Datasets:

librarian-bots

/

model_cards_with_metadata_test

like 1

ReplaceHumanWithAI/qwen1.5-llm

ReplaceHumanWithAI

Entry not found

axssel/duncan_robinson

axssel

Entry not found

anon11112/bikiniunder

anon11112

Entry not found

aitorrent/dolphin-2.9.2-qwen2-7b-gguf

aitorrent

text-generation

--- quantized_by: bartowski pipeline_tag: text-generation tags: - torrent --- [](https://ko-fi.com/U7U2ZEFWU) ## Llamacpp imatrix Quantizations of dolphin-2.9.2-qwen2-7b Using <a href="https://github.com/ggerganov/llama.cpp/">llama.cpp</a> release <a href="https://github.com/ggerganov/llama.cpp/releases/tag/b2965">b2965</a> for quantization. Original model: https://huggingface.co/cognitivecomputations/dolphin-2.9.2-qwen2-7b All quants made using imatrix option with dataset from [here](https://gist.github.com/bartowski1182/b6ac44691e994344625687afe3263b3a) ## Prompt format ``` <|im_start|>system {system_prompt}<|im_end|> <|im_start|>user {prompt}<|im_end|> <|im_start|>assistant ``` ## Downloading using huggingface-cli First, make sure you have hugginface-cli installed: ``` pip install -U "huggingface_hub[cli]" ``` Then, you can target the specific file you want: ``` huggingface-cli download bartowski/dolphin-2.9.2-qwen2-7b-GGUF --include "dolphin-2.9.2-qwen2-7b-Q4_K_M.gguf" --local-dir ./ ``` If the model is bigger than 50GB, it will have been split into multiple files. In order to download them all to a local folder, run: ``` huggingface-cli download bartowski/dolphin-2.9.2-qwen2-7b-GGUF --include "dolphin-2.9.2-qwen2-7b-Q8_0.gguf/*" --local-dir dolphin-2.9.2-qwen2-7b-Q8_0 ``` You can either specify a new local-dir (dolphin-2.9.2-qwen2-7b-Q8_0) or download them all in place (./) ## Which file should I choose? A great write up with charts showing various performances is provided by Artefact2 [here](https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9) The first thing to figure out is how big a model you can run. To do this, you'll need to figure out how much RAM and/or VRAM you have. If you want your model running as FAST as possible, you'll want to fit the whole thing on your GPU's VRAM. Aim for a quant with a file size 1-2GB smaller than your GPU's total VRAM. If you want the absolute maximum quality, add both your system RAM and your GPU's VRAM together, then similarly grab a quant with a file size 1-2GB Smaller than that total. Next, you'll need to decide if you want to use an 'I-quant' or a 'K-quant'. If you don't want to think too much, grab one of the K-quants. These are in format 'QX_K_X', like Q5_K_M. If you want to get more into the weeds, you can check out this extremely useful feature chart: [llama.cpp feature matrix](https://github.com/ggerganov/llama.cpp/wiki/Feature-matrix) But basically, if you're aiming for below Q4, and you're running cuBLAS (Nvidia) or rocBLAS (AMD), you should look towards the I-quants. These are in format IQX_X, like IQ3_M. These are newer and offer better performance for their size. These I-quants can also be used on CPU and Apple Metal, but will be slower than their K-quant equivalent, so speed vs performance is a tradeoff you'll have to decide. The I-quants are *not* compatible with Vulcan, which is also AMD, so if you have an AMD card double check if you're using the rocBLAS build or the Vulcan build. At the time of writing this, LM Studio has a preview with ROCm support, and other inference engines have specific builds for ROCm.

axssel/christiane_endler

axssel

Entry not found

TatevK/fintuningLLM

TatevK

Entry not found

axssel/raven_chileno

axssel

Entry not found

Akshay203/ak_lora_model_appointment

Akshay203

transformers

--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl base_model: unsloth/llama-3-8b-bnb-4bit --- # Uploaded model - **Developed by:** Akshay203 - **License:** apache-2.0 - **Finetuned from model :** unsloth/llama-3-8b-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

tansw/mistral-instruct-reddit

tansw

peft

--- license: apache-2.0 library_name: peft tags: - generated_from_trainer base_model: mistralai/Mistral-7B-Instruct-v0.2 model-index: - name: mistral-instruct-reddit 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-instruct-reddit This model is a fine-tuned version of [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) on an unknown 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: 0.0002 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 2 - training_steps: 100 - mixed_precision_training: Native AMP ### Training results ### Framework versions - PEFT 0.11.1 - Transformers 4.41.2 - Pytorch 2.3.1+cu121 - Datasets 2.20.0 - Tokenizers 0.19.1

axssel/marcela_cubillos

axssel

Entry not found

anon11112/jenna

anon11112

Entry not found

moschouChry/chronos-t5-finetuned_tiny_1-Patient0-fine-tuned_20240616_175107

moschouChry

transformers

text2text-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]

anon11112/realistic

anon11112

Entry not found

aitorrent/dolphin-2.9.2-qwen2-72b-gguf

aitorrent

text-generation

--- quantized_by: bartowski pipeline_tag: text-generation tags: - torrent --- [](https://ko-fi.com/U7U2ZEFWU) ## Which file should I choose? A great write up with charts showing various performances is provided by Artefact2 [here](https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9) The first thing to figure out is how big a model you can run. To do this, you'll need to figure out how much RAM and/or VRAM you have. If you want your model running as FAST as possible, you'll want to fit the whole thing on your GPU's VRAM. Aim for a quant with a file size 1-2GB smaller than your GPU's total VRAM. If you want the absolute maximum quality, add both your system RAM and your GPU's VRAM together, then similarly grab a quant with a file size 1-2GB Smaller than that total. Next, you'll need to decide if you want to use an 'I-quant' or a 'K-quant'. If you don't want to think too much, grab one of the K-quants. These are in format 'QX_K_X', like Q5_K_M. If you want to get more into the weeds, you can check out this extremely useful feature chart: [llama.cpp feature matrix](https://github.com/ggerganov/llama.cpp/wiki/Feature-matrix) But basically, if you're aiming for below Q4, and you're running cuBLAS (Nvidia) or rocBLAS (AMD), you should look towards the I-quants. These are in format IQX_X, like IQ3_M. These are newer and offer better performance for their size. These I-quants can also be used on CPU and Apple Metal, but will be slower than their K-quant equivalent, so speed vs performance is a tradeoff you'll have to decide. The I-quants are *not* compatible with Vulcan, which is also AMD, so if you have an AMD card double check if you're using the rocBLAS build or the Vulcan build. At the time of writing this, LM Studio has a preview with ROCm support, and other inference engines have specific builds for ROCm.

KeroroK66/SubaruOozora

KeroroK66

--- license: openrail ---

anon11112/sexyattire

anon11112

Entry not found

moschouChry/chronos-t5-finetuned_tiny_1-Patient0-fine-tuned_20240616_175503

moschouChry

Entry not found

whizzzzkid/G_59000

whizzzzkid

Entry not found

whizzzzkid/G_58000

whizzzzkid

Entry not found

moschouChry/chronos-t5-finetuned_tiny_1-Patient0-fine-tuned_20240616_175811

moschouChry

transformers

text2text-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]

RaNgO11/text-to-image

RaNgO11

--- language: - en ---

MrezaPRZ/codellama_database_learning_synthetic_data_bird_dev_set_with_knowledge

MrezaPRZ

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]

kim512/Llama-3-70b-Arimas-story-RP-V1.6-3.0bpw-h6-exl2

kim512

transformers

text-generation

--- base_model: [] library_name: transformers tags: - mergekit - merge - llama 3 - 70b - arimas - story - roleplay - rp --- # EXL2 quants of [ryzen88/Llama-3-70b-Arimas-story-RP-V1.6](https://huggingface.co/ryzen88/Llama-3-70b-Arimas-story-RP-V1.6) [3.00 bits per weight](https://huggingface.co/kim512/Llama-3-70b-Arimas-story-RP-V1.6-3.0bpw-h6-exl2) [3.50 bits per weight](https://huggingface.co/kim512/Llama-3-70b-Arimas-story-RP-V1.6-3.5bpw-h6-exl2) [4.00 bits per weight](https://huggingface.co/kim512/Llama-3-70b-Arimas-story-RP-V1.6-4.0bpw-h6-exl2) [4.50 bits per weight](https://huggingface.co/kim512/Llama-3-70b-Arimas-story-RP-V1.6-4.5bpw-h6-exl2) [6.00 bits per weight](https://huggingface.co/kim512/Llama-3-70b-Arimas-story-RP-V1.6-6.0bpw-h6-exl2) [8.00 bits per weight](https://huggingface.co/kim512/Llama-3-70b-Arimas-story-RP-V1.6-8.0bpw-h8-exl2) Created using the defaults from exllamav2 1.4.0 convert.py 3.0bpw to 6.0bpw head bits = 6 8.0bpw head bits = 8 length = 8192 dataset rows = 200 measurement rows = 32 measurement length = 8192 # model Llama-3-70b-Arimas-story-RP-V1.6 This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). ## Merge Details I Greatly expanded the amount of models used in this merge, experimented a lot with different idea's. This version feels a lot more convincing than V1.5 Hopefully the long context window will also remain strong after Quants. Because of the many merges switched back from BFloat to Float. Tried breadcrums without the Ties, that went very poorly. ### Merge Method This model was merged using the breadcrumbs_ties merge method using I:\Llama-3-70B-Instruct-Gradient-262k as a base. ### Models Merged The following models were included in the merge: * \Smaug-Llama-3-70B-Instruct * \Meta-LLama-3-Cat-Smaug-LLama-70b * \Meta-LLama-3-Cat-A-LLama-70b * \Llama-3-70B-Synthia-v3.5 * \Llama-3-70B-Instruct-Gradient-524k * \Llama-3-70B-Instruct-Gradient-262k * \Tess-2.0-Llama-3-70B-v0.2 * \Llama-3-Lumimaid-70B-v0.1-alt ### Configuration The following YAML configuration was used to produce this model: ```yaml models: - model: \Llama-3-70B-Instruct-Gradient-262k parameters: weight: 0.25 density: 0.90 gamma: 0.01 - model: \Meta-LLama-3-Cat-Smaug-LLama-70b parameters: weight: 0.28 density: 0.90 gamma: 0.01 - model: \Llama-3-Lumimaid-70B-v0.1-alt parameters: weight: 0.15 density: 0.90 gamma: 0.01 - model: \Tess-2.0-Llama-3-70B-v0.2 parameters: weight: 0.06 density: 0.90 gamma: 0.01 - model: \Smaug-Llama-3-70B-Instruct parameters: weight: 0.04 density: 0.90 gamma: 0.01 - model: \Llama-3-70B-Synthia-v3.5 parameters: weight: 0.05 density: 0.90 gamma: 0.01 - model: \Llama-3-70B-Instruct-Gradient-524k parameters: weight: 0.03 density: 0.90 gamma: 0.01 - model: \Meta-LLama-3-Cat-A-LLama-70b parameters: weight: 0.14 density: 0.90 gamma: 0.01 merge_method: breadcrumbs_ties base_model: I:\Llama-3-70B-Instruct-Gradient-262k dtype: float16 ```

kim512/Llama-3-70b-Arimas-story-RP-V1.6-3.5bpw-h6-exl2

kim512

transformers

text-generation

--- base_model: [] library_name: transformers tags: - mergekit - merge - llama 3 - 70b - arimas - story - roleplay - rp --- # EXL2 quants of [ryzen88/Llama-3-70b-Arimas-story-RP-V1.6](https://huggingface.co/ryzen88/Llama-3-70b-Arimas-story-RP-V1.6) [3.00 bits per weight](https://huggingface.co/kim512/Llama-3-70b-Arimas-story-RP-V1.6-3.0bpw-h6-exl2) [3.50 bits per weight](https://huggingface.co/kim512/Llama-3-70b-Arimas-story-RP-V1.6-3.5bpw-h6-exl2) [4.00 bits per weight](https://huggingface.co/kim512/Llama-3-70b-Arimas-story-RP-V1.6-4.0bpw-h6-exl2) [4.50 bits per weight](https://huggingface.co/kim512/Llama-3-70b-Arimas-story-RP-V1.6-4.5bpw-h6-exl2) [6.00 bits per weight](https://huggingface.co/kim512/Llama-3-70b-Arimas-story-RP-V1.6-6.0bpw-h6-exl2) [8.00 bits per weight](https://huggingface.co/kim512/Llama-3-70b-Arimas-story-RP-V1.6-8.0bpw-h8-exl2) Created using the defaults from exllamav2 1.4.0 convert.py 3.0bpw to 6.0bpw head bits = 6 8.0bpw head bits = 8 length = 8192 dataset rows = 200 measurement rows = 32 measurement length = 8192 # model Llama-3-70b-Arimas-story-RP-V1.6 This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). ## Merge Details I Greatly expanded the amount of models used in this merge, experimented a lot with different idea's. This version feels a lot more convincing than V1.5 Hopefully the long context window will also remain strong after Quants. Because of the many merges switched back from BFloat to Float. Tried breadcrums without the Ties, that went very poorly. ### Merge Method This model was merged using the breadcrumbs_ties merge method using I:\Llama-3-70B-Instruct-Gradient-262k as a base. ### Models Merged The following models were included in the merge: * \Smaug-Llama-3-70B-Instruct * \Meta-LLama-3-Cat-Smaug-LLama-70b * \Meta-LLama-3-Cat-A-LLama-70b * \Llama-3-70B-Synthia-v3.5 * \Llama-3-70B-Instruct-Gradient-524k * \Llama-3-70B-Instruct-Gradient-262k * \Tess-2.0-Llama-3-70B-v0.2 * \Llama-3-Lumimaid-70B-v0.1-alt ### Configuration The following YAML configuration was used to produce this model: ```yaml models: - model: \Llama-3-70B-Instruct-Gradient-262k parameters: weight: 0.25 density: 0.90 gamma: 0.01 - model: \Meta-LLama-3-Cat-Smaug-LLama-70b parameters: weight: 0.28 density: 0.90 gamma: 0.01 - model: \Llama-3-Lumimaid-70B-v0.1-alt parameters: weight: 0.15 density: 0.90 gamma: 0.01 - model: \Tess-2.0-Llama-3-70B-v0.2 parameters: weight: 0.06 density: 0.90 gamma: 0.01 - model: \Smaug-Llama-3-70B-Instruct parameters: weight: 0.04 density: 0.90 gamma: 0.01 - model: \Llama-3-70B-Synthia-v3.5 parameters: weight: 0.05 density: 0.90 gamma: 0.01 - model: \Llama-3-70B-Instruct-Gradient-524k parameters: weight: 0.03 density: 0.90 gamma: 0.01 - model: \Meta-LLama-3-Cat-A-LLama-70b parameters: weight: 0.14 density: 0.90 gamma: 0.01 merge_method: breadcrumbs_ties base_model: I:\Llama-3-70B-Instruct-Gradient-262k dtype: float16 ```

kim512/Llama-3-70b-Arimas-story-RP-V1.6-4.0bpw-h6-exl2

kim512

transformers

text-generation

--- base_model: [] library_name: transformers tags: - mergekit - merge - llama 3 - 70b - arimas - story - roleplay - rp --- # EXL2 quants of [ryzen88/Llama-3-70b-Arimas-story-RP-V1.6](https://huggingface.co/ryzen88/Llama-3-70b-Arimas-story-RP-V1.6) [3.00 bits per weight](https://huggingface.co/kim512/Llama-3-70b-Arimas-story-RP-V1.6-3.0bpw-h6-exl2) [3.50 bits per weight](https://huggingface.co/kim512/Llama-3-70b-Arimas-story-RP-V1.6-3.5bpw-h6-exl2) [4.00 bits per weight](https://huggingface.co/kim512/Llama-3-70b-Arimas-story-RP-V1.6-4.0bpw-h6-exl2) [4.50 bits per weight](https://huggingface.co/kim512/Llama-3-70b-Arimas-story-RP-V1.6-4.5bpw-h6-exl2) [6.00 bits per weight](https://huggingface.co/kim512/Llama-3-70b-Arimas-story-RP-V1.6-6.0bpw-h6-exl2) [8.00 bits per weight](https://huggingface.co/kim512/Llama-3-70b-Arimas-story-RP-V1.6-8.0bpw-h8-exl2) Created using the defaults from exllamav2 1.4.0 convert.py 3.0bpw to 6.0bpw head bits = 6 8.0bpw head bits = 8 length = 8192 dataset rows = 200 measurement rows = 32 measurement length = 8192 # model Llama-3-70b-Arimas-story-RP-V1.6 This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). ## Merge Details I Greatly expanded the amount of models used in this merge, experimented a lot with different idea's. This version feels a lot more convincing than V1.5 Hopefully the long context window will also remain strong after Quants. Because of the many merges switched back from BFloat to Float. Tried breadcrums without the Ties, that went very poorly. ### Merge Method This model was merged using the breadcrumbs_ties merge method using I:\Llama-3-70B-Instruct-Gradient-262k as a base. ### Models Merged The following models were included in the merge: * \Smaug-Llama-3-70B-Instruct * \Meta-LLama-3-Cat-Smaug-LLama-70b * \Meta-LLama-3-Cat-A-LLama-70b * \Llama-3-70B-Synthia-v3.5 * \Llama-3-70B-Instruct-Gradient-524k * \Llama-3-70B-Instruct-Gradient-262k * \Tess-2.0-Llama-3-70B-v0.2 * \Llama-3-Lumimaid-70B-v0.1-alt ### Configuration The following YAML configuration was used to produce this model: ```yaml models: - model: \Llama-3-70B-Instruct-Gradient-262k parameters: weight: 0.25 density: 0.90 gamma: 0.01 - model: \Meta-LLama-3-Cat-Smaug-LLama-70b parameters: weight: 0.28 density: 0.90 gamma: 0.01 - model: \Llama-3-Lumimaid-70B-v0.1-alt parameters: weight: 0.15 density: 0.90 gamma: 0.01 - model: \Tess-2.0-Llama-3-70B-v0.2 parameters: weight: 0.06 density: 0.90 gamma: 0.01 - model: \Smaug-Llama-3-70B-Instruct parameters: weight: 0.04 density: 0.90 gamma: 0.01 - model: \Llama-3-70B-Synthia-v3.5 parameters: weight: 0.05 density: 0.90 gamma: 0.01 - model: \Llama-3-70B-Instruct-Gradient-524k parameters: weight: 0.03 density: 0.90 gamma: 0.01 - model: \Meta-LLama-3-Cat-A-LLama-70b parameters: weight: 0.14 density: 0.90 gamma: 0.01 merge_method: breadcrumbs_ties base_model: I:\Llama-3-70B-Instruct-Gradient-262k dtype: float16 ```

kim512/Llama-3-70b-Arimas-story-RP-V1.6-4.5bpw-h6-exl2

kim512

transformers

text-generation

--- base_model: [] library_name: transformers tags: - mergekit - merge - llama 3 - 70b - arimas - story - roleplay - rp --- # EXL2 quants of [ryzen88/Llama-3-70b-Arimas-story-RP-V1.6](https://huggingface.co/ryzen88/Llama-3-70b-Arimas-story-RP-V1.6) [3.00 bits per weight](https://huggingface.co/kim512/Llama-3-70b-Arimas-story-RP-V1.6-3.0bpw-h6-exl2) [3.50 bits per weight](https://huggingface.co/kim512/Llama-3-70b-Arimas-story-RP-V1.6-3.5bpw-h6-exl2) [4.00 bits per weight](https://huggingface.co/kim512/Llama-3-70b-Arimas-story-RP-V1.6-4.0bpw-h6-exl2) [4.50 bits per weight](https://huggingface.co/kim512/Llama-3-70b-Arimas-story-RP-V1.6-4.5bpw-h6-exl2) [6.00 bits per weight](https://huggingface.co/kim512/Llama-3-70b-Arimas-story-RP-V1.6-6.0bpw-h6-exl2) [8.00 bits per weight](https://huggingface.co/kim512/Llama-3-70b-Arimas-story-RP-V1.6-8.0bpw-h8-exl2) Created using the defaults from exllamav2 1.4.0 convert.py 3.0bpw to 6.0bpw head bits = 6 8.0bpw head bits = 8 length = 8192 dataset rows = 200 measurement rows = 32 measurement length = 8192 # model Llama-3-70b-Arimas-story-RP-V1.6 This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). ## Merge Details I Greatly expanded the amount of models used in this merge, experimented a lot with different idea's. This version feels a lot more convincing than V1.5 Hopefully the long context window will also remain strong after Quants. Because of the many merges switched back from BFloat to Float. Tried breadcrums without the Ties, that went very poorly. ### Merge Method This model was merged using the breadcrumbs_ties merge method using I:\Llama-3-70B-Instruct-Gradient-262k as a base. ### Models Merged The following models were included in the merge: * \Smaug-Llama-3-70B-Instruct * \Meta-LLama-3-Cat-Smaug-LLama-70b * \Meta-LLama-3-Cat-A-LLama-70b * \Llama-3-70B-Synthia-v3.5 * \Llama-3-70B-Instruct-Gradient-524k * \Llama-3-70B-Instruct-Gradient-262k * \Tess-2.0-Llama-3-70B-v0.2 * \Llama-3-Lumimaid-70B-v0.1-alt ### Configuration The following YAML configuration was used to produce this model: ```yaml models: - model: \Llama-3-70B-Instruct-Gradient-262k parameters: weight: 0.25 density: 0.90 gamma: 0.01 - model: \Meta-LLama-3-Cat-Smaug-LLama-70b parameters: weight: 0.28 density: 0.90 gamma: 0.01 - model: \Llama-3-Lumimaid-70B-v0.1-alt parameters: weight: 0.15 density: 0.90 gamma: 0.01 - model: \Tess-2.0-Llama-3-70B-v0.2 parameters: weight: 0.06 density: 0.90 gamma: 0.01 - model: \Smaug-Llama-3-70B-Instruct parameters: weight: 0.04 density: 0.90 gamma: 0.01 - model: \Llama-3-70B-Synthia-v3.5 parameters: weight: 0.05 density: 0.90 gamma: 0.01 - model: \Llama-3-70B-Instruct-Gradient-524k parameters: weight: 0.03 density: 0.90 gamma: 0.01 - model: \Meta-LLama-3-Cat-A-LLama-70b parameters: weight: 0.14 density: 0.90 gamma: 0.01 merge_method: breadcrumbs_ties base_model: I:\Llama-3-70B-Instruct-Gradient-262k dtype: float16 ```

kim512/Llama-3-70b-Arimas-story-RP-V1.6-6.0bpw-h6-exl2

kim512

transformers

--- base_model: [] library_name: transformers tags: - mergekit - merge - llama 3 - 70b - arimas - story - roleplay - rp --- # EXL2 quants of [ryzen88/Llama-3-70b-Arimas-story-RP-V1.6](https://huggingface.co/ryzen88/Llama-3-70b-Arimas-story-RP-V1.6) [3.00 bits per weight](https://huggingface.co/kim512/Llama-3-70b-Arimas-story-RP-V1.6-3.0bpw-h6-exl2) [3.50 bits per weight](https://huggingface.co/kim512/Llama-3-70b-Arimas-story-RP-V1.6-3.5bpw-h6-exl2) [4.00 bits per weight](https://huggingface.co/kim512/Llama-3-70b-Arimas-story-RP-V1.6-4.0bpw-h6-exl2) [4.50 bits per weight](https://huggingface.co/kim512/Llama-3-70b-Arimas-story-RP-V1.6-4.5bpw-h6-exl2) [6.00 bits per weight](https://huggingface.co/kim512/Llama-3-70b-Arimas-story-RP-V1.6-6.0bpw-h6-exl2) [8.00 bits per weight](https://huggingface.co/kim512/Llama-3-70b-Arimas-story-RP-V1.6-8.0bpw-h8-exl2) Created using the defaults from exllamav2 1.4.0 convert.py 3.0bpw to 6.0bpw head bits = 6 8.0bpw head bits = 8 length = 8192 dataset rows = 200 measurement rows = 32 measurement length = 8192 # model Llama-3-70b-Arimas-story-RP-V1.6 This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). ## Merge Details I Greatly expanded the amount of models used in this merge, experimented a lot with different idea's. This version feels a lot more convincing than V1.5 Hopefully the long context window will also remain strong after Quants. Because of the many merges switched back from BFloat to Float. Tried breadcrums without the Ties, that went very poorly. ### Merge Method This model was merged using the breadcrumbs_ties merge method using I:\Llama-3-70B-Instruct-Gradient-262k as a base. ### Models Merged The following models were included in the merge: * \Smaug-Llama-3-70B-Instruct * \Meta-LLama-3-Cat-Smaug-LLama-70b * \Meta-LLama-3-Cat-A-LLama-70b * \Llama-3-70B-Synthia-v3.5 * \Llama-3-70B-Instruct-Gradient-524k * \Llama-3-70B-Instruct-Gradient-262k * \Tess-2.0-Llama-3-70B-v0.2 * \Llama-3-Lumimaid-70B-v0.1-alt ### Configuration The following YAML configuration was used to produce this model: ```yaml models: - model: \Llama-3-70B-Instruct-Gradient-262k parameters: weight: 0.25 density: 0.90 gamma: 0.01 - model: \Meta-LLama-3-Cat-Smaug-LLama-70b parameters: weight: 0.28 density: 0.90 gamma: 0.01 - model: \Llama-3-Lumimaid-70B-v0.1-alt parameters: weight: 0.15 density: 0.90 gamma: 0.01 - model: \Tess-2.0-Llama-3-70B-v0.2 parameters: weight: 0.06 density: 0.90 gamma: 0.01 - model: \Smaug-Llama-3-70B-Instruct parameters: weight: 0.04 density: 0.90 gamma: 0.01 - model: \Llama-3-70B-Synthia-v3.5 parameters: weight: 0.05 density: 0.90 gamma: 0.01 - model: \Llama-3-70B-Instruct-Gradient-524k parameters: weight: 0.03 density: 0.90 gamma: 0.01 - model: \Meta-LLama-3-Cat-A-LLama-70b parameters: weight: 0.14 density: 0.90 gamma: 0.01 merge_method: breadcrumbs_ties base_model: I:\Llama-3-70B-Instruct-Gradient-262k dtype: float16 ```

kim512/Llama-3-70b-Arimas-story-RP-V1.6-8.0bpw-h8-exl2

kim512

transformers

text-generation

--- base_model: [] library_name: transformers tags: - mergekit - merge - llama 3 - 70b - arimas - story - roleplay - rp --- # EXL2 quants of [ryzen88/Llama-3-70b-Arimas-story-RP-V1.6](https://huggingface.co/ryzen88/Llama-3-70b-Arimas-story-RP-V1.6) [3.00 bits per weight](https://huggingface.co/kim512/Llama-3-70b-Arimas-story-RP-V1.6-3.0bpw-h6-exl2) [3.50 bits per weight](https://huggingface.co/kim512/Llama-3-70b-Arimas-story-RP-V1.6-3.5bpw-h6-exl2) [4.00 bits per weight](https://huggingface.co/kim512/Llama-3-70b-Arimas-story-RP-V1.6-4.0bpw-h6-exl2) [4.50 bits per weight](https://huggingface.co/kim512/Llama-3-70b-Arimas-story-RP-V1.6-4.5bpw-h6-exl2) [6.00 bits per weight](https://huggingface.co/kim512/Llama-3-70b-Arimas-story-RP-V1.6-6.0bpw-h6-exl2) [8.00 bits per weight](https://huggingface.co/kim512/Llama-3-70b-Arimas-story-RP-V1.6-8.0bpw-h8-exl2) Created using the defaults from exllamav2 1.4.0 convert.py 3.0bpw to 6.0bpw head bits = 6 8.0bpw head bits = 8 length = 8192 dataset rows = 200 measurement rows = 32 measurement length = 8192 # model Llama-3-70b-Arimas-story-RP-V1.6 This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). ## Merge Details I Greatly expanded the amount of models used in this merge, experimented a lot with different idea's. This version feels a lot more convincing than V1.5 Hopefully the long context window will also remain strong after Quants. Because of the many merges switched back from BFloat to Float. Tried breadcrums without the Ties, that went very poorly. ### Merge Method This model was merged using the breadcrumbs_ties merge method using I:\Llama-3-70B-Instruct-Gradient-262k as a base. ### Models Merged The following models were included in the merge: * \Smaug-Llama-3-70B-Instruct * \Meta-LLama-3-Cat-Smaug-LLama-70b * \Meta-LLama-3-Cat-A-LLama-70b * \Llama-3-70B-Synthia-v3.5 * \Llama-3-70B-Instruct-Gradient-524k * \Llama-3-70B-Instruct-Gradient-262k * \Tess-2.0-Llama-3-70B-v0.2 * \Llama-3-Lumimaid-70B-v0.1-alt ### Configuration The following YAML configuration was used to produce this model: ```yaml models: - model: \Llama-3-70B-Instruct-Gradient-262k parameters: weight: 0.25 density: 0.90 gamma: 0.01 - model: \Meta-LLama-3-Cat-Smaug-LLama-70b parameters: weight: 0.28 density: 0.90 gamma: 0.01 - model: \Llama-3-Lumimaid-70B-v0.1-alt parameters: weight: 0.15 density: 0.90 gamma: 0.01 - model: \Tess-2.0-Llama-3-70B-v0.2 parameters: weight: 0.06 density: 0.90 gamma: 0.01 - model: \Smaug-Llama-3-70B-Instruct parameters: weight: 0.04 density: 0.90 gamma: 0.01 - model: \Llama-3-70B-Synthia-v3.5 parameters: weight: 0.05 density: 0.90 gamma: 0.01 - model: \Llama-3-70B-Instruct-Gradient-524k parameters: weight: 0.03 density: 0.90 gamma: 0.01 - model: \Meta-LLama-3-Cat-A-LLama-70b parameters: weight: 0.14 density: 0.90 gamma: 0.01 merge_method: breadcrumbs_ties base_model: I:\Llama-3-70B-Instruct-Gradient-262k dtype: float16 ```

moschouChry/chronos-t5-finetuned_tiny_1-Patient0-fine-tuned_20240616_180200

moschouChry

Entry not found

HareRamaCh/results

HareRamaCh

Entry not found

Ecommarocchino/Jaw

Ecommarocchino

Entry not found

nathanhunt/w2v-bert-2.0-mongolian-colab-CV16.0

nathanhunt

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]

Dhahlan2000/Simple_Translation-model-for-GPT-v16

Dhahlan2000

transformers

text2text-generation

Entry not found

Yuki20/llama3_8b_instruct_aci_5e

Yuki20

transformers

--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl base_model: unsloth/llama-3-8b-Instruct-bnb-4bit --- # Uploaded model - **Developed by:** Yuki20 - **License:** apache-2.0 - **Finetuned from model :** unsloth/llama-3-8b-Instruct-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

aaalby/hyein

aaalby

--- license: openrail ---

xjw1001001/lora_vit_code

xjw1001001

Entry not found

ChengSyuen/llama-3-8b-chat-finetuned

ChengSyuen

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]

aitorrent/dolphin-2.9.2-Phi-3-Medium-abliterated-GGUF-torrent

aitorrent

transformers

--- base_model: cognitivecomputations/dolphin-2.9.2-Phi-3-Medium-abliterated datasets: - cognitivecomputations/Dolphin-2.9.2 - teknium/OpenHermes-2.5 - m-a-p/CodeFeedback-Filtered-Instruction - cognitivecomputations/dolphin-coder - cognitivecomputations/samantha-data - microsoft/orca-math-word-problems-200k - internlm/Agent-FLAN - cognitivecomputations/SystemChat-2.0 language: - en library_name: transformers license: mit quantized_by: mradermacher tags: - torrent --- [](https://ko-fi.com/U7U2ZEFWU) ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/cognitivecomputations/dolphin-2.9.2-Phi-3-Medium-abliterated <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/dolphin-2.9.2-Phi-3-Medium-abliterated-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/dolphin-2.9.2-Phi-3-Medium-abliterated-GGUF/resolve/main/dolphin-2.9.2-Phi-3-Medium-abliterated.Q2_K.gguf) | Q2_K | 5.3 | | | [GGUF](https://huggingface.co/mradermacher/dolphin-2.9.2-Phi-3-Medium-abliterated-GGUF/resolve/main/dolphin-2.9.2-Phi-3-Medium-abliterated.IQ3_XS.gguf) | IQ3_XS | 5.9 | | | [GGUF](https://huggingface.co/mradermacher/dolphin-2.9.2-Phi-3-Medium-abliterated-GGUF/resolve/main/dolphin-2.9.2-Phi-3-Medium-abliterated.Q3_K_S.gguf) | Q3_K_S | 6.2 | | | [GGUF](https://huggingface.co/mradermacher/dolphin-2.9.2-Phi-3-Medium-abliterated-GGUF/resolve/main/dolphin-2.9.2-Phi-3-Medium-abliterated.IQ3_S.gguf) | IQ3_S | 6.2 | beats Q3_K* | | [GGUF](https://huggingface.co/mradermacher/dolphin-2.9.2-Phi-3-Medium-abliterated-GGUF/resolve/main/dolphin-2.9.2-Phi-3-Medium-abliterated.IQ3_M.gguf) | IQ3_M | 6.4 | | | [GGUF](https://huggingface.co/mradermacher/dolphin-2.9.2-Phi-3-Medium-abliterated-GGUF/resolve/main/dolphin-2.9.2-Phi-3-Medium-abliterated.Q3_K_M.gguf) | Q3_K_M | 6.9 | lower quality | | [GGUF](https://huggingface.co/mradermacher/dolphin-2.9.2-Phi-3-Medium-abliterated-GGUF/resolve/main/dolphin-2.9.2-Phi-3-Medium-abliterated.Q3_K_L.gguf) | Q3_K_L | 7.4 | | | [GGUF](https://huggingface.co/mradermacher/dolphin-2.9.2-Phi-3-Medium-abliterated-GGUF/resolve/main/dolphin-2.9.2-Phi-3-Medium-abliterated.IQ4_XS.gguf) | IQ4_XS | 7.7 | | | [GGUF](https://huggingface.co/mradermacher/dolphin-2.9.2-Phi-3-Medium-abliterated-GGUF/resolve/main/dolphin-2.9.2-Phi-3-Medium-abliterated.Q4_K_S.gguf) | Q4_K_S | 8.1 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/dolphin-2.9.2-Phi-3-Medium-abliterated-GGUF/resolve/main/dolphin-2.9.2-Phi-3-Medium-abliterated.Q4_K_M.gguf) | Q4_K_M | 8.5 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/dolphin-2.9.2-Phi-3-Medium-abliterated-GGUF/resolve/main/dolphin-2.9.2-Phi-3-Medium-abliterated.Q5_K_S.gguf) | Q5_K_S | 9.7 | | | [GGUF](https://huggingface.co/mradermacher/dolphin-2.9.2-Phi-3-Medium-abliterated-GGUF/resolve/main/dolphin-2.9.2-Phi-3-Medium-abliterated.Q5_K_M.gguf) | Q5_K_M | 10.0 | | | [GGUF](https://huggingface.co/mradermacher/dolphin-2.9.2-Phi-3-Medium-abliterated-GGUF/resolve/main/dolphin-2.9.2-Phi-3-Medium-abliterated.Q6_K.gguf) | Q6_K | 11.6 | very good quality | | [GGUF](https://huggingface.co/mradermacher/dolphin-2.9.2-Phi-3-Medium-abliterated-GGUF/resolve/main/dolphin-2.9.2-Phi-3-Medium-abliterated.Q8_0.gguf) | Q8_0 | 14.9 | 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

MG31/v8_11_safetensors

MG31

object-detection

--- pipeline_tag: object-detection ---

CLASS-MATE/Llama3-8b-dataset2

CLASS-MATE

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]

ashu2000/Llama-2-7b-chat-finetune

ashu2000

transformers

text-generation

--- license: apache-2.0 ---

nope13456/egro

nope13456

--- license: mit ---

africa3939/sd3-medium

africa3939

Entry not found

subhasishtech88/lama_fine_tune_lora_model_1

subhasishtech88

transformers

--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl base_model: unsloth/llama-3-8b-bnb-4bit --- # Uploaded model - **Developed by:** subhasishtech88 - **License:** apache-2.0 - **Finetuned from model :** unsloth/llama-3-8b-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

Xiaolihai/BioGPT-Large_MeDistill_28_BioGPT-Large_ep10

Xiaolihai

Entry not found

V3N0M/Qwen-Jenna-v01

V3N0M

transformers

--- base_model: unsloth/qwen2-0.5b-bnb-4bit language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - qwen2 - trl --- # Uploaded model - **Developed by:** V3N0M - **License:** apache-2.0 - **Finetuned from model :** unsloth/qwen2-0.5b-bnb-4bit This qwen2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

SogoChang/distilbert-base-uncased-finetuned-imdb

SogoChang

Entry not found

CarelS/gpt2-wikitext2

CarelS

transformers

text-generation

Entry not found

ehristoforu/dpo-spo-loras

ehristoforu

--- license: creativeml-openrail-m ---

SilvioLima/absa_treinamento_0

SilvioLima

transformers

text-generation

# Model Card para ABSA_AOTE_distilGPT2 ## Dados Gerais - **Nome:** Modelo para Aspect-opinion Triplet Extraction (AOTE) baseado em distilGPT2 - **Tipo:** decoder-only - **Licença:** [Licença do modelo] - **Modelo base:** distilGPT2 ## Resumo Modelo distilGPT2 ajustado para a tarefa ABSA/AOTE com os datasets SemEval + Amazon. Para treinamento foi utilizado PyTorch. Parâmetros: | Parâmetro | Valor | Descrição | | ------------- | ------------- | ------------- | |model | distilGPT2 | Nome do modelo base | |train_size | None | Número de amostras para treinameto | |val_size | None | Número de amostras para validação | |test_size | None | Número de amostras para teste | |max_input_length | 128 | Quantidade de tokens máxima na entrada | |max_output_length | 128 | Quantidade de tokens máxima na saída | |batch_size | 16 | Quantidade de amostras no batch | |n_epochs | 10 | Número máximo de épocas de treinamento | |lr | 1,00E-03 | Taxa de aprendizado | |use_weights | FALSO | Usar ou não pesos personalizados para cada polaridade | |use_paraphrase | FALSO | Usar ou não a saída no formato de paráfrase | |use_prompt | FALSO | Usar uma instrução junto com o review na entrada | |one_shot | FALSO | Fornecer ou não um exemplo junto ao prompt | |early_stop | 3 | Paciência do early stop (se a perda de validação não abaixar por três épocas o treinamento encerra) | ## Utilização Pretendida O modelo foi ajustado considerando o formato de entrada e saída descrito abaixo, sendo assim recomenda-se que ao se carregar, fazer inferências com dados seguindo o mesmo formato. Entrada: The pizza was good, but the waiter was lazy. Saída: [('pizza', 'good', 'POS'), ('waiter', 'lazy', 'NEG')] ## Idiomas Inglês ## Dados de Treinamento Os dados são uma composição dos datasets ASTE de [1] e DM-ASTE [2], que seguem o mesmo formato de dados descrito acima. [1] XU, Lu et al. Position-aware tagging for aspect sentiment triplet extraction. arXiv preprint arXiv:2010.02609, 2020. [2] XU, Ting et al. Measuring Your ASTE Models in The Wild: A Diversified Multi-domain Dataset For Aspect Sentiment Triplet Extraction. arXiv preprint arXiv:2305.17448, 2023.

HareRamaCh/model-finetuned

HareRamaCh

Entry not found

JamesKim/m2m100-ft3

JamesKim

transformers

text2text-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]

pablovela5620/dsine_kappa

pablovela5620

Entry not found

AiHubber/CatRave990

AiHubber

--- license: openrail ---

sgarcianicito/ubi

sgarcianicito

--- license: openrail ---

BarBossHk/egg

BarBossHk

--- license: afl-3.0 ---

ckazotronsyka/worke

ckazotronsyka

Entry not found

Mohammed-majeed/llama-3-8b-bnb-4bit-Unsloth-chunk-7-0.5-1

Mohammed-majeed

transformers

--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl base_model: unsloth/llama-3-8b-bnb-4bit --- # Uploaded model - **Developed by:** Mohammed-majeed - **License:** apache-2.0 - **Finetuned from model :** unsloth/llama-3-8b-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

audo/Lumina-T2Music

audo

transformers

text-to-audio

--- license: apache-2.0 tags: - text-to-audio - music library_name: transformers --- # Lumina Text-to-Music We will provide our implementation and pretrained models as open source in this repository recently. - Generation Model: Flag-DiT - Text Encoder: [FLAN-T5-Large](https://huggingface.co/google/flan-t5-large) - VAE: Make an Audio 2, finetuned from [Makee an Audio](https://github.com/Text-to-Audio/Make-An-Audio) - Decoder: [Vocoder](https://github.com/NVIDIA/BigVGAN) ## 📰 News - [2024-06-07] 🚀🚀🚀 We release the initial version of `Lumina-T2Music` for text-to-music generation. ## Installation Before installation, ensure that you have a working ``nvcc`` ```bash # The command should work and show the same version number as in our case. (12.1 in our case). nvcc --version ``` On some outdated distros (e.g., CentOS 7), you may also want to check that a late enough version of ``gcc`` is available ```bash # The command should work and show a version of at least 6.0. # If not, consult distro-specific tutorials to obtain a newer version or build manually. gcc --version ``` Downloading Lumina-T2X repo from github: ```bash git clone https://github.com/Alpha-VLLM/Lumina-T2X ``` ### 1. Create a conda environment and install PyTorch Note: You may want to adjust the CUDA version [according to your driver version](https://docs.nvidia.com/deploy/cuda-compatibility/#default-to-minor-version). ```bash conda create -n Lumina_T2X -y conda activate Lumina_T2X conda install python=3.11 pytorch==2.1.0 torchvision==0.16.0 torchaudio==2.1.0 pytorch-cuda=12.1 -c pytorch -c nvidia -y ``` ### 2. Install dependencies >[!Warning] > The environment dependencies for Lumina-T2Music are different from those for Lumina-T2I. Please install the appropriate environment. Installing `Lumina-T2Music` dependencies: ```bash cd .. # If you are in the `lumina_music` directory, execute this line. pip install -e ".[music]" ``` or you can use `requirements.txt` to install the environment. ```bash cd lumina_music # If you are not in the `lumina_music` folder, run this line. pip install -r requirements.txt ``` ### 3. Install ``flash-attn`` ```bash pip install flash-attn --no-build-isolation ``` ### 4. Install [nvidia apex](https://github.com/nvidia/apex) (optional) >[!Warning] > While Apex can improve efficiency, it is *not* a must to make Lumina-T2X work. > > Note that Lumina-T2X works smoothly with either: > + Apex not installed at all; OR > + Apex successfully installed with CUDA and C++ extensions. > > However, it will fail when: > + A Python-only build of Apex is installed. > > If the error `No module named 'fused_layer_norm_cuda'` appears, it typically means you are using a Python-only build of Apex. To resolve this, please run `pip uninstall apex`, and Lumina-T2X should then function correctly. You can clone the repo and install following the official guidelines (note that we expect a full build, i.e., with CUDA and C++ extensions) ```bash pip install ninja git clone https://github.com/NVIDIA/apex cd apex # if pip >= 23.1 (ref: https://pip.pypa.io/en/stable/news/#v23-1) which supports multiple `--config-settings` with the same key... pip install -v --disable-pip-version-check --no-cache-dir --no-build-isolation --config-settings "--build-option=--cpp_ext" --config-settings "--build-option=--cuda_ext" ./ # otherwise pip install -v --disable-pip-version-check --no-cache-dir --no-build-isolation --global-option="--cpp_ext" --global-option="--cuda_ext" ./ ``` ## Inference ### Preparation Prepare the pretrained checkpoints. ⭐⭐ (Recommended) you can use `huggingface-cli` downloading our model: ```bash huggingface-cli download --resume-download Alpha-VLLM/Lumina-T2Music --local-dir /path/to/ckpt ``` or using git for cloning the model you want to use: ```bash git clone https://huggingface.co/Alpha-VLLM/Lumina-T2Music ``` ### Web Demo To host a local gradio demo for interactive inference, run the following command: 1. updated `AutoencoderKL` ckpt path you should update `configs/lumina-text2music.yaml` to set `AutoencoderKL` checkpoint path. Please replace `/path/to/ckpt` with the path where your checkpoints are located (<real_path>). ```diff ... depth: 16 max_len: 1000 first_stage_config: target: models.autoencoder1d.AutoencoderKL params: embed_dim: 20 monitor: val/rec_loss - ckpt_path: /path/to/ckpt/maa2/maa2.ckpt + ckpt_path: <real_path>/maa2/maa2.ckpt ddconfig: double_z: true in_channels: 80 out_ch: 80 ... ``` 2. setting `Lumina-T2Music` and `Vocoder` checkpoint path and run demo Please replace `/path/to/ckpt` with the actual downloaded path. ```bash # `/path/to/ckpt` should be a directory containing `music_generation`, `maa2`, and `bigvnat`. # default python -u demo_music.py \ --ckpt "/path/to/ckpt/music_generation" \ --vocoder_ckpt "/path/to/ckpt/bigvnat" \ --config_path "configs/lumina-text2music.yaml" \ --sample_rate 16000 ``` ## Disclaimer Any organization or individual is prohibited from using any technology mentioned in this paper to generate someone's speech without his/her consent, including but not limited to government leaders, political figures, and celebrities. If you do not comply with this item, you could be in violation of copyright laws.

EmbeddedLLM/llama-2-13b-chat-int4-onnx-directml

EmbeddedLLM

transformers

text-generation

--- license: llama2 language: - en pipeline_tag: text-generation tags: - facebook - meta - llama - llama-2 - ONNX - DirectML - DML - conversational - ONNXRuntime - custom_code --- # Llama-2-13b-chat ONNX models for DirectML This repository hosts the optimized versions of [meta-llama/Llama-2-13b-chat-hf](https://huggingface.co/meta-llama/Llama-2-13b-chat-hf) to accelerate inference with ONNX Runtime for DirectML. ## Usage on Windows (Intel / AMD / Nvidia / Qualcomm) ```powershell conda create -n onnx python=3.10 conda activate onnx winget install -e --id GitHub.GitLFS pip install huggingface-hub[cli] huggingface-cli download EmbeddedLLM/llama-2-13b-chat-int4-onnx-directml --local-dir .\llama-2-13b-chat pip install numpy==1.26.4 Invoke-WebRequest -Uri "https://raw.githubusercontent.com/microsoft/onnxruntime-genai/main/examples/python/phi3-qa.py" -OutFile "phi3-qa.py" pip install onnxruntime-directml pip install --pre onnxruntime-genai-directml conda install conda-forge::vs2015_runtime python phi3-qa.py -m .\llama-2-13b-chat ``` ## What is DirectML DirectML is a high-performance, hardware-accelerated DirectX 12 library for machine learning. DirectML provides GPU acceleration for common machine learning tasks across a broad range of supported hardware and drivers, including all DirectX 12-capable GPUs from vendors such as AMD, Intel, NVIDIA, and Qualcomm.

pookie3000/trump_lora

pookie3000

transformers

--- library_name: transformers tags: - unsloth --- # 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]

richardkelly/Qwen-Qwen1.5-7B-1718554398

richardkelly

peft

--- library_name: peft base_model: Qwen/Qwen1.5-7B --- # 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. 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] ### Framework versions - PEFT 0.11.1

Darkknight12/Pytorch_Mnist_Model

Darkknight12

--- license: mit ---

marcossoaresgg/zhline

marcossoaresgg

--- license: openrail ---

vivym/face-parsing-bisenet

vivym

# Face Parsing BiSeNet [https://github.com/zllrunning/face-parsing.PyTorch](https://github.com/zllrunning/face-parsing.PyTorch)

gwong001/my_awesome_model

gwong001

Entry not found

whizzzzkid/G_80000

whizzzzkid

Entry not found

Wenrui/ML_TTS_Dataset

Wenrui

# ML_TTS_Dataset ## Pipeline 1. 文件重命名 * ML_TTS_Dataset/examples/bash/rename/run_single_split.sh 2. Convert to 16kHZ * bash ML_TTS_Dataset/examples/bash/resample/run_single_dir.sh 指定输出的音频格式和采样率 3. 检查静音区，设置阈值去除带背景声视频 * bash ML_TTS_Dataset/examples/bash/noise_suppression 可跳过第2步，直接到第3步，不需要额外的转换采样率。 4. 多说话人检测，抛弃掉多说话人的音频。 * bash ML_TTS_Dataset/examples/bash/speaker_diarization/run_audio_root.sh 5. ASR(with duraction) * bash ML_TTS_Dataset/examples/bash/asr/run_single_split.sh 6. clips cutting(按照duraction 裁剪为3s-10s片段) 上一步得到asr输出的时间戳，可按照时间戳剪辑音频。这一步会相当占用网络带宽（所有数据都存在网盘上），建议交给飞扬来在本地剪辑。 7. 最终效果见ML_TTS_Dataset/examples/demo.txt

Xiaolihai/BioMistral-7B_MeDistill_28_BioGPT-Large_ep10

Xiaolihai

Entry not found

alru28/trained-sd3-lora

alru28

Entry not found

ElectricIceBird/ppo-Huggy

ElectricIceBird

Entry not found

shalexxxy/my_t5_small_test

shalexxxy

transformers

text2text-generation

Entry not found

KYAGABA/wav2vec2-large-xls-r-300m-rw-1hr-v1

KYAGABA

transformers

automatic-speech-recognition

--- license: apache-2.0 base_model: facebook/wav2vec2-xls-r-300m tags: - generated_from_trainer datasets: - common_voice_5_1 metrics: - wer model-index: - name: wav2vec2-large-xls-r-300m-rw-1hr-v1 results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: common_voice_5_1 type: common_voice_5_1 config: rw split: test args: rw metrics: - name: Wer type: wer value: 0.9068557919621749 --- <!-- 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. --> # wav2vec2-large-xls-r-300m-rw-1hr-v1 This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice_5_1 dataset. It achieves the following results on the evaluation set: - Loss: 1.1842 - Wer: 0.9069 - Cer: 0.2771 ## 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: 0.0001 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 40 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | Cer | |:-------------:|:-------:|:----:|:---------------:|:------:|:------:| | 8.8463 | 5.2632 | 100 | 4.4056 | 1.0 | 1.0 | | 3.2194 | 10.5263 | 200 | 3.1877 | 1.0 | 1.0 | | 2.9338 | 15.7895 | 300 | 2.9724 | 1.0 | 1.0 | | 2.7275 | 21.0526 | 400 | 2.5030 | 1.0 | 0.7623 | | 1.1143 | 26.3158 | 500 | 1.2838 | 0.9378 | 0.3333 | | 0.4144 | 31.5789 | 600 | 1.2007 | 0.9099 | 0.2962 | | 0.2425 | 36.8421 | 700 | 1.1657 | 0.9040 | 0.2815 | ### Framework versions - Transformers 4.41.2 - Pytorch 2.3.0+cu121 - Datasets 2.20.0 - Tokenizers 0.19.1

Fearless-15/Apex

Fearless-15

--- license: other license_name: dev license_link: LICENSE ---

richardkelly/google-gemma-2b-1718555510

richardkelly

peft

--- library_name: peft base_model: google/gemma-2b --- # 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. 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] ### Framework versions - PEFT 0.11.1

utkukose/deneme

utkukose

--- license: mit ---

Bucino/llnn

Bucino

--- license: openrail ---

callmesan/audio-abuse-feature

callmesan

transformers

audio-classification

--- base_model: HariprasathSB/indic-whisper-vulnerable tags: - generated_from_trainer metrics: - accuracy model-index: - name: audio-abuse-feature 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. --> # audio-abuse-feature This model is a fine-tuned version of [HariprasathSB/indic-whisper-vulnerable](https://huggingface.co/HariprasathSB/indic-whisper-vulnerable) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4489 - Accuracy: 0.8814 - Macro Precision: 0.8557 - Macro Recall: 0.8472 - Macro F1-score: 0.8513 ## 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: 16 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.01 - num_epochs: 5 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Macro Precision | Macro Recall | Macro F1-score | |:-------------:|:------:|:----:|:---------------:|:--------:|:---------------:|:------------:|:--------------:| | 0.4633 | 0.4367 | 50 | 0.3753 | 0.8327 | 0.8321 | 0.8314 | 0.8317 | | 0.345 | 0.8734 | 100 | 0.4170 | 0.8241 | 0.8612 | 0.8126 | 0.8150 | | 0.2592 | 1.3100 | 150 | 0.3357 | 0.8512 | 0.8506 | 0.8502 | 0.8504 | | 0.2097 | 1.7467 | 200 | 0.3142 | 0.8758 | 0.8757 | 0.8744 | 0.8749 | | 0.1545 | 2.1834 | 250 | 0.3551 | 0.8721 | 0.8713 | 0.8718 | 0.8715 | | 0.0829 | 2.6201 | 300 | 0.3916 | 0.8795 | 0.8797 | 0.8778 | 0.8786 | | 0.0944 | 3.0568 | 350 | 0.4137 | 0.8721 | 0.8714 | 0.8730 | 0.8718 | | 0.0416 | 3.4934 | 400 | 0.5350 | 0.8659 | 0.8677 | 0.8631 | 0.8646 | | 0.0469 | 3.9301 | 450 | 0.5129 | 0.8733 | 0.8727 | 0.8726 | 0.8727 | | 0.0247 | 4.3668 | 500 | 0.5543 | 0.8708 | 0.8713 | 0.8689 | 0.8698 | | 0.0208 | 4.8035 | 550 | 0.5611 | 0.8696 | 0.8691 | 0.8688 | 0.8689 | ### Framework versions - Transformers 4.41.2 - Pytorch 2.1.2 - Datasets 2.19.2 - Tokenizers 0.19.1

marcosprun/rociomedina

marcosprun

Entry not found

hishamcse/Reinforce-Pixelcopter-PLE-v0

hishamcse

reinforcement-learning

--- tags: - Pixelcopter-PLE-v0 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-Pixelcopter-PLE-v0 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Pixelcopter-PLE-v0 type: Pixelcopter-PLE-v0 metrics: - type: mean_reward value: 97.00 +/- 0.00 name: mean_reward verified: false --- # **Reinforce** Agent playing **Pixelcopter-PLE-v0** This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction

moschouChry/chronos-t5-finetuned_tiny_1-Patient0-fine-tuned_20240616_194355

moschouChry

transformers

text2text-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]

moschouChry/chronos-t5-finetuned_tiny_1-Patient0-fine-tuned_20240616_194735

moschouChry

Entry not found

whizzzzkid/ft_G_0050000

whizzzzkid

Entry not found

whizzzzkid/ft_G_030000

whizzzzkid

Entry not found

Xiaolihai/BioMistral-7B_MeDistill_28_BioMistral-7B_ep10

Xiaolihai

Entry not found

Richdog89/Dog

Richdog89

--- license: artistic-2.0 datasets: - HuggingFaceFW/fineweb language: - ae ---

microzen/Qwen2-1.5b-lora

microzen

transformers

--- library_name: transformers tags: - unsloth --- # 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]

ksridhar/atari_2B_atari_carnival_1111

ksridhar

sample-factory

reinforcement-learning

--- library_name: sample-factory tags: - deep-reinforcement-learning - reinforcement-learning - sample-factory model-index: - name: APPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: atari_carnival type: atari_carnival metrics: - type: mean_reward value: 718.00 +/- 546.29 name: mean_reward verified: false --- A(n) **APPO** model trained on the **atari_carnival** environment. This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory. Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/

scholl99/tinyllama_humanMOD_qlora_v2

scholl99

transformers

--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl base_model: unsloth/tinyllama-bnb-4bit --- # Uploaded model - **Developed by:** scholl99 - **License:** apache-2.0 - **Finetuned from model :** unsloth/tinyllama-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

nqv2291/bloom_560m-sft-open_ner_en

nqv2291

Entry not found

ksridhar/atari_2B_atari_pooyan_1111

ksridhar

sample-factory

reinforcement-learning

--- library_name: sample-factory tags: - deep-reinforcement-learning - reinforcement-learning - sample-factory model-index: - name: APPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: atari_pooyan type: atari_pooyan metrics: - type: mean_reward value: 333.50 +/- 174.87 name: mean_reward verified: false --- A(n) **APPO** model trained on the **atari_pooyan** environment. This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory. Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/

ksridhar/atari_2B_atari_airraid_1111

ksridhar

sample-factory

reinforcement-learning

--- library_name: sample-factory tags: - deep-reinforcement-learning - reinforcement-learning - sample-factory model-index: - name: APPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: atari_airraid type: atari_airraid metrics: - type: mean_reward value: 465.00 +/- 182.76 name: mean_reward verified: false --- A(n) **APPO** model trained on the **atari_airraid** environment. This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory. Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/

danielgi97/stable-diffusion-2-1

danielgi97

Entry not found

ksridhar/atari_2B_atari_journeyescape_1111

ksridhar

sample-factory

reinforcement-learning

--- library_name: sample-factory tags: - deep-reinforcement-learning - reinforcement-learning - sample-factory model-index: - name: APPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: atari_journeyescape type: atari_journeyescape metrics: - type: mean_reward value: -21220.00 +/- 7108.14 name: mean_reward verified: false --- A(n) **APPO** model trained on the **atari_journeyescape** environment. This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory. Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/

kmate97/HeikoGrauel2_TITAN

kmate97

Entry not found

gguille/lista

gguille

--- license: gpl-2.0 ---

wolffenbuetell/MERGERPLUSLORA17

wolffenbuetell

--- license: openrail ---

ckpt/Lumina-Next-SFT

ckpt

text-to-image

--- license: apache-2.0 tags: - text-to-image - safetensors --- # Lumina-Next-SFT The `Lumina-Next-SFT` is a Next-DiT model containing 2B parameters and utilizes [Gemma-2B](https://huggingface.co/google/gemma-2b) as the text encoder, enhanced through high-quality supervised fine-tuning (SFT). Our generative model has `Next-DiT` as the backbone, the text encoder is the `Gemma` 2B model, and the VAE uses a version of `sdxl` fine-tuned by stabilityai. - Generation Model: Next-DiT - Text Encoder: [Gemma-2B](https://huggingface.co/google/gemma-2b) - VAE: [stabilityai/sdxl-vae](https://huggingface.co/stabilityai/sdxl-vae) [paper](https://arxiv.org/abs/2405.05945) ## 📰 News - [2024-06-08] 🎉🎉🎉 We have released the `Lumina-Next-SFT` model. - [2024-05-28] We updated the `Lumina-Next-T2I` model to support 2K Resolution image generation. - [2024-05-16] We have converted the `.pth` weights to `.safetensors` weights. Please pull the latest code to use `demo.py` for inference. - [2024-05-12] We release the next version of `Lumina-T2I`, called `Lumina-Next-T2I` for faster and lower memory usage image generation model. ## 🎮 Model Zoo More checkpoints of our model will be released soon~ | Resolution | Next-DiT Parameter| Text Encoder | Prediction | Download URL | | ---------- | ----------------------- | ------------ | -----------|-------------- | | 1024 | 2B | [Gemma-2B](https://huggingface.co/google/gemma-2b) | Rectified Flow | [hugging face](https://huggingface.co/Alpha-VLLM/Lumina-Next-SFT) | ## Installation Before installation, ensure that you have a working ``nvcc`` ```bash # The command should work and show the same version number as in our case. (12.1 in our case). nvcc --version ``` On some outdated distros (e.g., CentOS 7), you may also want to check that a late enough version of ``gcc`` is available ```bash # The command should work and show a version of at least 6.0. # If not, consult distro-specific tutorials to obtain a newer version or build manually. gcc --version ``` Downloading Lumina-T2X repo from GitHub: ```bash git clone https://github.com/Alpha-VLLM/Lumina-T2X ``` ### 1. Create a conda environment and install PyTorch Note: You may want to adjust the CUDA version [according to your driver version](https://docs.nvidia.com/deploy/cuda-compatibility/#default-to-minor-version). ```bash conda create -n Lumina_T2X -y conda activate Lumina_T2X conda install python=3.11 pytorch==2.1.0 torchvision==0.16.0 torchaudio==2.1.0 pytorch-cuda=12.1 -c pytorch -c nvidia -y ``` ### 2. Install dependencies ```bash pip install diffusers fairscale accelerate tensorboard transformers gradio torchdiffeq click ``` or you can use ```bash cd lumina_next_t2i pip install -r requirements.txt ``` ### 3. Install ``flash-attn`` ```bash pip install flash-attn --no-build-isolation ``` ### 4. Install [nvidia apex](https://github.com/nvidia/apex) (optional) >[!Warning] > While Apex can improve efficiency, it is *not* a must to make Lumina-T2X work. > > Note that Lumina-T2X works smoothly with either: > + Apex not installed at all; OR > + Apex successfully installed with CUDA and C++ extensions. > > However, it will fail when: > + A Python-only build of Apex is installed. > > If the error `No module named 'fused_layer_norm_cuda'` appears, it typically means you are using a Python-only build of Apex. To resolve this, please run `pip uninstall apex`, and Lumina-T2X should then function correctly. You can clone the repo and install following the official guidelines (note that we expect a full build, i.e., with CUDA and C++ extensions) ```bash pip install ninja git clone https://github.com/NVIDIA/apex cd apex # if pip >= 23.1 (ref: https://pip.pypa.io/en/stable/news/#v23-1) which supports multiple `--config-settings` with the same key... pip install -v --disable-pip-version-check --no-cache-dir --no-build-isolation --config-settings "--build-option=--cpp_ext" --config-settings "--build-option=--cuda_ext" ./ # otherwise pip install -v --disable-pip-version-check --no-cache-dir --no-build-isolation --global-option="--cpp_ext" --global-option="--cuda_ext" ./ ``` ## Inference To ensure that our generative model is ready to use right out of the box, we provide a user-friendly CLI program and a locally deployable Web Demo site. ### CLI 1. Install Lumina-Next-T2I ```bash pip install -e . ``` 2. Prepare the pre-trained model ⭐⭐ (Recommended) you can use huggingface_cli to download our model: ```bash huggingface-cli download --resume-download Alpha-VLLM/Lumina-Next-T2I --local-dir /path/to/ckpt ``` or using git for cloning the model you want to use: ```bash git clone https://huggingface.co/Alpha-VLLM/Lumina-Next-T2I ``` 1. Setting your personal inference configuration Update your own personal inference settings to generate different styles of images, checking `config/infer/config.yaml` for detailed settings. Detailed config structure: > `/path/to/ckpt` should be a directory containing `consolidated*.pth` and `model_args.pth` ```yaml - settings: model: ckpt: "/path/to/ckpt" # if ckpt is "", you should use `--ckpt` for passing model path when using `lumina` cli. ckpt_lm: "" # if ckpt is "", you should use `--ckpt_lm` for passing model path when using `lumina` cli. token: "" # if LLM is a huggingface gated repo, you should input your access token from huggingface and when token is "", you should `--token` for accessing the model. transport: path_type: "Linear" # option: ["Linear", "GVP", "VP"] prediction: "velocity" # option: ["velocity", "score", "noise"] loss_weight: "velocity" # option: [None, "velocity", "likelihood"] sample_eps: 0.1 train_eps: 0.2 ode: atol: 1e-6 # Absolute tolerance rtol: 1e-3 # Relative tolerance reverse: false # option: true or false likelihood: false # option: true or false infer: resolution: "1024x1024" # option: ["1024x1024", "512x2048", "2048x512", "(Extrapolation) 1664x1664", "(Extrapolation) 1024x2048", "(Extrapolation) 2048x1024"] num_sampling_steps: 60 # range: 1-1000 cfg_scale: 4. # range: 1-20 solver: "euler" # option: ["euler", "dopri5", "dopri8"] t_shift: 4 # range: 1-20 (int only) ntk_scaling: true # option: true or false proportional_attn: true # option: true or false seed: 0 # rnage: any number ``` - model: - `ckpt`: lumina-next-t2i checkpoint path from [huggingface repo](https://huggingface.co/Alpha-VLLM/Lumina-Next-T2I) containing `consolidated*.pth` and `model_args.pth`. - `ckpt_lm`: LLM checkpoint. - `token`: huggingface access token for accessing gated repo. - transport: - `path_type`: the type of path for transport: 'Linear', 'GVP' (Geodesic Vector Pursuit), or 'VP' (Vector Pursuit). - `prediction`: the prediction model for the transport dynamics. - `loss_weight`: the weighting of different components in the loss function, can be 'velocity' for dynamic modeling, 'likelihood' for statistical consistency, or None for no weighting - `sample_eps`: sampling in the transport model. - `train_eps`: training to stabilize the learning process. - ode: - `atol`: Absolute tolerance for the ODE solver. (options: ["Linear", "GVP", "VP"]) - `rtol`: Relative tolerance for the ODE solver. (option: ["velocity", "score", "noise"]) - `reverse`: run the ODE solver in reverse. (option: [None, "velocity", "likelihood"]) - `likelihood`: Enable calculation of likelihood during the ODE solving process. - infer - `resolution`: generated image resolution. - `num_sampling_steps`: sampling step for generating image. - `cfg_scale`: classifier-free guide scaling factor - `solver`: solver for image generation. - `t_shift`: time shift factor. - `ntk_scaling`: ntk rope scaling factor. - `proportional_attn`: Whether to use proportional attention. - `seed`: random initialization seeds. 1. Run with CLI inference command: ```bash lumina_next infer -c <config_path> <caption_here> <output_dir> ``` e.g. Demo command: ```bash cd lumina_next_t2i lumina_next infer -c "config/infer/settings.yaml" "a snowman of ..." "./outputs" ``` ### Web Demo To host a local gradio demo for interactive inference, run the following command: ```bash # `/path/to/ckpt` should be a directory containing `consolidated*.pth` and `model_args.pth` # default python -u demo.py --ckpt "/path/to/ckpt" # the demo by default uses bf16 precision. to switch to fp32: python -u demo.py --ckpt "/path/to/ckpt" --precision fp32 # use ema model python -u demo.py --ckpt "/path/to/ckpt" --ema ```

h34i7cby47t/modelf

h34i7cby47t

Entry not found

HourunLi/BGE3-research

HourunLi

--- license: apache-2.0 ---