**: GPT-J's pre-training corpus contains content mostly collected from the public internet, and like most web-scale datasets, it contains content many users would find objectionable. As such, the model is likely to reflect these shortcomings, potentially overtly in the case it is explicitly asked to produce objectionable content, and sometimes subtly, as in the case of biased or harmful implicit associations. - **`databricks-dolly-15k`**: The training data on which `dolly-v2-12b` is instruction tuned represents natural language instructions generated by Databricks employees during a period spanning March and April 2023 and includes passages from Wikipedia as references passages for instruction categories like closed QA and summarization. To our knowledge it does not contain obscenity, intellectual property or personally identifying information about non-public figures, but it may contain typos and factual errors. The dataset may also reflect biases found in Wikipedia. Finally, the dataset likely reflects the interests and semantic choices of Databricks employees, a demographic which is not representative of the global population at large. Databricks is committed to ongoing research and development efforts to develop helpful, honest and harmless AI technologies that maximize the potential of all individuals and organizations. ### Benchmark Metrics Below you'll find various models benchmark performance on the [EleutherAI LLM Evaluation Harness](https://github.com/EleutherAI/lm-evaluation-harness); model results are sorted by geometric mean to produce an intelligible ordering. As outlined above, these results demonstrate that `dolly-v2-12b` is not state of the art, and in fact underperforms `dolly-v1-6b` in some evaluation benchmarks. We believe this owes to the composition and size of the underlying fine tuning datasets, but a robust statement as to the sources of these variations requires further study. | model | openbookqa | arc_easy | winogrande | hellaswag | arc_challenge | piqa | boolq | gmean | | --------------------------------- | ------------ | ---------- | ------------ | ----------- | --------------- | -------- | -------- | ---------| | EleutherAI/pythia-2.8b | 0.348 | 0.585859 | 0.589582 | 0.591217 | 0.323379 | 0.73395 | 0.638226 | 0.523431 | | EleutherAI/pythia-6.9b | 0.368 | 0.604798 | 0.608524 | 0.631548 | 0.343857 | 0.761153 | 0.6263 | 0.543567 | | databricks/dolly-v2-3b | 0.384 | 0.611532 | 0.589582 | 0.650767 | 0.370307 | 0.742655 | 0.575535 | 0.544886 | | EleutherAI/pythia-12b | 0.364 | 0.627104 | 0.636148 | 0.668094 | 0.346416 | 0.760065 | 0.673394 | 0.559676 | | EleutherAI/gpt-j-6B | 0.382 | 0.621633 | 0.651144 | 0.662617 | 0.363481 | 0.761153 | 0.655963 | 0.565936 | | databricks/dolly-v2-12b | 0.408 | 0.63931 | 0.616417 | 0.707927 | 0.388225 | 0.757889 | 0.568196 | 0.56781 | | databricks/dolly-v2-7b | 0.392 | 0.633838 | 0.607735 | 0.686517 | 0.406997 | 0.750816 | 0.644037 | 0.573487 | | databricks/dolly-v1-6b | 0.41 | 0.62963 | 0.643252 | 0.676758 | 0.384812 | 0.773667 | 0.687768 | 0.583431 | | EleutherAI/gpt-neox-20b | 0.402 | 0.683923 | 0.656669 | 0.7142 | 0.408703 | 0.784004 | 0.695413 | 0.602236 | # Citation ``` @online{DatabricksBlog2023DollyV2, author = {Mike Conover and Matt Hayes and Ankit Mathur and Jianwei Xie and Jun Wan and Sam Shah and Ali Ghodsi and Patrick Wendell and Matei Zaharia and Reynold Xin}, title = {Free Dolly: Introducing the World's First Truly Open Instruction-Tuned LLM}, year = {2023}, url = {https://www.databricks.com/blog/2023/04/12/dolly-first-open-commercially-viable-instruction-tuned-llm}, urldate = {2023-06-30} } ``` # Happy Hacking!

mradermacher/ORCA_LLaMA_70B_QLoRA-GGUF

mradermacher

transformers

--- exported_from: fangloveskari/ORCA_LLaMA_70B_QLoRA language: - en library_name: transformers license: llama2 quantized_by: mradermacher --- ## About <!-- ### quantize_version: 1 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: --> <!-- ### vocab_type: --> static quants of https://huggingface.co/fangloveskari/ORCA_LLaMA_70B_QLoRA <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) | Link | Type | Size/GB | Notes | |:-----|:-----|--------:|:------| | [GGUF](https://huggingface.co/mradermacher/ORCA_LLaMA_70B_QLoRA-GGUF/resolve/main/ORCA_LLaMA_70B_QLoRA.Q2_K.gguf) | Q2_K | 25.6 | | | [GGUF](https://huggingface.co/mradermacher/ORCA_LLaMA_70B_QLoRA-GGUF/resolve/main/ORCA_LLaMA_70B_QLoRA.IQ3_S.gguf) | IQ3_S | 30.0 | beats Q3_K* | | [GGUF](https://huggingface.co/mradermacher/ORCA_LLaMA_70B_QLoRA-GGUF/resolve/main/ORCA_LLaMA_70B_QLoRA.Q3_K_S.gguf) | Q3_K_S | 30.0 | | | [GGUF](https://huggingface.co/mradermacher/ORCA_LLaMA_70B_QLoRA-GGUF/resolve/main/ORCA_LLaMA_70B_QLoRA.IQ3_M.gguf) | IQ3_M | 31.0 | | | [GGUF](https://huggingface.co/mradermacher/ORCA_LLaMA_70B_QLoRA-GGUF/resolve/main/ORCA_LLaMA_70B_QLoRA.Q3_K_M.gguf) | Q3_K_M | 33.4 | lower quality | | [GGUF](https://huggingface.co/mradermacher/ORCA_LLaMA_70B_QLoRA-GGUF/resolve/main/ORCA_LLaMA_70B_QLoRA.Q3_K_L.gguf) | Q3_K_L | 36.2 | | | [GGUF](https://huggingface.co/mradermacher/ORCA_LLaMA_70B_QLoRA-GGUF/resolve/main/ORCA_LLaMA_70B_QLoRA.Q4_K_S.gguf) | Q4_K_S | 39.3 | fast, recommended | | [PART 1](https://huggingface.co/mradermacher/ORCA_LLaMA_70B_QLoRA-GGUF/resolve/main/ORCA_LLaMA_70B_QLoRA.Q6_K.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/ORCA_LLaMA_70B_QLoRA-GGUF/resolve/main/ORCA_LLaMA_70B_QLoRA.Q6_K.gguf.part2of2) | Q6_K | 56.7 | very good quality | | [PART 1](https://huggingface.co/mradermacher/ORCA_LLaMA_70B_QLoRA-GGUF/resolve/main/ORCA_LLaMA_70B_QLoRA.Q8_0.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/ORCA_LLaMA_70B_QLoRA-GGUF/resolve/main/ORCA_LLaMA_70B_QLoRA.Q8_0.gguf.part2of2) | Q8_0 | 73.4 | fast, best quality | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better):  And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->

K00B404/Merged_Beowolx-CodePro_Medusa2-14X-7B-Mistral-I-v0-2

K00B404

transformers

text-generation

--- base_model: - K00B404/Merged_Beowolx-CodePro_Medusa2-7B-Mistral-I-v0-2 - EmbeddedLLM/Mistral-7B-Merge-14-v0.1 library_name: transformers tags: - mergekit - merge --- # merge This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). ## Merge Details ### Merge Method This model was merged using the SLERP merge method. ### Models Merged The following models were included in the merge: * [K00B404/Merged_Beowolx-CodePro_Medusa2-7B-Mistral-I-v0-2](https://huggingface.co/K00B404/Merged_Beowolx-CodePro_Medusa2-7B-Mistral-I-v0-2) * [EmbeddedLLM/Mistral-7B-Merge-14-v0.1](https://huggingface.co/EmbeddedLLM/Mistral-7B-Merge-14-v0.1) ### Configuration The following YAML configuration was used to produce this model: ```yaml slices: - sources: - model: EmbeddedLLM/Mistral-7B-Merge-14-v0.1 layer_range: [0, 32] - model: K00B404/Merged_Beowolx-CodePro_Medusa2-7B-Mistral-I-v0-2 layer_range: [0, 32] merge_method: slerp base_model: EmbeddedLLM/Mistral-7B-Merge-14-v0.1 parameters: t: - filter: self_attn value: [0, 0.5, 0.3, 0.7, 1] - filter: mlp value: [1, 0.5, 0.7, 0.3, 0] - value: 0.5 dtype: bfloat16 ```

SpideyDLK/wav2vec2-large-xls-r-300m-sinhala-original-split-part3

SpideyDLK

transformers

automatic-speech-recognition

--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

RichardErkhov/databricks_-_dolly-v1-6b-8bits

RichardErkhov

transformers

text-generation

Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) dolly-v1-6b - bnb 8bits - Model creator: https://huggingface.co/databricks/ - Original model: https://huggingface.co/databricks/dolly-v1-6b/ Original model description: --- license: cc-by-nc-4.0 datasets: - tatsu-lab/alpaca language: - en library_name: transformers inference: false --- # dolly-v1-6b Model Card ## Dolly v2 Is Out! Please try Dolly v2 instead: - https://huggingface.co/databricks/dolly-v2-12b - https://huggingface.co/databricks/dolly-v2-7b - https://huggingface.co/databricks/dolly-v2-3b ## Summary Databricks’ `dolly-v1-6b`, a large language model ([blog post](https://www.databricks.com/blog/2023/03/24/hello-dolly-democratizing-magic-chatgpt-open-models.html)) trained on the Databricks machine learning platform, demonstrates that a two-years-old [open source model](https://huggingface.co/EleutherAI/gpt-j-6B) can, when subjected to just 30 minutes of fine tuning on a focused corpus of 50k records ([Stanford Alpaca](https://crfm.stanford.edu/2023/03/13/alpaca.html)), exhibit surprisingly high quality instruction following behavior not characteristic of the foundation model on which it is based. We believe this finding is important because it demonstrates that the ability to create powerful artificial intelligence technologies is vastly more accessible than previously realized. Databricks is committed to ensuring that every organization and individual benefits from the transformative power of artificial intelligence. The Dolly model family represents our first steps along this journey, and we’re excited to share this technology with the world. **Owner**: Databricks, Inc. ## Model Overview `dolly-v1-6b` is a 6 billion parameter causal language model created by [Databricks](https://databricks.com/) that is derived from [EleutherAI’s](https://www.eleuther.ai/) [GPT-J](https://huggingface.co/EleutherAI/gpt-j-6B) (released June 2021) and fine-tuned on a ~52K record instruction corpus ([Stanford Alpaca](https://crfm.stanford.edu/2023/03/13/alpaca.html)) (CC-NC-BY-4.0) consisting of question/answer pairs generated using the techniques outlined in the [Self-Instruct](https://arxiv.org/abs/2212.10560) paper. The [original version](https://www.databricks.com/blog/2023/03/24/hello-dolly-democratizing-magic-chatgpt-open-models.html) of was Dolly was trained using [deepspeed](https://github.com/microsoft/DeepSpeed) [ZeRO 3](https://github.com/microsoft/DeepSpeed/blob/master/docs/code-docs/source/zero3.rst) on the [Databricks Machine Learning Platform](https://www.databricks.com/product/machine-learning) in just 30 minutes (1 epoch) using a single [NDasrA100_v4](https://learn.microsoft.com/en-us/azure/virtual-machines/nda100-v4-series) machine with 8x A100 40GB GPUs. The most recent `dolly-v1-6b` checkpoint was trained for 10 epochs on the same hardware. Like its base model, `dolly-v1-6b` has six billion parameters consisting of 28 transformer layers with 16 attention heads each. It employs [Rotary Position Embedding](https://arxiv.org/abs/2104.09864) (RoPE) and shares the same tokenizer as GPT-3. GPT-J was trained on [The Pile](https://huggingface.co/datasets/the_pile), a 400B token dataset of diverse documents designed primarily for text generation tasks. ## Known Limitations **`dolly-v1-6b` is not a state-of-the-art generative language model** and, though quantitative benchmarking is ongoing, is not designed to perform competitively with more modern model architectures or models subject to larger pretraining corpuses. **It is designed for academic or research purposes, and to encourage model and engineering experimentation.** The Dolly model family is under active development, and so any list of shortcomings is unlikely to be exhaustive, but we include known limitations and misfires here as a means to document and share our preliminary findings with the community. In particular, `dolly-v1-6b` struggles with: syntactically complex prompts, programming problems, mathematical operations, factual errors, dates and times, open-ended question answering, hallucination, enumerating lists of specific length, stylistic mimicry, having a sense of humor, etc. ## Training Data, Bias & Objectionable Content Like all language models, `dolly-v1-6b` reflects the content and limitations of its training corpuses. - **The Pile**: GPT-J’s pre-training corpus contains content mostly collected from the public internet, and like most web-scale datasets, it contains content many users would find objectionable. As such, the model is likely to reflect these shortcomings, potentially overtly in the case it is explicitly asked to produce objectionable content, and sometimes subtly, as in the case of biased or harmful implicit associations. - **Stanford Alpaca**: The instruction tuning corpus for dolly-6b can be assumed to share many of the limitations. In addition, it is known to contain factual inaccuracies, semantic and syntactic irregularities, nonsensical responses, and incorrect mathematical calculations, among other data shortcomings. The model outputs will reflect these limitations. Databricks is committed to ongoing research and development efforts to develop helpful, honest and harmless AI technologies that maximize the potential of all individuals and organizations. ## Intended Uses **`dolly-v1-6b` is intended exclusively for research purposes. We do not recommend using `dolly-v1-6b` in high-risk applications (e.g., educational or vocational training, product safety components, or other uses that may impact the well-being of individuals.)** `dolly-v1-6b` is differentiated from its predecessor, GPT-J, in the extent to which it exhibits coherent responses to natural language questions and instruction prompts. `dolly-v1-6b` appears to exhibit reasonable (but categorically not state-of-the-art) behavior across a range of capabilities, including many of those outlined in the [InstructGPT](https://arxiv.org/abs/2203.02155) paper. We include evocative examples below, but invite the reader to observe that they contain factual inaccuracies or semantic misfires that illustrate some of the model’s shortcomings. ### Brainstorming **What are considerations I should keep in mind when planning a backcountry backpacking trip?** When planning a backcountry backpacking trip, there are several considerations to keep in mind. First, make sure you are prepared for any potential weather conditions, including extreme temperatures and lightning storms. Second, consider the route you will take and the terrain that you will be traversing. Third, make sure that you have enough food, water and gear to stay safe and comfortable during your backcountry journey. Fourth, plan for potential wildlife encounters and make sure you have the appropriate safety measures in place. Finally, have an emergency plan in place in case of a medical emergency. ### Classification **Label each of the following as either a scientific concept or a product: Nikon D750, quantum entanglement, CRISPR, and a Macbook Pro.** Nikon D750: Product Quantum entanglement: Scientific Concept CRISPR: Product Macbook Pro: Product ### Extraction **Extract all the people and places from the following passage: Basquiat was born on December 22, 1960, in Park Slope, Brooklyn, New York City, the second of four children to Matilde Basquiat (née Andrades, 1934–2008) and Gérard Basquiat (1930–2013). He had an older brother, Max, who died shortly before his birth, and two younger sisters, Lisane (b. 1964) and Jeanine (b. 1967). His father was born in Port-au-Prince, Haiti and his mother was born in Brooklyn to Puerto Rican parents. He was raised Catholic.** People: Basquiat, Matilde Basquiat, Gérard Basquiat, Max Basquiat, Lisane Basquiat, Jeanine Basquiat. Place: Park Slope, Brooklyn, New York City. ### Generation **Write a press release declaring the completion of Atlantis II, a facility designed for long-term human habitation at the bottom of the ocean. Be sure to mention some of its advanced technological features.** Atlantis II, a new facility designed for long-term human habitation at the bottom of the ocean, is now complete! With advanced technological features, it will be a safe and comfortable living environment for generations to come. From self-sustaining ecosystems to advanced robotics, the facility offers a range of opportunities for exploration, discovery and exploration. Atlantis II will be a home for a wide range of species and will serve as a hub for ocean research, exploration and exploration, bringing together a diverse team of scientists, engineers, biologists, and divers to uncover the mysteries of the deep. ### Summarization **Give me a one line summary of this: Coffee is one of the most widely consumed beverages in the world. It has primarily consumed due to its stimulant effect and unique taste since the ancient times. Afterwards, its consumption has been historically associated with a lower risk of some diseases such as type 2 diabetes mellitus, obesity, cardiovascular disease and some type of cancer and thus it has also consumed due to health benefits. It contains many bioactive compounds such as caffeine, chlorogenic acids and diterpenoid alcohols which have so far been associated with many potential health benefits. For example, caffeine reduces risk of developing neurodegenerative disease and chlorogenic acids (CGA) and diterpene alcohols have many health benefits such as antioxidant and chemo-preventive. Coffee also have harmful effects. For example, diterpenoid alcohols increases serum homocysteine and cholesterol levels and thus it has adverse effects on cardiovascular system. Overall, the study that supports the health benefits of coffee is increasing. But, it is thought-provoking that the association with health benefits of coffee consumption and frequency at different levels in each study. For this reason, we aimed to examine the health effect of the coffee and how much consumption is to investigate whether it meets the claimed health benefits.** Coffee is one of the most widely consumed beverages in the world and has historically been associated with a lower risk of some diseases such as type 2 diabetes mellitus, obesity, cardiovascular disease and some type of cancer. However, there are mixed results in the literature and further research is required to determine the health benefits of coffee consumption. ### Rephrasing **Give me a different way to say the following to a 4 year old: "Son, this is the last time I'm going to tell you. Go to bed!"** "Goodnight, Son. Sleep tight and I'll see you in the morning!" ### Closed QA **I'm going to give you a passage from the book Neuromancer and I'd like you to answer the following question: What is the tool that allows Case to access the matrix? Case was twenty-four. At twenty-two, he'd been a cowboy, a rustler, one of the best in the Sprawl. He'd been trained by the best, by McCoy Pauley and Bobby Quine, legends in the biz. He'd operated on an almost permanent adrenaline high, a byproduct of youth and proficiency, jacked into a custom cyberspace deck that projected his disembodied consciousness into the consensual hallucination that was the matrix.** The tool that allows Case to access the matrix is his cyberspace deck, which allows him to manipulate and interact with the virtual world around him. It enables him to access information from various sources and interact with the people in the matrix, and it is one of the main weapons of Case's cyberworld. ### Open QA **What is the literary style of Jorge Luis Borges?** Jorge Luis Borges's literary style is often described as magical realism. He often creates stories that combine fictional characters with real life elements and use the juxtaposition of these elements to create a sense of surrealism. He often uses allegory and symbolism to explore complex philosophical ideas and explore questions of human perception and understanding. His writing is characterized by a playful and inventive use of language, with characters often being metaphors or symbols for larger, more abstract concepts. ## Usage ### Load Model & Tokenizer ```python import numpy as np from transformers import ( AutoModelForCausalLM, AutoTokenizer, PreTrainedModel, PreTrainedTokenizer ) tokenizer = AutoTokenizer.from_pretrained("databricks/dolly-v1-6b", padding_side="left") model = AutoModelForCausalLM.from_pretrained("databricks/dolly-v1-6b", device_map="auto", trust_remote_code=True) ``` ### Generate Text ```python PROMPT_FORMAT = """Below is an instruction that describes a task. Write a response that appropriately completes the request. ### Instruction: {instruction} ### Response: """ def generate_response(instruction: str, *, model: PreTrainedModel, tokenizer: PreTrainedTokenizer, do_sample: bool = True, max_new_tokens: int = 256, top_p: float = 0.92, top_k: int = 0, **kwargs) -> str: input_ids = tokenizer(PROMPT_FORMAT.format(instruction=instruction), return_tensors="pt").input_ids.to("cuda") # each of these is encoded to a single token response_key_token_id = tokenizer.encode("### Response:")[0] end_key_token_id = tokenizer.encode("### End")[0] gen_tokens = model.generate(input_ids, pad_token_id=tokenizer.pad_token_id, eos_token_id=end_key_token_id, do_sample=do_sample, max_new_tokens=max_new_tokens, top_p=top_p, top_k=top_k, **kwargs)[0].cpu() # find where the response begins response_positions = np.where(gen_tokens == response_key_token_id)[0] if len(response_positions) >= 0: response_pos = response_positions[0] # find where the response ends end_pos = None end_positions = np.where(gen_tokens == end_key_token_id)[0] if len(end_positions) > 0: end_pos = end_positions[0] return tokenizer.decode(gen_tokens[response_pos + 1 : end_pos]).strip() return None # Sample similar to: "Excited to announce the release of Dolly, a powerful new language model from Databricks! #AI #Databricks" generate_response("Write a tweet announcing Dolly, a large language model from Databricks.", model=model, tokenizer=tokenizer) ``` ### Benchmark Metrics Below you'll find various models benchmark performance on the [EleutherAI LLM Evaluation Harness](https://github.com/EleutherAI/lm-evaluation-harness) model results are sorted by geometric mean to produce an intelligible ordering. These results demonstrate that Dolly is not state of the art, as we describe above, but also point to an interesting observation. Namely, Dolly is only marginally better (and in the case of Winogrande worse) and its basemodel GPT-J-6B. Despite this fact, the qualitative behavior of Dolly is materially different from the underlying model ([try it yourself](https://huggingface.co/EleutherAI/gpt-j-6B) on Hugging Face!), which points to meaningful limitations of the existing evaluation benchmarks for measuring the quality of generative models. | model | openbookqa | arc_easy | winogrande | hellaswag | arc_challenge | piqa | boolq | | --------------------------- | ------------ | ---------- | ------------ | ----------- | --------------- | -------- | ---------| | cerebras/Cerebras-GPT-13B | 0.36 | 0.598906 | 0.607735 | 0.593109 | 0.325939 | 0.749728 | 0.611621 | | EleutherAI/gpt-j-6B | 0.382 | 0.621633 | 0.651144 | 0.662617 | 0.363481 | 0.761153 | 0.655963 | | dolly-v1-6b (1 epoch) | 0.428 | 0.608586 | 0.633781 | 0.650568 | 0.377133 | 0.761697 | 0.69633 | | dolly-v1-6b (10 epochs) | 0.41 | 0.62963 | 0.643252 | 0.676758 | 0.384812 | 0.773667 | 0.687768 | | EleutherAI/gpt-neox-20b | 0.402 | 0.683923 | 0.656669 | 0.7142 | 0.408703 | 0.784004 | 0.695413 | # Citation ``` @online{DatabricksBlog2023DollyV1, author = {Mike Conover and Matt Hayes and Ankit Mathur and Xiangrui Meng and Jianwei Xie and Jun Wan and Ali Ghodsi and Patrick Wendell and Matei Zaharia}, title = {Hello Dolly: Democratizing the magic of ChatGPT with open models}, year = {2023}, url = {https://www.databricks.com/blog/2023/03/24/hello-dolly-democratizing-magic-chatgpt-open-models.html}, urldate = {2023-06-30} } ``` # Happy Hacking!

Ancastal/nllb-finetuned-ma-en-ancastal

Ancastal

transformers

text2text-generation

Entry not found

seachus/dqn-SpaceInvadersNoFrameskip-v4

seachus

stable-baselines3

reinforcement-learning

--- library_name: stable-baselines3 tags: - SpaceInvadersNoFrameskip-v4 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: DQN results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: SpaceInvadersNoFrameskip-v4 type: SpaceInvadersNoFrameskip-v4 metrics: - type: mean_reward value: 257.00 +/- 38.81 name: mean_reward verified: false --- # **DQN** Agent playing **SpaceInvadersNoFrameskip-v4** This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/> SB3: https://github.com/DLR-RM/stable-baselines3<br/> SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib Install the RL Zoo (with SB3 and SB3-Contrib): ```bash pip install rl_zoo3 ``` ``` # Download model and save it into the logs/ folder python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga seachus -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do: ``` python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga seachus -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` ## Training (with the RL Zoo) ``` python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga seachus ``` ## Hyperparameters ```python OrderedDict([('batch_size', 32), ('buffer_size', 100000), ('env_wrapper', ['stable_baselines3.common.atari_wrappers.AtariWrapper']), ('exploration_final_eps', 0.01), ('exploration_fraction', 0.1), ('frame_stack', 4), ('gradient_steps', 1), ('learning_rate', 0.0001), ('learning_starts', 100000), ('n_timesteps', 100000.0), ('optimize_memory_usage', False), ('policy', 'CnnPolicy'), ('target_update_interval', 1000), ('train_freq', 4), ('normalize', False)]) ``` # Environment Arguments ```python {'render_mode': 'rgb_array'} ```

abbenedek/whisper-small-cer

abbenedek

Entry not found

OvrK12/t5large538893

OvrK12

Entry not found

BunnyToon/floral-fury

BunnyToon

--- license: openrail ---

HarithKharrufa/whisper-small-hi

HarithKharrufa

transformers

automatic-speech-recognition

Entry not found

jdeklerk10/DS-6.7B-schema_0

jdeklerk10

peft

--- license: other library_name: peft tags: - trl - sft - generated_from_trainer base_model: deepseek-ai/deepseek-coder-6.7b-instruct model-index: - name: DS-6.7B-schema_0 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # DS-6.7B-schema_0 This model is a fine-tuned version of [deepseek-ai/deepseek-coder-6.7b-instruct](https://huggingface.co/deepseek-ai/deepseek-coder-6.7b-instruct) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.1549 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.01 - num_epochs: 1 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 0.0913 | 0.19 | 50 | 0.1589 | | 0.069 | 0.38 | 100 | 0.1580 | | 0.0667 | 0.57 | 150 | 0.1571 | | 0.063 | 0.76 | 200 | 0.1555 | | 0.1838 | 0.95 | 250 | 0.1549 | ### Framework versions - PEFT 0.10.0 - Transformers 4.40.0.dev0 - Pytorch 2.2.1+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2

APLunch/q-FrozenLake-v1-4x4-noSlippery

APLunch

reinforcement-learning

--- tags: - FrozenLake-v1-4x4-no_slippery - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-FrozenLake-v1-4x4-noSlippery results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: FrozenLake-v1-4x4-no_slippery type: FrozenLake-v1-4x4-no_slippery metrics: - type: mean_reward value: 1.00 +/- 0.00 name: mean_reward verified: false --- # **Q-Learning** Agent playing1 **FrozenLake-v1** This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** . ## Usage ```python model = load_from_hub(repo_id="APLunch/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```

HuggingFaceM4/idefics2-chat-tfrm-compatible

HuggingFaceM4

transformers

text2text-generation

Invalid username or password.

heyllm234/sc13

heyllm234

transformers

text-generation

--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

fangzhaoz/mistralv1_lora_r8_25e5_e3

fangzhaoz

peft

--- license: apache-2.0 library_name: peft tags: - generated_from_trainer base_model: mistralai/Mistral-7B-v0.1 model-index: - name: mistralv1_lora_r8_25e5_e3 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # mistralv1_lora_r8_25e5_e3 This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2.5e-05 - train_batch_size: 4 - eval_batch_size: 8 - seed: 0 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results ### Framework versions - PEFT 0.9.0 - Transformers 4.39.3 - Pytorch 2.2.2 - Datasets 2.18.0 - Tokenizers 0.15.2

fangzhaoz/mistralv1_lora_r8_25e5_e3_merged

fangzhaoz

transformers

text-generation

--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

BunnyToon/kings-court

BunnyToon

--- license: openrail ---

EdBerg/gpt-neo-20B

EdBerg

transformers

--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

pabhi18/en_hi_trans_model

pabhi18

--- license: mit ---

Zainabsa99/Llama-2-7b-chat-finetune

Zainabsa99

transformers

text-generation

Entry not found

OwOOwO/dumbo-krillin

OwOOwO

transformers

text-generation

--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

RichardErkhov/thrunlab_-_sparse_mistral_7b_refined_web_50p_2024-04-13-4bits

RichardErkhov

Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) sparse_mistral_7b_refined_web_50p_2024-04-13 - bnb 4bits - Model creator: https://huggingface.co/thrunlab/ - Original model: https://huggingface.co/thrunlab/sparse_mistral_7b_refined_web_50p_2024-04-13/ Original model description: --- license: apache-2.0 base_model: mistralai/Mistral-7B-v0.1 tags: - generated_from_trainer model-index: - name: sparse_mistral_7b_refined_web_50p_2024-04-13 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # sparse_mistral_7b_refined_web_50p_2024-04-13 This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.1985 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 1 - eval_batch_size: 4 - seed: 0 - distributed_type: multi-GPU - num_devices: 4 - gradient_accumulation_steps: 8 - total_train_batch_size: 32 - total_eval_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 2350 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.3391 | 0.01 | 25 | 2.4196 | | 2.2711 | 0.02 | 50 | 2.3577 | | 2.3054 | 0.02 | 75 | 2.3158 | | 2.2795 | 0.03 | 100 | 2.2966 | | 2.3175 | 0.04 | 125 | 2.2846 | | 2.2388 | 0.05 | 150 | 2.2766 | | 2.1679 | 0.06 | 175 | 2.2705 | | 2.2996 | 0.06 | 200 | 2.2678 | | 2.2788 | 0.07 | 225 | 2.2647 | | 2.2448 | 0.08 | 250 | 2.2637 | | 2.1837 | 0.09 | 275 | 2.2624 | | 2.2089 | 0.1 | 300 | 2.2621 | | 2.2686 | 0.1 | 325 | 2.2601 | | 2.2254 | 0.11 | 350 | 2.2593 | | 2.162 | 0.12 | 375 | 2.2590 | | 2.2687 | 0.13 | 400 | 2.2563 | | 2.2595 | 0.14 | 425 | 2.2571 | | 2.186 | 0.14 | 450 | 2.2564 | | 2.2689 | 0.15 | 475 | 2.2580 | | 2.2472 | 0.16 | 500 | 2.2554 | | 2.2005 | 0.17 | 525 | 2.2553 | | 2.1983 | 0.18 | 550 | 2.2552 | | 2.2388 | 0.18 | 575 | 2.2547 | | 2.1443 | 0.19 | 600 | 2.2555 | | 2.2198 | 0.2 | 625 | 2.2534 | | 2.3008 | 0.21 | 650 | 2.2536 | | 2.179 | 0.22 | 675 | 2.2521 | | 2.2069 | 0.22 | 700 | 2.2531 | | 2.1819 | 0.23 | 725 | 2.2526 | | 2.1218 | 0.24 | 750 | 2.2536 | | 2.1845 | 0.25 | 775 | 2.2515 | | 2.2167 | 0.26 | 800 | 2.2510 | | 2.2252 | 0.26 | 825 | 2.2520 | | 2.1664 | 0.27 | 850 | 2.2519 | | 2.1853 | 0.28 | 875 | 2.2530 | | 2.1499 | 0.29 | 900 | 2.2513 | | 2.2763 | 0.3 | 925 | 2.2517 | | 2.2528 | 0.3 | 950 | 2.2518 | | 2.2505 | 0.31 | 975 | 2.2500 | | 2.1683 | 0.32 | 1000 | 2.2502 | | 2.2177 | 0.33 | 1025 | 2.2501 | | 2.238 | 0.34 | 1050 | 2.2516 | | 2.193 | 0.34 | 1075 | 2.2507 | | 2.2025 | 0.35 | 1100 | 2.2502 | | 2.0944 | 0.36 | 1125 | 2.2512 | | 2.2272 | 0.37 | 1150 | 2.2508 | | 2.2264 | 0.38 | 1175 | 2.2500 | | 2.1837 | 0.38 | 1200 | 2.2507 | | 2.1444 | 0.39 | 1225 | 2.2489 | | 2.2464 | 0.4 | 1250 | 2.2499 | | 2.1388 | 0.41 | 1275 | 2.2508 | | 2.193 | 0.42 | 1300 | 2.2492 | | 2.2376 | 0.42 | 1325 | 2.2506 | | 2.2212 | 0.43 | 1350 | 2.2478 | | 2.2002 | 0.44 | 1375 | 2.2488 | | 2.2729 | 0.45 | 1400 | 2.2484 | | 2.2329 | 0.46 | 1425 | 2.2473 | | 2.1919 | 0.46 | 1450 | 2.2481 | | 2.2102 | 0.47 | 1475 | 2.2475 | | 2.1466 | 0.48 | 1500 | 2.2473 | | 2.1819 | 0.49 | 1525 | 2.2478 | | 2.2558 | 0.5 | 1550 | 2.2468 | | 2.2137 | 0.5 | 1575 | 2.2463 | | 2.2288 | 0.51 | 1600 | 2.2466 | | 2.1479 | 0.52 | 1625 | 2.2468 | | 2.1726 | 0.53 | 1650 | 2.2471 | | 2.1805 | 0.54 | 1675 | 2.2454 | | 2.1505 | 0.54 | 1700 | 2.2470 | | 2.1337 | 0.55 | 1725 | 2.2465 | | 2.2413 | 0.56 | 1750 | 2.2460 | | 2.152 | 0.57 | 1775 | 2.2478 | | 2.2669 | 0.58 | 1800 | 2.2471 | | 2.2925 | 0.58 | 1825 | 2.2465 | | 2.222 | 0.59 | 1850 | 2.2457 | | 2.1308 | 0.6 | 1875 | 2.2466 | | 2.201 | 0.61 | 1900 | 2.2456 | | 2.2247 | 0.62 | 1925 | 2.2460 | | 2.2426 | 0.62 | 1950 | 2.2463 | | 2.2312 | 0.63 | 1975 | 2.2465 | | 2.2679 | 0.64 | 2000 | 2.2464 | | 2.1928 | 0.65 | 2025 | 2.2463 | | 2.2087 | 0.66 | 2050 | 2.2455 | | 2.1792 | 0.66 | 2075 | 2.2470 | | 2.252 | 0.67 | 2100 | 2.2468 | | 2.2018 | 0.68 | 2125 | 2.2456 | | 2.2006 | 0.69 | 2150 | 2.2451 | | 2.2076 | 0.7 | 2175 | 2.2449 | | 2.2436 | 0.7 | 2200 | 2.2460 | | 2.2156 | 0.71 | 2225 | 2.2477 | | 2.1348 | 0.72 | 2250 | 2.2455 | | 2.1338 | 0.73 | 2275 | 2.2450 | | 2.2147 | 0.74 | 2300 | 2.2455 | | 2.2766 | 0.74 | 2325 | 2.2444 | | 2.204 | 0.75 | 2350 | 2.2458 | ### Framework versions - Transformers 4.36.2 - Pytorch 2.1.2+cu121 - Datasets 2.15.0 - Tokenizers 0.15.0

cstr/Spaetzle-v60-7b-GGUF

cstr

Entry not found

RichardErkhov/thrunlab_-_sparse_mistral_7b_refined_web_50p_2024-04-13-8bits

RichardErkhov

Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) sparse_mistral_7b_refined_web_50p_2024-04-13 - bnb 8bits - Model creator: https://huggingface.co/thrunlab/ - Original model: https://huggingface.co/thrunlab/sparse_mistral_7b_refined_web_50p_2024-04-13/ Original model description: --- license: apache-2.0 base_model: mistralai/Mistral-7B-v0.1 tags: - generated_from_trainer model-index: - name: sparse_mistral_7b_refined_web_50p_2024-04-13 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # sparse_mistral_7b_refined_web_50p_2024-04-13 This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.1985 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 1 - eval_batch_size: 4 - seed: 0 - distributed_type: multi-GPU - num_devices: 4 - gradient_accumulation_steps: 8 - total_train_batch_size: 32 - total_eval_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 2350 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.3391 | 0.01 | 25 | 2.4196 | | 2.2711 | 0.02 | 50 | 2.3577 | | 2.3054 | 0.02 | 75 | 2.3158 | | 2.2795 | 0.03 | 100 | 2.2966 | | 2.3175 | 0.04 | 125 | 2.2846 | | 2.2388 | 0.05 | 150 | 2.2766 | | 2.1679 | 0.06 | 175 | 2.2705 | | 2.2996 | 0.06 | 200 | 2.2678 | | 2.2788 | 0.07 | 225 | 2.2647 | | 2.2448 | 0.08 | 250 | 2.2637 | | 2.1837 | 0.09 | 275 | 2.2624 | | 2.2089 | 0.1 | 300 | 2.2621 | | 2.2686 | 0.1 | 325 | 2.2601 | | 2.2254 | 0.11 | 350 | 2.2593 | | 2.162 | 0.12 | 375 | 2.2590 | | 2.2687 | 0.13 | 400 | 2.2563 | | 2.2595 | 0.14 | 425 | 2.2571 | | 2.186 | 0.14 | 450 | 2.2564 | | 2.2689 | 0.15 | 475 | 2.2580 | | 2.2472 | 0.16 | 500 | 2.2554 | | 2.2005 | 0.17 | 525 | 2.2553 | | 2.1983 | 0.18 | 550 | 2.2552 | | 2.2388 | 0.18 | 575 | 2.2547 | | 2.1443 | 0.19 | 600 | 2.2555 | | 2.2198 | 0.2 | 625 | 2.2534 | | 2.3008 | 0.21 | 650 | 2.2536 | | 2.179 | 0.22 | 675 | 2.2521 | | 2.2069 | 0.22 | 700 | 2.2531 | | 2.1819 | 0.23 | 725 | 2.2526 | | 2.1218 | 0.24 | 750 | 2.2536 | | 2.1845 | 0.25 | 775 | 2.2515 | | 2.2167 | 0.26 | 800 | 2.2510 | | 2.2252 | 0.26 | 825 | 2.2520 | | 2.1664 | 0.27 | 850 | 2.2519 | | 2.1853 | 0.28 | 875 | 2.2530 | | 2.1499 | 0.29 | 900 | 2.2513 | | 2.2763 | 0.3 | 925 | 2.2517 | | 2.2528 | 0.3 | 950 | 2.2518 | | 2.2505 | 0.31 | 975 | 2.2500 | | 2.1683 | 0.32 | 1000 | 2.2502 | | 2.2177 | 0.33 | 1025 | 2.2501 | | 2.238 | 0.34 | 1050 | 2.2516 | | 2.193 | 0.34 | 1075 | 2.2507 | | 2.2025 | 0.35 | 1100 | 2.2502 | | 2.0944 | 0.36 | 1125 | 2.2512 | | 2.2272 | 0.37 | 1150 | 2.2508 | | 2.2264 | 0.38 | 1175 | 2.2500 | | 2.1837 | 0.38 | 1200 | 2.2507 | | 2.1444 | 0.39 | 1225 | 2.2489 | | 2.2464 | 0.4 | 1250 | 2.2499 | | 2.1388 | 0.41 | 1275 | 2.2508 | | 2.193 | 0.42 | 1300 | 2.2492 | | 2.2376 | 0.42 | 1325 | 2.2506 | | 2.2212 | 0.43 | 1350 | 2.2478 | | 2.2002 | 0.44 | 1375 | 2.2488 | | 2.2729 | 0.45 | 1400 | 2.2484 | | 2.2329 | 0.46 | 1425 | 2.2473 | | 2.1919 | 0.46 | 1450 | 2.2481 | | 2.2102 | 0.47 | 1475 | 2.2475 | | 2.1466 | 0.48 | 1500 | 2.2473 | | 2.1819 | 0.49 | 1525 | 2.2478 | | 2.2558 | 0.5 | 1550 | 2.2468 | | 2.2137 | 0.5 | 1575 | 2.2463 | | 2.2288 | 0.51 | 1600 | 2.2466 | | 2.1479 | 0.52 | 1625 | 2.2468 | | 2.1726 | 0.53 | 1650 | 2.2471 | | 2.1805 | 0.54 | 1675 | 2.2454 | | 2.1505 | 0.54 | 1700 | 2.2470 | | 2.1337 | 0.55 | 1725 | 2.2465 | | 2.2413 | 0.56 | 1750 | 2.2460 | | 2.152 | 0.57 | 1775 | 2.2478 | | 2.2669 | 0.58 | 1800 | 2.2471 | | 2.2925 | 0.58 | 1825 | 2.2465 | | 2.222 | 0.59 | 1850 | 2.2457 | | 2.1308 | 0.6 | 1875 | 2.2466 | | 2.201 | 0.61 | 1900 | 2.2456 | | 2.2247 | 0.62 | 1925 | 2.2460 | | 2.2426 | 0.62 | 1950 | 2.2463 | | 2.2312 | 0.63 | 1975 | 2.2465 | | 2.2679 | 0.64 | 2000 | 2.2464 | | 2.1928 | 0.65 | 2025 | 2.2463 | | 2.2087 | 0.66 | 2050 | 2.2455 | | 2.1792 | 0.66 | 2075 | 2.2470 | | 2.252 | 0.67 | 2100 | 2.2468 | | 2.2018 | 0.68 | 2125 | 2.2456 | | 2.2006 | 0.69 | 2150 | 2.2451 | | 2.2076 | 0.7 | 2175 | 2.2449 | | 2.2436 | 0.7 | 2200 | 2.2460 | | 2.2156 | 0.71 | 2225 | 2.2477 | | 2.1348 | 0.72 | 2250 | 2.2455 | | 2.1338 | 0.73 | 2275 | 2.2450 | | 2.2147 | 0.74 | 2300 | 2.2455 | | 2.2766 | 0.74 | 2325 | 2.2444 | | 2.204 | 0.75 | 2350 | 2.2458 | ### Framework versions - Transformers 4.36.2 - Pytorch 2.1.2+cu121 - Datasets 2.15.0 - Tokenizers 0.15.0

boyan0814/distilgpt2-finetuned-github_cybersecurity_READMEs

boyan0814

Entry not found

OvrK12/t5largeGenreDetection

OvrK12

Entry not found

RichardErkhov/thrunlab_-_sparse_mistral_7b_refined_web_50p_2024-04-13-gguf

RichardErkhov

Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) sparse_mistral_7b_refined_web_50p_2024-04-13 - GGUF - Model creator: https://huggingface.co/thrunlab/ - Original model: https://huggingface.co/thrunlab/sparse_mistral_7b_refined_web_50p_2024-04-13/ | Name | Quant method | Size | | ---- | ---- | ---- | | [sparse_mistral_7b_refined_web_50p_2024-04-13.Q2_K.gguf](https://huggingface.co/RichardErkhov/thrunlab_-_sparse_mistral_7b_refined_web_50p_2024-04-13-gguf/blob/main/sparse_mistral_7b_refined_web_50p_2024-04-13.Q2_K.gguf) | Q2_K | 2.53GB | | [sparse_mistral_7b_refined_web_50p_2024-04-13.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/thrunlab_-_sparse_mistral_7b_refined_web_50p_2024-04-13-gguf/blob/main/sparse_mistral_7b_refined_web_50p_2024-04-13.IQ3_XS.gguf) | IQ3_XS | 2.81GB | | [sparse_mistral_7b_refined_web_50p_2024-04-13.IQ3_S.gguf](https://huggingface.co/RichardErkhov/thrunlab_-_sparse_mistral_7b_refined_web_50p_2024-04-13-gguf/blob/main/sparse_mistral_7b_refined_web_50p_2024-04-13.IQ3_S.gguf) | IQ3_S | 2.96GB | | [sparse_mistral_7b_refined_web_50p_2024-04-13.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/thrunlab_-_sparse_mistral_7b_refined_web_50p_2024-04-13-gguf/blob/main/sparse_mistral_7b_refined_web_50p_2024-04-13.Q3_K_S.gguf) | Q3_K_S | 2.95GB | | [sparse_mistral_7b_refined_web_50p_2024-04-13.IQ3_M.gguf](https://huggingface.co/RichardErkhov/thrunlab_-_sparse_mistral_7b_refined_web_50p_2024-04-13-gguf/blob/main/sparse_mistral_7b_refined_web_50p_2024-04-13.IQ3_M.gguf) | IQ3_M | 3.06GB | | [sparse_mistral_7b_refined_web_50p_2024-04-13.Q3_K.gguf](https://huggingface.co/RichardErkhov/thrunlab_-_sparse_mistral_7b_refined_web_50p_2024-04-13-gguf/blob/main/sparse_mistral_7b_refined_web_50p_2024-04-13.Q3_K.gguf) | Q3_K | 3.28GB | | [sparse_mistral_7b_refined_web_50p_2024-04-13.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/thrunlab_-_sparse_mistral_7b_refined_web_50p_2024-04-13-gguf/blob/main/sparse_mistral_7b_refined_web_50p_2024-04-13.Q3_K_M.gguf) | Q3_K_M | 3.28GB | | [sparse_mistral_7b_refined_web_50p_2024-04-13.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/thrunlab_-_sparse_mistral_7b_refined_web_50p_2024-04-13-gguf/blob/main/sparse_mistral_7b_refined_web_50p_2024-04-13.Q3_K_L.gguf) | Q3_K_L | 3.56GB | | [sparse_mistral_7b_refined_web_50p_2024-04-13.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/thrunlab_-_sparse_mistral_7b_refined_web_50p_2024-04-13-gguf/blob/main/sparse_mistral_7b_refined_web_50p_2024-04-13.IQ4_XS.gguf) | IQ4_XS | 3.67GB | | [sparse_mistral_7b_refined_web_50p_2024-04-13.Q4_0.gguf](https://huggingface.co/RichardErkhov/thrunlab_-_sparse_mistral_7b_refined_web_50p_2024-04-13-gguf/blob/main/sparse_mistral_7b_refined_web_50p_2024-04-13.Q4_0.gguf) | Q4_0 | 3.83GB | | [sparse_mistral_7b_refined_web_50p_2024-04-13.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/thrunlab_-_sparse_mistral_7b_refined_web_50p_2024-04-13-gguf/blob/main/sparse_mistral_7b_refined_web_50p_2024-04-13.IQ4_NL.gguf) | IQ4_NL | 3.87GB | | [sparse_mistral_7b_refined_web_50p_2024-04-13.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/thrunlab_-_sparse_mistral_7b_refined_web_50p_2024-04-13-gguf/blob/main/sparse_mistral_7b_refined_web_50p_2024-04-13.Q4_K_S.gguf) | Q4_K_S | 3.86GB | | [sparse_mistral_7b_refined_web_50p_2024-04-13.Q4_K.gguf](https://huggingface.co/RichardErkhov/thrunlab_-_sparse_mistral_7b_refined_web_50p_2024-04-13-gguf/blob/main/sparse_mistral_7b_refined_web_50p_2024-04-13.Q4_K.gguf) | Q4_K | 4.07GB | | [sparse_mistral_7b_refined_web_50p_2024-04-13.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/thrunlab_-_sparse_mistral_7b_refined_web_50p_2024-04-13-gguf/blob/main/sparse_mistral_7b_refined_web_50p_2024-04-13.Q4_K_M.gguf) | Q4_K_M | 4.07GB | | [sparse_mistral_7b_refined_web_50p_2024-04-13.Q4_1.gguf](https://huggingface.co/RichardErkhov/thrunlab_-_sparse_mistral_7b_refined_web_50p_2024-04-13-gguf/blob/main/sparse_mistral_7b_refined_web_50p_2024-04-13.Q4_1.gguf) | Q4_1 | 4.24GB | | [sparse_mistral_7b_refined_web_50p_2024-04-13.Q5_0.gguf](https://huggingface.co/RichardErkhov/thrunlab_-_sparse_mistral_7b_refined_web_50p_2024-04-13-gguf/blob/main/sparse_mistral_7b_refined_web_50p_2024-04-13.Q5_0.gguf) | Q5_0 | 4.65GB | | [sparse_mistral_7b_refined_web_50p_2024-04-13.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/thrunlab_-_sparse_mistral_7b_refined_web_50p_2024-04-13-gguf/blob/main/sparse_mistral_7b_refined_web_50p_2024-04-13.Q5_K_S.gguf) | Q5_K_S | 4.65GB | | [sparse_mistral_7b_refined_web_50p_2024-04-13.Q5_K.gguf](https://huggingface.co/RichardErkhov/thrunlab_-_sparse_mistral_7b_refined_web_50p_2024-04-13-gguf/blob/main/sparse_mistral_7b_refined_web_50p_2024-04-13.Q5_K.gguf) | Q5_K | 4.78GB | | [sparse_mistral_7b_refined_web_50p_2024-04-13.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/thrunlab_-_sparse_mistral_7b_refined_web_50p_2024-04-13-gguf/blob/main/sparse_mistral_7b_refined_web_50p_2024-04-13.Q5_K_M.gguf) | Q5_K_M | 4.78GB | | [sparse_mistral_7b_refined_web_50p_2024-04-13.Q5_1.gguf](https://huggingface.co/RichardErkhov/thrunlab_-_sparse_mistral_7b_refined_web_50p_2024-04-13-gguf/blob/main/sparse_mistral_7b_refined_web_50p_2024-04-13.Q5_1.gguf) | Q5_1 | 5.07GB | | [sparse_mistral_7b_refined_web_50p_2024-04-13.Q6_K.gguf](https://huggingface.co/RichardErkhov/thrunlab_-_sparse_mistral_7b_refined_web_50p_2024-04-13-gguf/blob/main/sparse_mistral_7b_refined_web_50p_2024-04-13.Q6_K.gguf) | Q6_K | 5.53GB | Original model description: --- license: apache-2.0 base_model: mistralai/Mistral-7B-v0.1 tags: - generated_from_trainer model-index: - name: sparse_mistral_7b_refined_web_50p_2024-04-13 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # sparse_mistral_7b_refined_web_50p_2024-04-13 This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.1985 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 1 - eval_batch_size: 4 - seed: 0 - distributed_type: multi-GPU - num_devices: 4 - gradient_accumulation_steps: 8 - total_train_batch_size: 32 - total_eval_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 2350 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.3391 | 0.01 | 25 | 2.4196 | | 2.2711 | 0.02 | 50 | 2.3577 | | 2.3054 | 0.02 | 75 | 2.3158 | | 2.2795 | 0.03 | 100 | 2.2966 | | 2.3175 | 0.04 | 125 | 2.2846 | | 2.2388 | 0.05 | 150 | 2.2766 | | 2.1679 | 0.06 | 175 | 2.2705 | | 2.2996 | 0.06 | 200 | 2.2678 | | 2.2788 | 0.07 | 225 | 2.2647 | | 2.2448 | 0.08 | 250 | 2.2637 | | 2.1837 | 0.09 | 275 | 2.2624 | | 2.2089 | 0.1 | 300 | 2.2621 | | 2.2686 | 0.1 | 325 | 2.2601 | | 2.2254 | 0.11 | 350 | 2.2593 | | 2.162 | 0.12 | 375 | 2.2590 | | 2.2687 | 0.13 | 400 | 2.2563 | | 2.2595 | 0.14 | 425 | 2.2571 | | 2.186 | 0.14 | 450 | 2.2564 | | 2.2689 | 0.15 | 475 | 2.2580 | | 2.2472 | 0.16 | 500 | 2.2554 | | 2.2005 | 0.17 | 525 | 2.2553 | | 2.1983 | 0.18 | 550 | 2.2552 | | 2.2388 | 0.18 | 575 | 2.2547 | | 2.1443 | 0.19 | 600 | 2.2555 | | 2.2198 | 0.2 | 625 | 2.2534 | | 2.3008 | 0.21 | 650 | 2.2536 | | 2.179 | 0.22 | 675 | 2.2521 | | 2.2069 | 0.22 | 700 | 2.2531 | | 2.1819 | 0.23 | 725 | 2.2526 | | 2.1218 | 0.24 | 750 | 2.2536 | | 2.1845 | 0.25 | 775 | 2.2515 | | 2.2167 | 0.26 | 800 | 2.2510 | | 2.2252 | 0.26 | 825 | 2.2520 | | 2.1664 | 0.27 | 850 | 2.2519 | | 2.1853 | 0.28 | 875 | 2.2530 | | 2.1499 | 0.29 | 900 | 2.2513 | | 2.2763 | 0.3 | 925 | 2.2517 | | 2.2528 | 0.3 | 950 | 2.2518 | | 2.2505 | 0.31 | 975 | 2.2500 | | 2.1683 | 0.32 | 1000 | 2.2502 | | 2.2177 | 0.33 | 1025 | 2.2501 | | 2.238 | 0.34 | 1050 | 2.2516 | | 2.193 | 0.34 | 1075 | 2.2507 | | 2.2025 | 0.35 | 1100 | 2.2502 | | 2.0944 | 0.36 | 1125 | 2.2512 | | 2.2272 | 0.37 | 1150 | 2.2508 | | 2.2264 | 0.38 | 1175 | 2.2500 | | 2.1837 | 0.38 | 1200 | 2.2507 | | 2.1444 | 0.39 | 1225 | 2.2489 | | 2.2464 | 0.4 | 1250 | 2.2499 | | 2.1388 | 0.41 | 1275 | 2.2508 | | 2.193 | 0.42 | 1300 | 2.2492 | | 2.2376 | 0.42 | 1325 | 2.2506 | | 2.2212 | 0.43 | 1350 | 2.2478 | | 2.2002 | 0.44 | 1375 | 2.2488 | | 2.2729 | 0.45 | 1400 | 2.2484 | | 2.2329 | 0.46 | 1425 | 2.2473 | | 2.1919 | 0.46 | 1450 | 2.2481 | | 2.2102 | 0.47 | 1475 | 2.2475 | | 2.1466 | 0.48 | 1500 | 2.2473 | | 2.1819 | 0.49 | 1525 | 2.2478 | | 2.2558 | 0.5 | 1550 | 2.2468 | | 2.2137 | 0.5 | 1575 | 2.2463 | | 2.2288 | 0.51 | 1600 | 2.2466 | | 2.1479 | 0.52 | 1625 | 2.2468 | | 2.1726 | 0.53 | 1650 | 2.2471 | | 2.1805 | 0.54 | 1675 | 2.2454 | | 2.1505 | 0.54 | 1700 | 2.2470 | | 2.1337 | 0.55 | 1725 | 2.2465 | | 2.2413 | 0.56 | 1750 | 2.2460 | | 2.152 | 0.57 | 1775 | 2.2478 | | 2.2669 | 0.58 | 1800 | 2.2471 | | 2.2925 | 0.58 | 1825 | 2.2465 | | 2.222 | 0.59 | 1850 | 2.2457 | | 2.1308 | 0.6 | 1875 | 2.2466 | | 2.201 | 0.61 | 1900 | 2.2456 | | 2.2247 | 0.62 | 1925 | 2.2460 | | 2.2426 | 0.62 | 1950 | 2.2463 | | 2.2312 | 0.63 | 1975 | 2.2465 | | 2.2679 | 0.64 | 2000 | 2.2464 | | 2.1928 | 0.65 | 2025 | 2.2463 | | 2.2087 | 0.66 | 2050 | 2.2455 | | 2.1792 | 0.66 | 2075 | 2.2470 | | 2.252 | 0.67 | 2100 | 2.2468 | | 2.2018 | 0.68 | 2125 | 2.2456 | | 2.2006 | 0.69 | 2150 | 2.2451 | | 2.2076 | 0.7 | 2175 | 2.2449 | | 2.2436 | 0.7 | 2200 | 2.2460 | | 2.2156 | 0.71 | 2225 | 2.2477 | | 2.1348 | 0.72 | 2250 | 2.2455 | | 2.1338 | 0.73 | 2275 | 2.2450 | | 2.2147 | 0.74 | 2300 | 2.2455 | | 2.2766 | 0.74 | 2325 | 2.2444 | | 2.204 | 0.75 | 2350 | 2.2458 | ### Framework versions - Transformers 4.36.2 - Pytorch 2.1.2+cu121 - Datasets 2.15.0 - Tokenizers 0.15.0

mooo16/Gemma-all

mooo16

Entry not found

izaznov/taxi_3_Q_learning

izaznov

reinforcement-learning

--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: taxi_3_Q_learning results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.56 +/- 2.71 name: mean_reward verified: false --- # **Q-Learning** Agent playing1 **Taxi-v3** This is a trained model of a **Q-Learning** agent playing **Taxi-v3** . ## Usage ```python model = load_from_hub(repo_id="izaznov/taxi_3_Q_learning", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```

Elisa/question_answering_finetuned

Elisa

transformers

question-answering

Entry not found

Lewdiculous/Aura_7B-GGUF-IQ-Imatrix

Lewdiculous

--- inference: false tags: - gguf - quantized - roleplay - multimodal - vision - llava - sillytavern - merge - mistral - conversational --- # #Roleplay #Writing #Creative This repository hosts GGUF-IQ-Imatrix quants for [ResplendentAI/Aura_7B](https://huggingface.co/ResplendentAI/Aura_7B). "Please read all the model information at the bottom of the card."  **What does "Imatrix" mean?** <details><summary> ⇲ Click here to expand/hide more information about this topic. </summary> It stands for **Importance Matrix**, a technique used to improve the quality of quantized models. The **Imatrix** is calculated based on calibration data, and it helps determine the importance of different model activations during the quantization process. The idea is to preserve the most important information during quantization, which can help reduce the loss of model performance, especially when the calibration data is diverse. [[1]](https://github.com/ggerganov/llama.cpp/discussions/5006) [[2]](https://github.com/ggerganov/llama.cpp/discussions/5263#discussioncomment-8395384) For imatrix data generation, kalomaze's `groups_merged.txt` with additional roleplay chats was used, you can find it [here](https://huggingface.co/Lewdiculous/Nyanade_Stunna-Maid-7B-GGUF-IQ-Imatrix/blob/main/imatrix-with-rp-ex.txt). This was just to add a bit more diversity to the data with the intended use case in mind. </details><br> # Quantization information: <details><summary> ⇲ Click here to expand/hide more information about this topic. </summary> ```python quantization_options = [ "Q4_K_M", "Q4_K_S", "IQ4_XS", "Q5_K_M", "Q5_K_S", "Q6_K", "Q8_0", "IQ3_M", "IQ3_S", "IQ3_XXS" ] ``` **Steps performed:** ``` Base⇢ GGUF(F16)⇢ Imatrix-Data(F16)⇢ GGUF(Imatrix-Quants) ``` *Using the latest llama.cpp at the time.* </details><br> # Original model information: Aura is an advanced sentience simulation trained on my own philosophical writings. It has been tested with various character cards and it worked with all of them. This model may not be overly intelligent, but it aims to be an entertaining companion. I recommend keeping the temperature around 1.5 or lower with a Min P value of 0.05. This model can get carried away with prose at higher temperature. I will say though that the prose of this model is distinct from the GPT 3.5/4 variant, and lends an air of humanity to the outputs. I am aware that this model is overfit, but that was the point of the entire exercise. If you have trouble getting the model to follow an asterisks/quote format, I recommend asterisks/plaintext instead. This model skews toward shorter outputs, so be prepared to lengthen your introduction and examples if you want longer outputs. This model responds best to ChatML for multiturn conversations.

mradermacher/sheep-duck-llama-2-70b-v1.1-GGUF

mradermacher

--- exported_from: Riiid/sheep-duck-llama-2-70b-v1.1 language: - en library_name: transformers license: llama2 quantized_by: mradermacher tags: - Riiid - llama-2 - sheep-duck-llama-2 --- ## About <!-- ### quantize_version: 1 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: --> <!-- ### vocab_type: --> static quants of https://huggingface.co/Riiid/sheep-duck-llama-2-70b-v1.1 <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/sheep-duck-llama-2-70b-v1.1-i1-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) | Link | Type | Size/GB | Notes | |:-----|:-----|--------:|:------| | [GGUF](https://huggingface.co/mradermacher/sheep-duck-llama-2-70b-v1.1-GGUF/resolve/main/sheep-duck-llama-2-70b-v1.1.Q2_K.gguf) | Q2_K | 25.6 | | | [GGUF](https://huggingface.co/mradermacher/sheep-duck-llama-2-70b-v1.1-GGUF/resolve/main/sheep-duck-llama-2-70b-v1.1.IQ3_XS.gguf) | IQ3_XS | 28.4 | | | [GGUF](https://huggingface.co/mradermacher/sheep-duck-llama-2-70b-v1.1-GGUF/resolve/main/sheep-duck-llama-2-70b-v1.1.IQ3_S.gguf) | IQ3_S | 30.0 | beats Q3_K* | | [GGUF](https://huggingface.co/mradermacher/sheep-duck-llama-2-70b-v1.1-GGUF/resolve/main/sheep-duck-llama-2-70b-v1.1.Q3_K_S.gguf) | Q3_K_S | 30.0 | | | [GGUF](https://huggingface.co/mradermacher/sheep-duck-llama-2-70b-v1.1-GGUF/resolve/main/sheep-duck-llama-2-70b-v1.1.IQ3_M.gguf) | IQ3_M | 31.0 | | | [GGUF](https://huggingface.co/mradermacher/sheep-duck-llama-2-70b-v1.1-GGUF/resolve/main/sheep-duck-llama-2-70b-v1.1.Q3_K_M.gguf) | Q3_K_M | 33.4 | lower quality | | [GGUF](https://huggingface.co/mradermacher/sheep-duck-llama-2-70b-v1.1-GGUF/resolve/main/sheep-duck-llama-2-70b-v1.1.Q3_K_L.gguf) | Q3_K_L | 36.2 | | | [GGUF](https://huggingface.co/mradermacher/sheep-duck-llama-2-70b-v1.1-GGUF/resolve/main/sheep-duck-llama-2-70b-v1.1.IQ4_XS.gguf) | IQ4_XS | 37.3 | | | [GGUF](https://huggingface.co/mradermacher/sheep-duck-llama-2-70b-v1.1-GGUF/resolve/main/sheep-duck-llama-2-70b-v1.1.Q4_K_S.gguf) | Q4_K_S | 39.3 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/sheep-duck-llama-2-70b-v1.1-GGUF/resolve/main/sheep-duck-llama-2-70b-v1.1.Q4_K_M.gguf) | Q4_K_M | 41.5 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/sheep-duck-llama-2-70b-v1.1-GGUF/resolve/main/sheep-duck-llama-2-70b-v1.1.Q5_K_S.gguf) | Q5_K_S | 47.6 | | | [GGUF](https://huggingface.co/mradermacher/sheep-duck-llama-2-70b-v1.1-GGUF/resolve/main/sheep-duck-llama-2-70b-v1.1.Q5_K_M.gguf) | Q5_K_M | 48.9 | | | [PART 1](https://huggingface.co/mradermacher/sheep-duck-llama-2-70b-v1.1-GGUF/resolve/main/sheep-duck-llama-2-70b-v1.1.Q6_K.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/sheep-duck-llama-2-70b-v1.1-GGUF/resolve/main/sheep-duck-llama-2-70b-v1.1.Q6_K.gguf.part2of2) | Q6_K | 56.7 | very good quality | | [PART 1](https://huggingface.co/mradermacher/sheep-duck-llama-2-70b-v1.1-GGUF/resolve/main/sheep-duck-llama-2-70b-v1.1.Q8_0.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/sheep-duck-llama-2-70b-v1.1-GGUF/resolve/main/sheep-duck-llama-2-70b-v1.1.Q8_0.gguf.part2of2) | Q8_0 | 73.4 | fast, best quality | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better):  And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->

stinoco/whisper-tiny-clinical-assistance

stinoco

transformers

automatic-speech-recognition

--- language: - es license: apache-2.0 base_model: openai/whisper-tiny tags: - generated_from_trainer datasets: - stinoco/es_clinical_assistance model-index: - name: Whisper Chilean Spanish Small results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Whisper Chilean Spanish Small This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the stinoco/es_clinical_assistance dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 10 ### Training results ### Framework versions - Transformers 4.40.0.dev0 - Pytorch 2.0.0+cu117 - Datasets 2.18.0 - Tokenizers 0.15.2

shiro76/DBO_Train

shiro76

--- license: apache-2.0 ---

lleticiasilvaa/TinyLlama-1.1B-Chat-v1.0-checkpoint-25

lleticiasilvaa

transformers

text-generation

--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

IrwinD/log_sage_reward_model

IrwinD

transformers

text-classification

--- license: apache-2.0 base_model: distilbert/distilbert-base-uncased tags: - trl - reward-trainer - generated_from_trainer datasets: - hdfs_rlhf_log_summary_dataset metrics: - accuracy model-index: - name: log_sage_reward_model results: - task: name: Text Classification type: text-classification dataset: name: hdfs_rlhf_log_summary_dataset type: hdfs_rlhf_log_summary_dataset config: default split: None args: default metrics: - name: Accuracy type: accuracy value: 1.0 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # log_sage_reward_model This model is a fine-tuned version of [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) on the hdfs_rlhf_log_summary_dataset dataset. It achieves the following results on the evaluation set: - Loss: 0.4346 - Accuracy: 1.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1.41e-05 - train_batch_size: 6 - eval_batch_size: 24 - seed: 42 - gradient_accumulation_steps: 16 - total_train_batch_size: 96 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 40 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 1 | 0.6931 | 0.8 | | No log | 2.0 | 2 | 0.6932 | 0.8 | | No log | 3.0 | 3 | 0.6927 | 0.8 | | No log | 4.0 | 4 | 0.6916 | 1.0 | | No log | 5.0 | 5 | 0.6895 | 1.0 | | No log | 6.0 | 6 | 0.6861 | 1.0 | | No log | 7.0 | 7 | 0.6800 | 1.0 | | No log | 8.0 | 8 | 0.6720 | 1.0 | | No log | 9.0 | 9 | 0.6613 | 1.0 | | 0.3371 | 10.0 | 10 | 0.6489 | 1.0 | | 0.3371 | 11.0 | 11 | 0.6380 | 1.0 | | 0.3371 | 12.0 | 12 | 0.6289 | 1.0 | | 0.3371 | 13.0 | 13 | 0.6243 | 1.0 | | 0.3371 | 14.0 | 14 | 0.6158 | 1.0 | | 0.3371 | 15.0 | 15 | 0.6049 | 1.0 | | 0.3371 | 16.0 | 16 | 0.5933 | 1.0 | | 0.3371 | 17.0 | 17 | 0.5812 | 1.0 | | 0.3371 | 18.0 | 18 | 0.5619 | 1.0 | | 0.3371 | 19.0 | 19 | 0.5455 | 1.0 | | 0.2662 | 20.0 | 20 | 0.5372 | 1.0 | | 0.2662 | 21.0 | 21 | 0.5299 | 1.0 | | 0.2662 | 22.0 | 22 | 0.5194 | 1.0 | | 0.2662 | 23.0 | 23 | 0.5001 | 1.0 | | 0.2662 | 24.0 | 24 | 0.4711 | 1.0 | | 0.2662 | 25.0 | 25 | 0.4529 | 1.0 | | 0.2662 | 26.0 | 26 | 0.4462 | 1.0 | | 0.2662 | 27.0 | 27 | 0.4399 | 1.0 | | 0.2662 | 28.0 | 28 | 0.4361 | 1.0 | | 0.2662 | 29.0 | 29 | 0.4353 | 1.0 | | 0.198 | 30.0 | 30 | 0.4391 | 1.0 | | 0.198 | 31.0 | 31 | 0.4491 | 1.0 | | 0.198 | 32.0 | 32 | 0.4516 | 1.0 | | 0.198 | 33.0 | 33 | 0.4486 | 1.0 | | 0.198 | 34.0 | 34 | 0.4529 | 1.0 | | 0.198 | 35.0 | 35 | 0.4516 | 1.0 | | 0.198 | 36.0 | 36 | 0.4518 | 1.0 | | 0.198 | 37.0 | 37 | 0.4468 | 1.0 | | 0.198 | 38.0 | 38 | 0.4412 | 1.0 | | 0.198 | 39.0 | 39 | 0.4369 | 1.0 | | 0.1619 | 40.0 | 40 | 0.4346 | 1.0 | ### Framework versions - Transformers 4.39.0 - Pytorch 2.2.1+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2

jdeklerk10/DS-6.7B-schema

jdeklerk10

peft

--- license: other library_name: peft tags: - trl - sft - generated_from_trainer base_model: deepseek-ai/deepseek-coder-6.7b-instruct model-index: - name: DS-6.7B-schema results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # DS-6.7B-schema This model is a fine-tuned version of [deepseek-ai/deepseek-coder-6.7b-instruct](https://huggingface.co/deepseek-ai/deepseek-coder-6.7b-instruct) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.1412 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.01 - num_epochs: 1 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 1.5484 | 0.19 | 50 | 0.1685 | | 0.1696 | 0.38 | 100 | 0.1607 | | 0.1252 | 0.57 | 150 | 0.1452 | | 0.1023 | 0.76 | 200 | 0.1409 | | 1.6109 | 0.95 | 250 | 0.1412 | ### Framework versions - PEFT 0.10.0 - Transformers 4.40.0.dev0 - Pytorch 2.2.1+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2

Gordon119/TAT_TD-openai-whisper-large-v2-mix-with-zh-TAT-epoch3-total5epoch

Gordon119

transformers

--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

zzttbrdd/sn6_10m

zzttbrdd

transformers

text-generation

--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

carolynayamamoto/biogpt-customized-dataset

carolynayamamoto

transformers

token-classification

Entry not found

akumar23/mental-falcon-7b

akumar23

transformers

--- library_name: transformers license: apache-2.0 --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> Builds on the falcon 7b instruct shared model, the specific model is linked in the model description section. Trained using this [mental health dataset](https://huggingface.co/datasets/Amod/mental_health_counseling_conversations) How to test the model ```python import json import os from pprint import pprint import bitsandbytes as bnb import torch import torch.nn as nn import transformers from datasets import load_dataset from huggingface_hub import notebook_login from peft import ( LoraConfig, PeftConfig, PeftModel, get_peft_model, prepare_model_for_kbit_training ) from transformers import ( AutoConfig, AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig ) os.environ["CUDA_VISIBLE_DEVICES"] = "0" PEFT_MODEL = "akumar23/mental-falcon-7b" bnb_config = BitsAndBytesConfig( load_in_4bit=True, bnb_4bit_use_double_quant=True, bnb_4bit_quant_type="nf4", bnb_4bit_compute_dtype=torch.bfloat16 ) config = PeftConfig.from_pretrained(PEFT_MODEL) model = AutoModelForCausalLM.from_pretrained( config.base_model_name_or_path, return_dict=True, quantization_config=bnb_config, device_map="auto", trust_remote_code=True ) tokenizer=AutoTokenizer.from_pretrained(config.base_model_name_or_path) tokenizer.pad_token = tokenizer.eos_token model = PeftModel.from_pretrained(model, PEFT_MODEL) generation_config = model.generation_config generation_config.max_new_tokens = 200 generation_config.temperature = 0.7 generation_config.top_p = 0.7 generation_config.num_return_sequences = 1 generation_config.pad_token_id = tokenizer.eos_token_id generation_config.eos_token_id = tokenizer.eos_token_id device = "cuda:0" prompt = """ <human>: how do i know if i am depressed <assistant>: """.strip() encoding = tokenizer(prompt, return_tensors="pt").to(device) with torch.inference_mode(): outputs = model.generate( input_ids = encoding.input_ids, attention_mask = encoding.attention_mask, generation_config = generation_config ) print(tokenizer.decode(outputs[0], skip_special_tokens=True)) ``` ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [Aryan Kumar](https://www.linkedin.com/in/aryan-kumar-9640b4150); - **Model type:** Causal decoder-only; - **Language(s) (NLP):** English and French; - **License:** Apache 2.0; - **Finetuned from model:** [falcon-7b-instruct-sharded](https://huggingface.co/vilsonrodrigues/falcon-7b-instruct-sharded). ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

EdBerg/gpt-neo-2B

EdBerg

transformers

--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]