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modelId stringlengths 5 122	author stringlengths 2 42	last_modified unknown	library_name stringclasses 189 values	tags sequencelengths 1 1.84k	pipeline_tag stringclasses 48 values	createdAt unknown	card stringlengths 1 901k
Guilherme34/Samantha-mixtraldolphin-GGUF	Guilherme34	"2024-04-15T01:23:47Z"	null	[ "gguf", "region:us" ]	null	"2024-04-15T01:03:56Z"	Entry not found
ashishp-wiai/ClipArt_LoRA_90-2024-04-15	ashishp-wiai	"2024-04-15T01:40:04Z"	null	[ "safetensors", "region:us" ]	null	"2024-04-15T01:04:39Z"	Entry not found
RZJournal/bloomz-560m_PROMPT_TUNING_CAUSAL_LM	RZJournal	"2024-04-15T02:09:11Z"	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	"2024-04-15T01:04:48Z"	--- 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]
heyllm234/sc16	heyllm234	"2024-04-15T01:07:02Z"	transformers	[ "transformers", "safetensors", "stablelm", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	"2024-04-15T01:04:57Z"	--- 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]
Raghu928/ChatShePT	Raghu928	"2024-04-15T01:05:36Z"	null	[ "license:apache-2.0", "region:us" ]	null	"2024-04-15T01:05:36Z"	--- license: apache-2.0 ---
liminerity/Bitnet-Mistral.0.2-70m	liminerity	"2024-04-15T01:47:09Z"	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "Mistral", "1bit", "bitnet", "abideen", "dataset:abideen/Cosmopedia-100k-pretrain", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	text-generation	"2024-04-15T01:07:16Z"	--- datasets: - abideen/Cosmopedia-100k-pretrain tags: - Mistral - 1bit - bitnet - abideen --- """this is my first attempt at converting a model float16 quantized model to 1.5bit. i used alpindale/Mistral-7B-v0.2-hf for the base model and \n trained on: abideen/cosmopedia-100k-pretain dataset and used his google colab project to make this""" #EXAMPLE INFERENCE CODE FROM ABIDEEN'S COLAB PROJECT ``` from transformers import AutoModelForCausalLM, AutoTokenizer from transformers.models.llama.modeling_llama import * # Load a pretrained BitNet model model = "liminerity/Bitnet-Mistral.0.2-70M" tokenizer = AutoTokenizer.from_pretrained(model) model = AutoModelForCausalLM.from_pretrained(model) def activation_quant(x): scale = 127.0 / x.abs().max(dim=-1, keepdim=True).values.clamp_(min=1e-5) y = (x * scale).round().clamp_(-128, 127) y = y / scale return y def weight_quant(w): scale = 1.0 / w.abs().mean().clamp_(min=1e-5) u = (w * scale).round().clamp_(-1, 1) u = u / scale return u class BitLinear(nn.Linear): def forward(self, x): w = self.weight # a weight tensor with shape [d, k] x = x.to(w.device) RMSNorm = LlamaRMSNorm(x.shape[-1]).to(w.device) x_norm = RMSNorm(x) # A trick for implementing Straight−Through−Estimator (STE) using detach() x_quant = x_norm + (activation_quant(x_norm) - x_norm).detach() w_quant = w + (weight_quant(w) - w).detach() y = F.linear(x_quant, w_quant) return y def convert_to_bitnet(model, copy_weights): for name, module in model.named_modules(): # Replace linear layers with BitNet if isinstance(module, LlamaSdpaAttention) or isinstance(module, LlamaMLP): for child_name, child_module in module.named_children(): if isinstance(child_module, nn.Linear): bitlinear = BitLinear(child_module.in_features, child_module.out_features, child_module.bias is not None).to(device="cuda:0") if copy_weights: bitlinear.weight = child_module.weight if child_module.bias is not None: bitlinear.bias = child_module.bias setattr(module, child_name, bitlinear) # Remove redundant input_layernorms elif isinstance(module, LlamaDecoderLayer): for child_name, child_module in module.named_children(): if isinstance(child_module, LlamaRMSNorm) and child_name == "input_layernorm": setattr(module, child_name, nn.Identity().to(device="cuda:0")) convert_to_bitnet(model, copy_weights=True) model.to(device="cuda:0") prompt = "What is Machine Learning?" inputs = tokenizer(prompt, return_tensors="pt").to(model.device) generate_ids = model.generate(inputs.input_ids, max_length=50) tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0] ```
DavidAU/PiVoT-0.1-Evil-a-Q6_K-GGUF	DavidAU	"2024-04-15T01:08:41Z"	null	[ "gguf", "not-for-all-audiences", "llama-cpp", "gguf-my-repo", "text-generation", "en", "ko", "dataset:maywell/ko_wikidata_QA", "dataset:kyujinpy/OpenOrca-KO", "dataset:Anthropic/hh-rlhf", "license:cc-by-sa-4.0", "region:us" ]	text-generation	"2024-04-15T01:08:26Z"	--- language: - en - ko license: cc-by-sa-4.0 tags: - not-for-all-audiences - llama-cpp - gguf-my-repo datasets: - maywell/ko_wikidata_QA - kyujinpy/OpenOrca-KO - Anthropic/hh-rlhf pipeline_tag: text-generation --- # DavidAU/PiVoT-0.1-Evil-a-Q6_K-GGUF This model was converted to GGUF format from [`maywell/PiVoT-0.1-Evil-a`](https://huggingface.co/maywell/PiVoT-0.1-Evil-a) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/maywell/PiVoT-0.1-Evil-a) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew. ```bash brew install ggerganov/ggerganov/llama.cpp ``` Invoke the llama.cpp server or the CLI. CLI: ```bash llama-cli --hf-repo DavidAU/PiVoT-0.1-Evil-a-Q6_K-GGUF --model pivot-0.1-evil-a.Q6_K.gguf -p "The meaning to life and the universe is" ``` Server: ```bash llama-server --hf-repo DavidAU/PiVoT-0.1-Evil-a-Q6_K-GGUF --model pivot-0.1-evil-a.Q6_K.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. ``` git clone https://github.com/ggerganov/llama.cpp && cd llama.cpp && make && ./main -m pivot-0.1-evil-a.Q6_K.gguf -n 128 ```
DavidAU/PiVoT-0.1-Starling-LM-RP-Q6_K-GGUF	DavidAU	"2024-04-15T01:09:54Z"	null	[ "gguf", "llama-cpp", "gguf-my-repo", "text-generation", "en", "license:cc-by-nc-4.0", "region:us" ]	text-generation	"2024-04-15T01:09:35Z"	--- language: - en license: cc-by-nc-4.0 tags: - llama-cpp - gguf-my-repo pipeline_tag: text-generation --- # DavidAU/PiVoT-0.1-Starling-LM-RP-Q6_K-GGUF This model was converted to GGUF format from [`maywell/PiVoT-0.1-Starling-LM-RP`](https://huggingface.co/maywell/PiVoT-0.1-Starling-LM-RP) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/maywell/PiVoT-0.1-Starling-LM-RP) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew. ```bash brew install ggerganov/ggerganov/llama.cpp ``` Invoke the llama.cpp server or the CLI. CLI: ```bash llama-cli --hf-repo DavidAU/PiVoT-0.1-Starling-LM-RP-Q6_K-GGUF --model pivot-0.1-starling-lm-rp.Q6_K.gguf -p "The meaning to life and the universe is" ``` Server: ```bash llama-server --hf-repo DavidAU/PiVoT-0.1-Starling-LM-RP-Q6_K-GGUF --model pivot-0.1-starling-lm-rp.Q6_K.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. ``` git clone https://github.com/ggerganov/llama.cpp && cd llama.cpp && make && ./main -m pivot-0.1-starling-lm-rp.Q6_K.gguf -n 128 ```
SiriusW/multi_balanced_model	SiriusW	"2024-04-15T01:13:35Z"	transformers	[ "transformers", "tensorboard", "safetensors", "distilbert", "token-classification", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	"2024-04-15T01:09:41Z"	Entry not found
abhayesian/BobzillaV22	abhayesian	"2024-04-15T01:10:00Z"	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	"2024-04-15T01:09:51Z"	--- 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]
DavidAU/FuseChat-7B-VaRM-Q6_K-GGUF	DavidAU	"2024-04-15T01:11:07Z"	transformers	[ "transformers", "gguf", "mistral", "mixtral", "solar", "model-fusion", "fusechat", "llama-cpp", "gguf-my-repo", "text-generation", "en", "dataset:FuseAI/FuseChat-Mixture", "base_model:openchat/openchat_3.5", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	text-generation	"2024-04-15T01:10:44Z"	--- language: - en license: apache-2.0 library_name: transformers tags: - mistral - mixtral - solar - model-fusion - fusechat - llama-cpp - gguf-my-repo base_model: openchat/openchat_3.5 datasets: - FuseAI/FuseChat-Mixture pipeline_tag: text-generation model-index: - name: FuseChat-7B-VaRM results: - task: type: text-generation name: Text Generation dataset: name: MT-Bench type: unknown metrics: - type: unknown value: 8.22 name: score source: url: https://huggingface.co/spaces/lmsys/mt-bench - task: type: text-generation name: Text Generation dataset: name: AI2 Reasoning Challenge (25-Shot) type: ai2_arc config: ARC-Challenge split: test args: num_few_shot: 25 metrics: - type: acc_norm value: 62.88 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=FuseAI/FuseChat-7B-VaRM name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: HellaSwag (10-Shot) type: hellaswag split: validation args: num_few_shot: 10 metrics: - type: acc_norm value: 84.25 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=FuseAI/FuseChat-7B-VaRM name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MMLU (5-Shot) type: cais/mmlu config: all split: test args: num_few_shot: 5 metrics: - type: acc value: 63.71 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=FuseAI/FuseChat-7B-VaRM name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: TruthfulQA (0-shot) type: truthful_qa config: multiple_choice split: validation args: num_few_shot: 0 metrics: - type: mc2 value: 45.67 source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=FuseAI/FuseChat-7B-VaRM name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: Winogrande (5-shot) type: winogrande config: winogrande_xl split: validation args: num_few_shot: 5 metrics: - type: acc value: 79.16 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=FuseAI/FuseChat-7B-VaRM name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: GSM8k (5-shot) type: gsm8k config: main split: test args: num_few_shot: 5 metrics: - type: acc value: 63.46 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=FuseAI/FuseChat-7B-VaRM name: Open LLM Leaderboard --- # DavidAU/FuseChat-7B-VaRM-Q6_K-GGUF This model was converted to GGUF format from [`FuseAI/FuseChat-7B-VaRM`](https://huggingface.co/FuseAI/FuseChat-7B-VaRM) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/FuseAI/FuseChat-7B-VaRM) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew. ```bash brew install ggerganov/ggerganov/llama.cpp ``` Invoke the llama.cpp server or the CLI. CLI: ```bash llama-cli --hf-repo DavidAU/FuseChat-7B-VaRM-Q6_K-GGUF --model fusechat-7b-varm.Q6_K.gguf -p "The meaning to life and the universe is" ``` Server: ```bash llama-server --hf-repo DavidAU/FuseChat-7B-VaRM-Q6_K-GGUF --model fusechat-7b-varm.Q6_K.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. ``` git clone https://github.com/ggerganov/llama.cpp && cd llama.cpp && make && ./main -m fusechat-7b-varm.Q6_K.gguf -n 128 ```
fangzhaoz/mistralv1_dora_r8_25e5_e3	fangzhaoz	"2024-04-15T01:12:23Z"	peft	[ "peft", "tensorboard", "safetensors", "generated_from_trainer", "base_model:mistralai/Mistral-7B-v0.1", "license:apache-2.0", "region:us" ]	null	"2024-04-15T01:12:13Z"	--- license: apache-2.0 library_name: peft tags: - generated_from_trainer base_model: mistralai/Mistral-7B-v0.1 model-index: - name: mistralv1_dora_r8_25e5_e3 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # mistralv1_dora_r8_25e5_e3 This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2.5e-05 - train_batch_size: 4 - eval_batch_size: 8 - seed: 0 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results ### Framework versions - PEFT 0.10.0 - Transformers 4.40.0.dev0 - Pytorch 2.2.1+cu121 - Datasets 2.18.0 - Tokenizers 0.15.0
aivoicemodelsbr23/Raul.Seixas	aivoicemodelsbr23	"2024-04-15T01:16:36Z"	null	[ "region:us" ]	null	"2024-04-15T01:12:18Z"	Entry not found
bingbort/bigstral-12b-v0.2-32k-Q6_K-GGUF	bingbort	"2024-04-15T01:12:56Z"	transformers	[ "transformers", "gguf", "mergekit", "merge", "llama-cpp", "gguf-my-repo", "base_model:alpindale/Mistral-7B-v0.2-hf", "endpoints_compatible", "region:us" ]	null	"2024-04-15T01:12:20Z"	--- library_name: transformers tags: - mergekit - merge - llama-cpp - gguf-my-repo base_model: - alpindale/Mistral-7B-v0.2-hf --- # bingbort/bigstral-12b-v0.2-32k-Q6_K-GGUF This model was converted to GGUF format from [`abacusai/bigstral-12b-v0.2-32k`](https://huggingface.co/abacusai/bigstral-12b-v0.2-32k) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/abacusai/bigstral-12b-v0.2-32k) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew. ```bash brew install ggerganov/ggerganov/llama.cpp ``` Invoke the llama.cpp server or the CLI. CLI: ```bash llama-cli --hf-repo bingbort/bigstral-12b-v0.2-32k-Q6_K-GGUF --model bigstral-12b-v0.2-32k.Q6_K.gguf -p "The meaning to life and the universe is" ``` Server: ```bash llama-server --hf-repo bingbort/bigstral-12b-v0.2-32k-Q6_K-GGUF --model bigstral-12b-v0.2-32k.Q6_K.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. ``` git clone https://github.com/ggerganov/llama.cpp && cd llama.cpp && make && ./main -m bigstral-12b-v0.2-32k.Q6_K.gguf -n 128 ```
mradermacher/StableBeluga2-GGUF	mradermacher	"2024-04-15T01:40:58Z"	null	[ "region:us" ]	null	"2024-04-15T01:12:24Z"	<!-- ### quantize_version: 1 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: --> <!-- ### vocab_type: --> static quants of https://huggingface.co/stabilityai/StableBeluga2
fangzhaoz/mistralv1_dora_r8_25e5_e3_merged	fangzhaoz	"2024-04-15T01:16:04Z"	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	text-generation	"2024-04-15T01:12:35Z"	--- 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]
mergekit-community/mergekit-slerp-mvmjnos	mergekit-community	"2024-04-15T01:12:39Z"	null	[ "region:us" ]	null	"2024-04-15T01:12:38Z"	Invalid username or password.
lattavia/mistral-finetuned-senior-V2	lattavia	"2024-04-15T01:14:14Z"	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	"2024-04-15T01:14:01Z"	--- 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]
gmkim/SOLAR-10.7B-open-korean-instructions-v1.0	gmkim	"2024-04-15T01:15:21Z"	null	[ "region:us" ]	null	"2024-04-15T01:15:20Z"	Entry not found
kyryl-opens-ml/duckdb-text2sql-codellama	kyryl-opens-ml	"2024-04-15T02:23:13Z"	peft	[ "peft", "safetensors", "trl", "sft", "generated_from_trainer", "dataset:generator", "base_model:codellama/CodeLlama-7b-hf", "license:llama2", "region:us" ]	null	"2024-04-15T01:18:16Z"	--- license: llama2 library_name: peft tags: - trl - sft - generated_from_trainer base_model: codellama/CodeLlama-7b-hf datasets: - generator model-index: - name: duckdb-text2sql-codellama 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. --> # duckdb-text2sql-codellama This model is a fine-tuned version of [codellama/CodeLlama-7b-hf](https://huggingface.co/codellama/CodeLlama-7b-hf) on the generator 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: 2 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - lr_scheduler_warmup_ratio: 0.03 - num_epochs: 0.01 ### Training results ### Framework versions - PEFT 0.8.2 - Transformers 4.38.2 - Pytorch 2.1.0+cu118 - Datasets 2.16.1 - Tokenizers 0.15.2
DavidAU/Tiny-Llama-2.2B-slerpx2-Q8_0-GGUF	DavidAU	"2024-04-15T01:19:46Z"	null	[ "gguf", "merge", "mergekit", "lazymergekit", "Chuanming/Tiny-Llama-2.2B-slerp", "llama-cpp", "gguf-my-repo", "base_model:Chuanming/Tiny-Llama-2.2B-slerp", "region:us" ]	null	"2024-04-15T01:19:35Z"	--- tags: - merge - mergekit - lazymergekit - Chuanming/Tiny-Llama-2.2B-slerp - llama-cpp - gguf-my-repo base_model: - Chuanming/Tiny-Llama-2.2B-slerp - Chuanming/Tiny-Llama-2.2B-slerp --- # DavidAU/Tiny-Llama-2.2B-slerpx2-Q8_0-GGUF This model was converted to GGUF format from [`aipib/Tiny-Llama-2.2B-slerpx2`](https://huggingface.co/aipib/Tiny-Llama-2.2B-slerpx2) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/aipib/Tiny-Llama-2.2B-slerpx2) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew. ```bash brew install ggerganov/ggerganov/llama.cpp ``` Invoke the llama.cpp server or the CLI. CLI: ```bash llama-cli --hf-repo DavidAU/Tiny-Llama-2.2B-slerpx2-Q8_0-GGUF --model tiny-llama-2.2b-slerpx2.Q8_0.gguf -p "The meaning to life and the universe is" ``` Server: ```bash llama-server --hf-repo DavidAU/Tiny-Llama-2.2B-slerpx2-Q8_0-GGUF --model tiny-llama-2.2b-slerpx2.Q8_0.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. ``` git clone https://github.com/ggerganov/llama.cpp && cd llama.cpp && make && ./main -m tiny-llama-2.2b-slerpx2.Q8_0.gguf -n 128 ```
DavidAU/Tiny-Cowboy-1.1b-v0.1-Q8_0-GGUF	DavidAU	"2024-04-15T01:20:17Z"	null	[ "gguf", "llama-cpp", "gguf-my-repo", "en", "license:cc-by-nc-4.0", "region:us" ]	null	"2024-04-15T01:20:05Z"	--- language: - en license: cc-by-nc-4.0 tags: - llama-cpp - gguf-my-repo widget: - text: 'Howdy! What is best about the prairie, cowpoke? ' example_title: Color of a Typical Cowboy Hat --- # DavidAU/Tiny-Cowboy-1.1b-v0.1-Q8_0-GGUF This model was converted to GGUF format from [`phanerozoic/Tiny-Cowboy-1.1b-v0.1`](https://huggingface.co/phanerozoic/Tiny-Cowboy-1.1b-v0.1) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/phanerozoic/Tiny-Cowboy-1.1b-v0.1) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew. ```bash brew install ggerganov/ggerganov/llama.cpp ``` Invoke the llama.cpp server or the CLI. CLI: ```bash llama-cli --hf-repo DavidAU/Tiny-Cowboy-1.1b-v0.1-Q8_0-GGUF --model tiny-cowboy-1.1b-v0.1.Q8_0.gguf -p "The meaning to life and the universe is" ``` Server: ```bash llama-server --hf-repo DavidAU/Tiny-Cowboy-1.1b-v0.1-Q8_0-GGUF --model tiny-cowboy-1.1b-v0.1.Q8_0.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. ``` git clone https://github.com/ggerganov/llama.cpp && cd llama.cpp && make && ./main -m tiny-cowboy-1.1b-v0.1.Q8_0.gguf -n 128 ```
DavidAU/Tiny-Viking-1.1b-v0.1-Q8_0-GGUF	DavidAU	"2024-04-15T01:21:41Z"	null	[ "gguf", "llama-cpp", "gguf-my-repo", "en", "license:cc-by-nc-4.0", "region:us" ]	null	"2024-04-15T01:21:35Z"	--- language: - en license: cc-by-nc-4.0 tags: - llama-cpp - gguf-my-repo widget: - text: 'Who are you? ' example_title: Introduction --- # DavidAU/Tiny-Viking-1.1b-v0.1-Q8_0-GGUF This model was converted to GGUF format from [`phanerozoic/Tiny-Viking-1.1b-v0.1`](https://huggingface.co/phanerozoic/Tiny-Viking-1.1b-v0.1) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/phanerozoic/Tiny-Viking-1.1b-v0.1) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew. ```bash brew install ggerganov/ggerganov/llama.cpp ``` Invoke the llama.cpp server or the CLI. CLI: ```bash llama-cli --hf-repo DavidAU/Tiny-Viking-1.1b-v0.1-Q8_0-GGUF --model tiny-viking-1.1b-v0.1.Q8_0.gguf -p "The meaning to life and the universe is" ``` Server: ```bash llama-server --hf-repo DavidAU/Tiny-Viking-1.1b-v0.1-Q8_0-GGUF --model tiny-viking-1.1b-v0.1.Q8_0.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. ``` git clone https://github.com/ggerganov/llama.cpp && cd llama.cpp && make && ./main -m tiny-viking-1.1b-v0.1.Q8_0.gguf -n 128 ```
DavidAU/Tiny-Pirate-1.1b-v0.1-Q8_0-GGUF	DavidAU	"2024-04-15T01:22:28Z"	null	[ "gguf", "llama-cpp", "gguf-my-repo", "en", "license:cc-by-nc-4.0", "region:us" ]	null	"2024-04-15T01:22:15Z"	--- language: - en license: cc-by-nc-4.0 tags: - llama-cpp - gguf-my-repo widget: - text: 'What is best in life? ' example_title: Healthy Eating Tips --- # DavidAU/Tiny-Pirate-1.1b-v0.1-Q8_0-GGUF This model was converted to GGUF format from [`phanerozoic/Tiny-Pirate-1.1b-v0.1`](https://huggingface.co/phanerozoic/Tiny-Pirate-1.1b-v0.1) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/phanerozoic/Tiny-Pirate-1.1b-v0.1) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew. ```bash brew install ggerganov/ggerganov/llama.cpp ``` Invoke the llama.cpp server or the CLI. CLI: ```bash llama-cli --hf-repo DavidAU/Tiny-Pirate-1.1b-v0.1-Q8_0-GGUF --model tiny-pirate-1.1b-v0.1.Q8_0.gguf -p "The meaning to life and the universe is" ``` Server: ```bash llama-server --hf-repo DavidAU/Tiny-Pirate-1.1b-v0.1-Q8_0-GGUF --model tiny-pirate-1.1b-v0.1.Q8_0.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. ``` git clone https://github.com/ggerganov/llama.cpp && cd llama.cpp && make && ./main -m tiny-pirate-1.1b-v0.1.Q8_0.gguf -n 128 ```
mergekit-community/mergekit-slerp-dnqylrm	mergekit-community	"2024-04-15T01:22:21Z"	null	[ "region:us" ]	null	"2024-04-15T01:22:21Z"	Invalid username or password.
DavidAU/Tiny-Knight-1.1b-v0.1-Q8_0-GGUF	DavidAU	"2024-04-15T01:22:52Z"	null	[ "gguf", "llama-cpp", "gguf-my-repo", "en", "license:cc-by-nc-4.0", "region:us" ]	null	"2024-04-15T01:22:45Z"	--- language: - en license: cc-by-nc-4.0 tags: - llama-cpp - gguf-my-repo widget: - text: 'Hail and well met! Pray, what kind of food do ye enjoy supping upon? ' example_title: The Code of Chivalry --- # DavidAU/Tiny-Knight-1.1b-v0.1-Q8_0-GGUF This model was converted to GGUF format from [`phanerozoic/Tiny-Knight-1.1b-v0.1`](https://huggingface.co/phanerozoic/Tiny-Knight-1.1b-v0.1) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/phanerozoic/Tiny-Knight-1.1b-v0.1) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew. ```bash brew install ggerganov/ggerganov/llama.cpp ``` Invoke the llama.cpp server or the CLI. CLI: ```bash llama-cli --hf-repo DavidAU/Tiny-Knight-1.1b-v0.1-Q8_0-GGUF --model tiny-knight-1.1b-v0.1.Q8_0.gguf -p "The meaning to life and the universe is" ``` Server: ```bash llama-server --hf-repo DavidAU/Tiny-Knight-1.1b-v0.1-Q8_0-GGUF --model tiny-knight-1.1b-v0.1.Q8_0.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. ``` git clone https://github.com/ggerganov/llama.cpp && cd llama.cpp && make && ./main -m tiny-knight-1.1b-v0.1.Q8_0.gguf -n 128 ```
d-d-o-s/s9-c	d-d-o-s	"2024-04-15T01:22:48Z"	null	[ "region:us" ]	null	"2024-04-15T01:22:48Z"	Entry not found
WesPro/MisHumHypNichLimaLora	WesPro	"2024-04-15T01:22:53Z"	null	[ "region:us" ]	null	"2024-04-15T01:22:53Z"	Entry not found
sdadasfgdfgfdg/baldis_basics	sdadasfgdfgfdg	"2024-04-15T01:25:21Z"	null	[ "license:openrail", "region:us" ]	null	"2024-04-15T01:23:37Z"	--- license: openrail ---
MusicBox27/SweetDemeanor	MusicBox27	"2024-04-15T01:26:54Z"	null	[ "license:openrail", "region:us" ]	null	"2024-04-15T01:23:37Z"	--- license: openrail ---
DavidAU/Mistral-Pirate-7b-v0.3-Q8_0-GGUF	DavidAU	"2024-04-15T01:24:08Z"	null	[ "gguf", "llama-cpp", "gguf-my-repo", "en", "license:cc-by-nc-4.0", "region:us" ]	null	"2024-04-15T01:23:48Z"	--- language: - en license: cc-by-nc-4.0 tags: - llama-cpp - gguf-my-repo --- # DavidAU/Mistral-Pirate-7b-v0.3-Q8_0-GGUF This model was converted to GGUF format from [`phanerozoic/Mistral-Pirate-7b-v0.3`](https://huggingface.co/phanerozoic/Mistral-Pirate-7b-v0.3) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/phanerozoic/Mistral-Pirate-7b-v0.3) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew. ```bash brew install ggerganov/ggerganov/llama.cpp ``` Invoke the llama.cpp server or the CLI. CLI: ```bash llama-cli --hf-repo DavidAU/Mistral-Pirate-7b-v0.3-Q8_0-GGUF --model mistral-pirate-7b-v0.3.Q8_0.gguf -p "The meaning to life and the universe is" ``` Server: ```bash llama-server --hf-repo DavidAU/Mistral-Pirate-7b-v0.3-Q8_0-GGUF --model mistral-pirate-7b-v0.3.Q8_0.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. ``` git clone https://github.com/ggerganov/llama.cpp && cd llama.cpp && make && ./main -m mistral-pirate-7b-v0.3.Q8_0.gguf -n 128 ```
hsiuping/finetuning-amazon-model	hsiuping	"2024-04-15T02:38:41Z"	transformers	[ "transformers", "tensorboard", "safetensors", "distilbert", "text-classification", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	"2024-04-15T01:24:02Z"	Entry not found
DavidAU/Mistral-Astronomy-7b-v0.2-Q8_0-GGUF	DavidAU	"2024-04-15T01:24:53Z"	null	[ "gguf", "text-generation-inference", "llama-cpp", "gguf-my-repo", "en", "region:us" ]	null	"2024-04-15T01:24:31Z"	--- language: - en tags: - text-generation-inference - llama-cpp - gguf-my-repo --- # DavidAU/Mistral-Astronomy-7b-v0.2-Q8_0-GGUF This model was converted to GGUF format from [`phanerozoic/Mistral-Astronomy-7b-v0.2`](https://huggingface.co/phanerozoic/Mistral-Astronomy-7b-v0.2) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/phanerozoic/Mistral-Astronomy-7b-v0.2) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew. ```bash brew install ggerganov/ggerganov/llama.cpp ``` Invoke the llama.cpp server or the CLI. CLI: ```bash llama-cli --hf-repo DavidAU/Mistral-Astronomy-7b-v0.2-Q8_0-GGUF --model mistral-astronomy-7b-v0.2.Q8_0.gguf -p "The meaning to life and the universe is" ``` Server: ```bash llama-server --hf-repo DavidAU/Mistral-Astronomy-7b-v0.2-Q8_0-GGUF --model mistral-astronomy-7b-v0.2.Q8_0.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. ``` git clone https://github.com/ggerganov/llama.cpp && cd llama.cpp && make && ./main -m mistral-astronomy-7b-v0.2.Q8_0.gguf -n 128 ```
NiharGupte/swin-tiny-patch4-window7-224-finetuned-student_six_classes	NiharGupte	"2024-04-15T01:31:40Z"	transformers	[ "transformers", "tensorboard", "safetensors", "swin", "image-classification", "generated_from_trainer", "dataset:imagefolder", "base_model:microsoft/swin-tiny-patch4-window7-224", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	"2024-04-15T01:25:11Z"	--- license: apache-2.0 base_model: microsoft/swin-tiny-patch4-window7-224 tags: - generated_from_trainer datasets: - imagefolder metrics: - accuracy model-index: - name: swin-tiny-patch4-window7-224-finetuned-student_six_classes results: - task: name: Image Classification type: image-classification dataset: name: imagefolder type: imagefolder config: default split: train args: default metrics: - name: Accuracy type: accuracy value: 0.9512578616352201 --- <!-- 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. --> # swin-tiny-patch4-window7-224-finetuned-student_six_classes This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.1187 - Accuracy: 0.9513 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 1.619 \| 0.94 \| 11 \| 1.1587 \| 0.4984 \| \| 0.841 \| 1.96 \| 23 \| 0.5082 \| 0.7689 \| \| 0.4154 \| 2.98 \| 35 \| 0.2849 \| 0.8868 \| \| 0.3476 \| 4.0 \| 47 \| 0.2089 \| 0.9418 \| \| 0.2414 \| 4.94 \| 58 \| 0.1575 \| 0.9450 \| \| 0.2128 \| 5.96 \| 70 \| 0.1226 \| 0.9497 \| \| 0.1783 \| 6.98 \| 82 \| 0.1203 \| 0.9481 \| \| 0.167 \| 8.0 \| 94 \| 0.1169 \| 0.9528 \| \| 0.1723 \| 8.94 \| 105 \| 0.1184 \| 0.9513 \| \| 0.1838 \| 9.36 \| 110 \| 0.1187 \| 0.9513 \| ### Framework versions - Transformers 4.38.2 - Pytorch 2.2.1+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2
DavidAU/Mistral-Cowboy-7b-v0.1-Q8_0-GGUF	DavidAU	"2024-04-15T01:25:34Z"	null	[ "gguf", "llama-cpp", "gguf-my-repo", "license:cc-by-nc-4.0", "region:us" ]	null	"2024-04-15T01:25:15Z"	--- license: cc-by-nc-4.0 tags: - llama-cpp - gguf-my-repo --- # DavidAU/Mistral-Cowboy-7b-v0.1-Q8_0-GGUF This model was converted to GGUF format from [`phanerozoic/Mistral-Cowboy-7b-v0.1`](https://huggingface.co/phanerozoic/Mistral-Cowboy-7b-v0.1) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/phanerozoic/Mistral-Cowboy-7b-v0.1) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew. ```bash brew install ggerganov/ggerganov/llama.cpp ``` Invoke the llama.cpp server or the CLI. CLI: ```bash llama-cli --hf-repo DavidAU/Mistral-Cowboy-7b-v0.1-Q8_0-GGUF --model mistral-cowboy-7b-v0.1.Q8_0.gguf -p "The meaning to life and the universe is" ``` Server: ```bash llama-server --hf-repo DavidAU/Mistral-Cowboy-7b-v0.1-Q8_0-GGUF --model mistral-cowboy-7b-v0.1.Q8_0.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. ``` git clone https://github.com/ggerganov/llama.cpp && cd llama.cpp && make && ./main -m mistral-cowboy-7b-v0.1.Q8_0.gguf -n 128 ```
bhugxer/ddpm-coleaf-v3	bhugxer	"2024-04-15T02:48:20Z"	diffusers	[ "diffusers", "license:mit", "diffusers:DDPMPipeline", "region:us" ]	null	"2024-04-15T01:25:32Z"	--- license: mit ---
alnawaisheh/my-code.emails	alnawaisheh	"2024-04-15T02:28:47Z"	keras	[ "keras", "code", "text-classification", "en", "dataset:alnawaisheh/mo-emails.data", "arxiv:1910.09700", "license:apache-2.0", "region:us" ]	text-classification	"2024-04-15T01:25:50Z"	--- license: apache-2.0 datasets: - alnawaisheh/mo-emails.data language: - en metrics: - accuracy - precision - recall - f1 library_name: keras pipeline_tag: text-classification tags: - code --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1). ## 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]
DavidAU/Mistral-Pirate-7b-v2-Q8_0-GGUF	DavidAU	"2024-04-15T01:26:18Z"	null	[ "gguf", "llama-cpp", "gguf-my-repo", "en", "license:cc-by-nc-4.0", "region:us" ]	null	"2024-04-15T01:25:57Z"	--- language: - en license: cc-by-nc-4.0 tags: - llama-cpp - gguf-my-repo --- # DavidAU/Mistral-Pirate-7b-v2-Q8_0-GGUF This model was converted to GGUF format from [`phanerozoic/Mistral-Pirate-7b-v2`](https://huggingface.co/phanerozoic/Mistral-Pirate-7b-v2) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/phanerozoic/Mistral-Pirate-7b-v2) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew. ```bash brew install ggerganov/ggerganov/llama.cpp ``` Invoke the llama.cpp server or the CLI. CLI: ```bash llama-cli --hf-repo DavidAU/Mistral-Pirate-7b-v2-Q8_0-GGUF --model mistral-pirate-7b-v2.Q8_0.gguf -p "The meaning to life and the universe is" ``` Server: ```bash llama-server --hf-repo DavidAU/Mistral-Pirate-7b-v2-Q8_0-GGUF --model mistral-pirate-7b-v2.Q8_0.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. ``` git clone https://github.com/ggerganov/llama.cpp && cd llama.cpp && make && ./main -m mistral-pirate-7b-v2.Q8_0.gguf -n 128 ```
DavidAU/Mistral-Darwin-7b-v0.1-Q8_0-GGUF	DavidAU	"2024-04-15T01:27:00Z"	null	[ "gguf", "llama-cpp", "gguf-my-repo", "license:cc-by-nc-4.0", "region:us" ]	null	"2024-04-15T01:26:40Z"	--- license: cc-by-nc-4.0 tags: - llama-cpp - gguf-my-repo --- # DavidAU/Mistral-Darwin-7b-v0.1-Q8_0-GGUF This model was converted to GGUF format from [`phanerozoic/Mistral-Darwin-7b-v0.1`](https://huggingface.co/phanerozoic/Mistral-Darwin-7b-v0.1) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/phanerozoic/Mistral-Darwin-7b-v0.1) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew. ```bash brew install ggerganov/ggerganov/llama.cpp ``` Invoke the llama.cpp server or the CLI. CLI: ```bash llama-cli --hf-repo DavidAU/Mistral-Darwin-7b-v0.1-Q8_0-GGUF --model mistral-darwin-7b-v0.1.Q8_0.gguf -p "The meaning to life and the universe is" ``` Server: ```bash llama-server --hf-repo DavidAU/Mistral-Darwin-7b-v0.1-Q8_0-GGUF --model mistral-darwin-7b-v0.1.Q8_0.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. ``` git clone https://github.com/ggerganov/llama.cpp && cd llama.cpp && make && ./main -m mistral-darwin-7b-v0.1.Q8_0.gguf -n 128 ```
ShenaoZ/0.001_ablation_iter_3	ShenaoZ	"2024-04-15T02:40:52Z"	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "alignment-handbook", "generated_from_trainer", "trl", "dpo", "conversational", "dataset:updated", "dataset:original", "base_model:ShenaoZ/0.001_ablation_iter_2", "license:mit", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	text-generation	"2024-04-15T01:26:59Z"	--- license: mit base_model: ShenaoZ/0.001_ablation_iter_2 tags: - alignment-handbook - generated_from_trainer - trl - dpo - generated_from_trainer datasets: - updated - original model-index: - name: 0.001_ablation_iter_3 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. --> # 0.001_ablation_iter_3 This model is a fine-tuned version of [ShenaoZ/0.001_ablation_iter_2](https://huggingface.co/ShenaoZ/0.001_ablation_iter_2) on the updated and the original datasets. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-07 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 8 - gradient_accumulation_steps: 2 - total_train_batch_size: 128 - total_eval_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 1 ### Training results ### Framework versions - Transformers 4.36.2 - Pytorch 2.1.2+cu121 - Datasets 2.14.6 - Tokenizers 0.15.2
aloobun/BunMaska-1.8B	aloobun	"2024-04-15T01:28:51Z"	transformers	[ "transformers", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	"2024-04-15T01:27:01Z"	Invalid username or password.
Feebo37/ppo-SnowballTarget	Feebo37	"2024-04-15T01:30:04Z"	ml-agents	[ "ml-agents", "tensorboard", "onnx", "SnowballTarget", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-SnowballTarget", "region:us" ]	reinforcement-learning	"2024-04-15T01:29:21Z"	--- library_name: ml-agents tags: - SnowballTarget - deep-reinforcement-learning - reinforcement-learning - ML-Agents-SnowballTarget --- # ppo Agent playing SnowballTarget This is a trained model of a ppo agent playing SnowballTarget using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A short tutorial where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A longer tutorial to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent playing directly in your browser 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: Feebo37/ppo-SnowballTarget 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
DavidAU/Mistral-Cowboy-7b-v0.1-Q6_K-GGUF	DavidAU	"2024-04-15T01:29:53Z"	null	[ "gguf", "llama-cpp", "gguf-my-repo", "license:cc-by-nc-4.0", "region:us" ]	null	"2024-04-15T01:29:27Z"	--- license: cc-by-nc-4.0 tags: - llama-cpp - gguf-my-repo --- # DavidAU/Mistral-Cowboy-7b-v0.1-Q6_K-GGUF This model was converted to GGUF format from [`phanerozoic/Mistral-Cowboy-7b-v0.1`](https://huggingface.co/phanerozoic/Mistral-Cowboy-7b-v0.1) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/phanerozoic/Mistral-Cowboy-7b-v0.1) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew. ```bash brew install ggerganov/ggerganov/llama.cpp ``` Invoke the llama.cpp server or the CLI. CLI: ```bash llama-cli --hf-repo DavidAU/Mistral-Cowboy-7b-v0.1-Q6_K-GGUF --model mistral-cowboy-7b-v0.1.Q6_K.gguf -p "The meaning to life and the universe is" ``` Server: ```bash llama-server --hf-repo DavidAU/Mistral-Cowboy-7b-v0.1-Q6_K-GGUF --model mistral-cowboy-7b-v0.1.Q6_K.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. ``` git clone https://github.com/ggerganov/llama.cpp && cd llama.cpp && make && ./main -m mistral-cowboy-7b-v0.1.Q6_K.gguf -n 128 ```
K00B404/Gem_Stral_Zephyr_4x-7B_Python_Ties	K00B404	"2024-04-15T01:29:29Z"	null	[ "region:us" ]	null	"2024-04-15T01:29:29Z"	Invalid username or password.
DavidAU/Mistral-Pirate-7b-v2-Q6_K-GGUF	DavidAU	"2024-04-15T01:30:59Z"	null	[ "gguf", "llama-cpp", "gguf-my-repo", "en", "license:cc-by-nc-4.0", "region:us" ]	null	"2024-04-15T01:30:32Z"	--- language: - en license: cc-by-nc-4.0 tags: - llama-cpp - gguf-my-repo --- # DavidAU/Mistral-Pirate-7b-v2-Q6_K-GGUF This model was converted to GGUF format from [`phanerozoic/Mistral-Pirate-7b-v2`](https://huggingface.co/phanerozoic/Mistral-Pirate-7b-v2) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/phanerozoic/Mistral-Pirate-7b-v2) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew. ```bash brew install ggerganov/ggerganov/llama.cpp ``` Invoke the llama.cpp server or the CLI. CLI: ```bash llama-cli --hf-repo DavidAU/Mistral-Pirate-7b-v2-Q6_K-GGUF --model mistral-pirate-7b-v2.Q6_K.gguf -p "The meaning to life and the universe is" ``` Server: ```bash llama-server --hf-repo DavidAU/Mistral-Pirate-7b-v2-Q6_K-GGUF --model mistral-pirate-7b-v2.Q6_K.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. ``` git clone https://github.com/ggerganov/llama.cpp && cd llama.cpp && make && ./main -m mistral-pirate-7b-v2.Q6_K.gguf -n 128 ```
ikozlov/MobileDiffusionTiny	ikozlov	"2024-04-15T01:51:08Z"	diffusers	[ "diffusers", "safetensors", "text-to-image", "license:openrail", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	"2024-04-15T01:31:37Z"	--- license: openrail library_name: diffusers pipeline_tag: text-to-image ---
DavidAU/Mistral-Darwin-7b-v0.1-Q6_K-GGUF	DavidAU	"2024-04-15T01:31:53Z"	null	[ "gguf", "llama-cpp", "gguf-my-repo", "license:cc-by-nc-4.0", "region:us" ]	null	"2024-04-15T01:31:38Z"	--- license: cc-by-nc-4.0 tags: - llama-cpp - gguf-my-repo --- # DavidAU/Mistral-Darwin-7b-v0.1-Q6_K-GGUF This model was converted to GGUF format from [`phanerozoic/Mistral-Darwin-7b-v0.1`](https://huggingface.co/phanerozoic/Mistral-Darwin-7b-v0.1) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/phanerozoic/Mistral-Darwin-7b-v0.1) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew. ```bash brew install ggerganov/ggerganov/llama.cpp ``` Invoke the llama.cpp server or the CLI. CLI: ```bash llama-cli --hf-repo DavidAU/Mistral-Darwin-7b-v0.1-Q6_K-GGUF --model mistral-darwin-7b-v0.1.Q6_K.gguf -p "The meaning to life and the universe is" ``` Server: ```bash llama-server --hf-repo DavidAU/Mistral-Darwin-7b-v0.1-Q6_K-GGUF --model mistral-darwin-7b-v0.1.Q6_K.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. ``` git clone https://github.com/ggerganov/llama.cpp && cd llama.cpp && make && ./main -m mistral-darwin-7b-v0.1.Q6_K.gguf -n 128 ```
ShenaoZ/0.0001_ablation_iter_3	ShenaoZ	"2024-04-15T02:46:03Z"	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "alignment-handbook", "generated_from_trainer", "trl", "dpo", "conversational", "dataset:updated", "dataset:original", "base_model:ShenaoZ/0.0001_ablation_iter_2", "license:mit", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	text-generation	"2024-04-15T01:32:19Z"	--- license: mit base_model: ShenaoZ/0.0001_ablation_iter_2 tags: - alignment-handbook - generated_from_trainer - trl - dpo - generated_from_trainer datasets: - updated - original model-index: - name: 0.0001_ablation_iter_3 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. --> # 0.0001_ablation_iter_3 This model is a fine-tuned version of [ShenaoZ/0.0001_ablation_iter_2](https://huggingface.co/ShenaoZ/0.0001_ablation_iter_2) on the updated and the original datasets. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-07 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 8 - gradient_accumulation_steps: 2 - total_train_batch_size: 128 - total_eval_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 1 ### Training results ### Framework versions - Transformers 4.36.2 - Pytorch 2.1.2+cu121 - Datasets 2.14.6 - Tokenizers 0.15.2
Abhinay123/wav2vec2_vedas2_epoch_1_step_1399	Abhinay123	"2024-04-15T01:33:39Z"	transformers	[ "transformers", "safetensors", "wav2vec2", "automatic-speech-recognition", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	"2024-04-15T01:32:27Z"	Invalid username or password.
DavidAU/Mistral-Pirate-7b-v0.3-Q6_K-GGUF	DavidAU	"2024-04-15T01:32:47Z"	null	[ "gguf", "llama-cpp", "gguf-my-repo", "en", "license:cc-by-nc-4.0", "region:us" ]	null	"2024-04-15T01:32:31Z"	--- language: - en license: cc-by-nc-4.0 tags: - llama-cpp - gguf-my-repo --- # DavidAU/Mistral-Pirate-7b-v0.3-Q6_K-GGUF This model was converted to GGUF format from [`phanerozoic/Mistral-Pirate-7b-v0.3`](https://huggingface.co/phanerozoic/Mistral-Pirate-7b-v0.3) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/phanerozoic/Mistral-Pirate-7b-v0.3) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew. ```bash brew install ggerganov/ggerganov/llama.cpp ``` Invoke the llama.cpp server or the CLI. CLI: ```bash llama-cli --hf-repo DavidAU/Mistral-Pirate-7b-v0.3-Q6_K-GGUF --model mistral-pirate-7b-v0.3.Q6_K.gguf -p "The meaning to life and the universe is" ``` Server: ```bash llama-server --hf-repo DavidAU/Mistral-Pirate-7b-v0.3-Q6_K-GGUF --model mistral-pirate-7b-v0.3.Q6_K.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. ``` git clone https://github.com/ggerganov/llama.cpp && cd llama.cpp && make && ./main -m mistral-pirate-7b-v0.3.Q6_K.gguf -n 128 ```
mrcreeper5/ema-tailsv2	mrcreeper5	"2024-04-15T02:19:46Z"	null	[ "license:openrail", "region:us" ]	null	"2024-04-15T01:32:40Z"	--- license: openrail ---
DavidAU/Mistral-Astronomy-7b-v0.2-Q6_K-GGUF	DavidAU	"2024-04-15T01:33:38Z"	null	[ "gguf", "text-generation-inference", "llama-cpp", "gguf-my-repo", "en", "region:us" ]	null	"2024-04-15T01:33:24Z"	--- language: - en tags: - text-generation-inference - llama-cpp - gguf-my-repo --- # DavidAU/Mistral-Astronomy-7b-v0.2-Q6_K-GGUF This model was converted to GGUF format from [`phanerozoic/Mistral-Astronomy-7b-v0.2`](https://huggingface.co/phanerozoic/Mistral-Astronomy-7b-v0.2) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/phanerozoic/Mistral-Astronomy-7b-v0.2) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew. ```bash brew install ggerganov/ggerganov/llama.cpp ``` Invoke the llama.cpp server or the CLI. CLI: ```bash llama-cli --hf-repo DavidAU/Mistral-Astronomy-7b-v0.2-Q6_K-GGUF --model mistral-astronomy-7b-v0.2.Q6_K.gguf -p "The meaning to life and the universe is" ``` Server: ```bash llama-server --hf-repo DavidAU/Mistral-Astronomy-7b-v0.2-Q6_K-GGUF --model mistral-astronomy-7b-v0.2.Q6_K.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. ``` git clone https://github.com/ggerganov/llama.cpp && cd llama.cpp && make && ./main -m mistral-astronomy-7b-v0.2.Q6_K.gguf -n 128 ```
ShenaoZ/0.0_ablation_iter_3	ShenaoZ	"2024-04-15T02:46:37Z"	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "alignment-handbook", "generated_from_trainer", "trl", "dpo", "conversational", "dataset:updated", "dataset:original", "base_model:ShenaoZ/0.0_ablation_iter_2", "license:mit", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	text-generation	"2024-04-15T01:33:48Z"	Invalid username or password.
DavidAU/TinyLlama-con-creative-writing-v0.2-Q8_0-GGUF	DavidAU	"2024-04-15T01:35:34Z"	null	[ "gguf", "llama-cpp", "gguf-my-repo", "region:us" ]	null	"2024-04-15T01:35:29Z"	--- tags: - llama-cpp - gguf-my-repo --- # DavidAU/TinyLlama-con-creative-writing-v0.2-Q8_0-GGUF This model was converted to GGUF format from [`mogaio/TinyLlama-con-creative-writing-v0.2`](https://huggingface.co/mogaio/TinyLlama-con-creative-writing-v0.2) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/mogaio/TinyLlama-con-creative-writing-v0.2) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew. ```bash brew install ggerganov/ggerganov/llama.cpp ``` Invoke the llama.cpp server or the CLI. CLI: ```bash llama-cli --hf-repo DavidAU/TinyLlama-con-creative-writing-v0.2-Q8_0-GGUF --model tinyllama-con-creative-writing-v0.2.Q8_0.gguf -p "The meaning to life and the universe is" ``` Server: ```bash llama-server --hf-repo DavidAU/TinyLlama-con-creative-writing-v0.2-Q8_0-GGUF --model tinyllama-con-creative-writing-v0.2.Q8_0.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. ``` git clone https://github.com/ggerganov/llama.cpp && cd llama.cpp && make && ./main -m tinyllama-con-creative-writing-v0.2.Q8_0.gguf -n 128 ```
csicar/summarize-mistral	csicar	"2024-04-15T01:36:10Z"	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	"2024-04-15T01:35:40Z"	--- 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. 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suneeln-duke/dukebot-qac-v1	suneeln-duke	"2024-04-15T01:36:06Z"	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	"2024-04-15T01:36:01Z"	--- 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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DavidAU/TinyLlama-con-brainstorming-v0.2-Q8_0-GGUF	DavidAU	"2024-04-15T01:36:55Z"	null	[ "gguf", "llama-cpp", "gguf-my-repo", "region:us" ]	null	"2024-04-15T01:36:39Z"	--- tags: - llama-cpp - gguf-my-repo --- # DavidAU/TinyLlama-con-brainstorming-v0.2-Q8_0-GGUF This model was converted to GGUF format from [`mogaio/TinyLlama-con-brainstorming-v0.2`](https://huggingface.co/mogaio/TinyLlama-con-brainstorming-v0.2) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/mogaio/TinyLlama-con-brainstorming-v0.2) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew. ```bash brew install ggerganov/ggerganov/llama.cpp ``` Invoke the llama.cpp server or the CLI. CLI: ```bash llama-cli --hf-repo DavidAU/TinyLlama-con-brainstorming-v0.2-Q8_0-GGUF --model tinyllama-con-brainstorming-v0.2.Q8_0.gguf -p "The meaning to life and the universe is" ``` Server: ```bash llama-server --hf-repo DavidAU/TinyLlama-con-brainstorming-v0.2-Q8_0-GGUF --model tinyllama-con-brainstorming-v0.2.Q8_0.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. ``` git clone https://github.com/ggerganov/llama.cpp && cd llama.cpp && make && ./main -m tinyllama-con-brainstorming-v0.2.Q8_0.gguf -n 128 ```
suneeln-duke/dukebot-qac-v1-merged	suneeln-duke	"2024-04-15T02:01:45Z"	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "4-bit", "region:us" ]	text-generation	"2024-04-15T01:36:55Z"	--- 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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DavidAU/Bloom-1b7-creative-writing-IT-baseline-Q8_0-GGUF	DavidAU	"2024-04-15T01:38:10Z"	null	[ "gguf", "generated_from_trainer", "llama-cpp", "gguf-my-repo", "base_model:bigscience/bloom-1b7", "license:bigscience-bloom-rail-1.0", "region:us" ]	null	"2024-04-15T01:37:49Z"	--- license: bigscience-bloom-rail-1.0 tags: - generated_from_trainer - llama-cpp - gguf-my-repo base_model: bigscience/bloom-1b7 model-index: - name: Bloom-1b7-creative-writing-IT results: [] --- # DavidAU/Bloom-1b7-creative-writing-IT-baseline-Q8_0-GGUF This model was converted to GGUF format from [`alonzogarbanzo/Bloom-1b7-creative-writing-IT-baseline`](https://huggingface.co/alonzogarbanzo/Bloom-1b7-creative-writing-IT-baseline) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/alonzogarbanzo/Bloom-1b7-creative-writing-IT-baseline) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew. ```bash brew install ggerganov/ggerganov/llama.cpp ``` Invoke the llama.cpp server or the CLI. CLI: ```bash llama-cli --hf-repo DavidAU/Bloom-1b7-creative-writing-IT-baseline-Q8_0-GGUF --model bloom-1b7-creative-writing-it-baseline.Q8_0.gguf -p "The meaning to life and the universe is" ``` Server: ```bash llama-server --hf-repo DavidAU/Bloom-1b7-creative-writing-IT-baseline-Q8_0-GGUF --model bloom-1b7-creative-writing-it-baseline.Q8_0.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. ``` git clone https://github.com/ggerganov/llama.cpp && cd llama.cpp && make && ./main -m bloom-1b7-creative-writing-it-baseline.Q8_0.gguf -n 128 ```
alexyhc/flan-t5-large-ds	alexyhc	"2024-04-15T02:14:52Z"	transformers	[ "transformers", "safetensors", "t5", "text2text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	text2text-generation	"2024-04-15T01:38:17Z"	--- 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. 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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]
coffie3/s29	coffie3	"2024-04-15T01:41:58Z"	transformers	[ "transformers", "safetensors", "stablelm", "text-generation", "conversational", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	"2024-04-15T01:39:26Z"	Entry not found
chienhsiung/test3	chienhsiung	"2024-04-15T01:39:54Z"	null	[ "region:us" ]	null	"2024-04-15T01:39:53Z"	Entry not found
DavidAU/Bloom-1b7-creative-writing-IT-baseline-Q6_K-GGUF	DavidAU	"2024-04-15T01:40:08Z"	null	[ "gguf", "generated_from_trainer", "llama-cpp", "gguf-my-repo", "base_model:bigscience/bloom-1b7", "license:bigscience-bloom-rail-1.0", "region:us" ]	null	"2024-04-15T01:40:00Z"	--- license: bigscience-bloom-rail-1.0 tags: - generated_from_trainer - llama-cpp - gguf-my-repo base_model: bigscience/bloom-1b7 model-index: - name: Bloom-1b7-creative-writing-IT results: [] --- # DavidAU/Bloom-1b7-creative-writing-IT-baseline-Q6_K-GGUF This model was converted to GGUF format from [`alonzogarbanzo/Bloom-1b7-creative-writing-IT-baseline`](https://huggingface.co/alonzogarbanzo/Bloom-1b7-creative-writing-IT-baseline) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/alonzogarbanzo/Bloom-1b7-creative-writing-IT-baseline) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew. ```bash brew install ggerganov/ggerganov/llama.cpp ``` Invoke the llama.cpp server or the CLI. CLI: ```bash llama-cli --hf-repo DavidAU/Bloom-1b7-creative-writing-IT-baseline-Q6_K-GGUF --model bloom-1b7-creative-writing-it-baseline.Q6_K.gguf -p "The meaning to life and the universe is" ``` Server: ```bash llama-server --hf-repo DavidAU/Bloom-1b7-creative-writing-IT-baseline-Q6_K-GGUF --model bloom-1b7-creative-writing-it-baseline.Q6_K.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. ``` git clone https://github.com/ggerganov/llama.cpp && cd llama.cpp && make && ./main -m bloom-1b7-creative-writing-it-baseline.Q6_K.gguf -n 128 ```
ashishp-wiai/ClipArt_LoRA_100-2024-04-15	ashishp-wiai	"2024-04-15T02:48:50Z"	null	[ "safetensors", "region:us" ]	null	"2024-04-15T01:40:42Z"	Entry not found
DavidAU/Writing_Partner_Mistral_7B-Q6_K-GGUF	DavidAU	"2024-04-15T01:41:42Z"	null	[ "gguf", "mistral", "instruct", "finetune", "chatml", "gpt4", "llama-cpp", "gguf-my-repo", "en", "license:apache-2.0", "region:us" ]	null	"2024-04-15T01:41:04Z"	--- language: - en license: apache-2.0 tags: - mistral - instruct - finetune - chatml - gpt4 - llama-cpp - gguf-my-repo --- # DavidAU/Writing_Partner_Mistral_7B-Q6_K-GGUF This model was converted to GGUF format from [`FPHam/Writing_Partner_Mistral_7B`](https://huggingface.co/FPHam/Writing_Partner_Mistral_7B) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/FPHam/Writing_Partner_Mistral_7B) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew. ```bash brew install ggerganov/ggerganov/llama.cpp ``` Invoke the llama.cpp server or the CLI. CLI: ```bash llama-cli --hf-repo DavidAU/Writing_Partner_Mistral_7B-Q6_K-GGUF --model writing_partner_mistral_7b.Q6_K.gguf -p "The meaning to life and the universe is" ``` Server: ```bash llama-server --hf-repo DavidAU/Writing_Partner_Mistral_7B-Q6_K-GGUF --model writing_partner_mistral_7b.Q6_K.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. ``` git clone https://github.com/ggerganov/llama.cpp && cd llama.cpp && make && ./main -m writing_partner_mistral_7b.Q6_K.gguf -n 128 ```
NoahZe/Frank	NoahZe	"2024-04-15T01:52:09Z"	null	[ "license:openrail", "region:us" ]	null	"2024-04-15T01:41:33Z"	--- license: openrail ---
idiotDeveloper/KoreanTelephone_Mini_dataset	idiotDeveloper	"2024-04-15T01:41:59Z"	null	[ "region:us" ]	null	"2024-04-15T01:41:58Z"	--- language: - ko license: apache-2.0 tags: - hf-asr-leaderboard - generated_from_trainer base_model: openai/whisper-small datasets: - idiotDeveloper/KoreanTelephone_Mini_dataset model-index: - name: idiotDeveloper/KoreanTelephone_Mini_dataset 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. --> # idiotDeveloper/KoreanTelephone_Mini_dataset This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the KoreanTelephone_Mini_dataset dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 50 - training_steps: 1 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.40.0.dev0 - Pytorch 1.10.1+cu111 - Datasets 2.18.0 - Tokenizers 0.15.2
mooo16/gemini-1.5-pro-gemma-rewrite-1024	mooo16	"2024-04-15T02:05:09Z"	peft	[ "peft", "tensorboard", "safetensors", "trl", "sft", "generated_from_trainer", "base_model:google/gemma-2b", "license:gemma", "region:us" ]	null	"2024-04-15T01:42:06Z"	--- license: gemma library_name: peft tags: - trl - sft - generated_from_trainer base_model: google/gemma-2b model-index: - name: gemini-1.5-pro-gemma-rewrite-1024 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. --> # gemini-1.5-pro-gemma-rewrite-1024 This model is a fine-tuned version of [google/gemma-2b](https://huggingface.co/google/gemma-2b) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0246 ## 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: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.03 - num_epochs: 5 - mixed_precision_training: Native AMP ### Training results ### Framework versions - PEFT 0.10.0 - Transformers 4.38.2 - Pytorch 2.2.1+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2
danielm2402/gemma-2b-data-scince-basic	danielm2402	"2024-04-15T01:50:06Z"	null	[ "license:apache-2.0", "region:us" ]	null	"2024-04-15T01:42:08Z"	--- license: apache-2.0 ---
Fizzarolli/lust-7b	Fizzarolli	"2024-04-15T02:21:17Z"	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "roleplay", "conversational", "trl", "unsloth", "en", "dataset:Fizzarolli/rpguild_processed", "dataset:Fizzarolli/bluemoon_processeed", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	text-generation	"2024-04-15T01:42:29Z"	--- license: apache-2.0 datasets: - Fizzarolli/rpguild_processed - Fizzarolli/bluemoon_processeed language: - en library_name: transformers tags: - roleplay - conversational - trl - unsloth --- # lust-7b experimental rp model. ## prompt format this one's a bit funky. ``` <\|description\|>Character Character is blah blah blah</s> <\|description\|>Character 2 Character 2 is blah blah blah (optional to make more than one)</s> <\|narrator\|> Describe what you want to happen in the scenario (I dont even know if this works) <\|message\|>Character Character does blah blah blah</s> <\|message\|>Character 2 Character 2 does blah blah blah</s> <\|message\|>Character [start model generation here!] ``` sillytavern templates: TODO ## quants gguf: https://huggingface.co/Fizzarolli/lust-7b-GGUF (mostly still todo)
spow12/Visual-novel-transcriptor	spow12	"2024-04-15T02:39:25Z"	transformers	[ "transformers", "safetensors", "whisper", "automatic-speech-recognition", "ja", "en", "dataset:reazon-research/reazonspeech", "dataset:joujiboi/japanese-anime-speech", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	"2024-04-15T01:43:07Z"	--- library_name: transformers datasets: - reazon-research/reazonspeech - joujiboi/japanese-anime-speech language: - ja - en metrics: - cer pipeline_tag: automatic-speech-recognition --- # Model Card for Model ID ![image](./cover_image.jpeg) <!-- Generated using cagliostrolab/animagine-xl-3.0 --> <!--Prompt: 1girl, black long hair, suit, headphone, write down, upper body, indoor, night, masterpiece, best quality --> Fine tunned ASR model from [distil-whisper/distil-large-v2](https://huggingface.co/distil-whisper/distil-large-v2). This model aimed to transcribe japanese audio especially visual novel. ## 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: spow12(yw_nam) - Shared by : spow12(yw_nam) - Model type: Seq2Seq - Language(s) (NLP): japanese - Finetuned from model : [distil-whisper/distil-large-v2](https://huggingface.co/distil-whisper/distil-large-v2). ## Uses ```python from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq import librosa processor = AutoProcessor.from_pretrained('spow12/Visual-novel-transcriptor', language="ja", task="transcribe") model = AutoModelForSpeechSeq2Seq.from_pretrained('spow12/Visual-novel-transcriptor').cuda() model.config.forced_decoder_ids = processor.get_decoder_prompt_ids(language="ja", task="transcribe") data, _ = librosa.load(wav_path, sr=16000) input_features = processor(data, sampling_rate=16000, return_tensors="pt").input_features.cuda() predicted_ids = model.generate(input_features) transcription = processor.batch_decode(predicted_ids, skip_special_tokens=True) print(transcription[0]) ``` ## Bias, Risks, and Limitations This model trained by japanese dataset included visual novel which contain nsfw content. ## Use & Credit This model is currently available for non-commercial use only. Also, since I'm not detailed in licensing, I hope you use it responsibly. By sharing this model, I hope to contribute to the research efforts of our community (the open-source community and anime persons). ## Citation ```bibtex @misc {Visual-novel-transcriptor, author = { {YoungWoo Nam} }, title = { Visual-novel-transcriptor }, year = 2024, url = { https://huggingface.co/spow12/Visual-novel-transcriptor }, publisher = { Hugging Face } } ```
frcp/jobtalks_llama_v1	frcp	"2024-04-15T01:43:21Z"	null	[ "region:us" ]	null	"2024-04-15T01:43:20Z"	--- tags: - autotrain - text-generation-inference - text-generation - peft library_name: transformers widget: - messages: - role: user content: What is your favorite condiment? license: other --- # Model Trained Using AutoTrain This model was trained using AutoTrain. For more information, please visit [AutoTrain](https://hf.co/docs/autotrain). # Usage ```python from transformers import AutoModelForCausalLM, AutoTokenizer model_path = "PATH_TO_THIS_REPO" tokenizer = AutoTokenizer.from_pretrained(model_path) model = AutoModelForCausalLM.from_pretrained( model_path, device_map="auto", torch_dtype='auto' ).eval() # Prompt content: "hi" messages = [ {"role": "user", "content": "hi"} ] input_ids = tokenizer.apply_chat_template(conversation=messages, tokenize=True, add_generation_prompt=True, return_tensors='pt') output_ids = model.generate(input_ids.to('cuda')) response = tokenizer.decode(output_ids[0][input_ids.shape[1]:], skip_special_tokens=True) # Model response: "Hello! How can I assist you today?" print(response) ```
andreass123/EEVE-Korean-2.8B-v1.0-Q8_0-GGUF	andreass123	"2024-04-15T01:44:23Z"	null	[ "gguf", "generated_from_trainer", "llama-cpp", "gguf-my-repo", "base_model:microsoft/phi-2", "license:apache-2.0", "region:us" ]	null	"2024-04-15T01:43:57Z"	--- license: apache-2.0 tags: - generated_from_trainer - llama-cpp - gguf-my-repo base_model: microsoft/phi-2 model-index: - name: yanolja/EEVE-Korean-2.8B-v1.0 results: [] --- # andreass123/EEVE-Korean-2.8B-v1.0-Q8_0-GGUF This model was converted to GGUF format from [`yanolja/EEVE-Korean-2.8B-v1.0`](https://huggingface.co/yanolja/EEVE-Korean-2.8B-v1.0) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/yanolja/EEVE-Korean-2.8B-v1.0) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew. ```bash brew install ggerganov/ggerganov/llama.cpp ``` Invoke the llama.cpp server or the CLI. CLI: ```bash llama-cli --hf-repo andreass123/EEVE-Korean-2.8B-v1.0-Q8_0-GGUF --model eeve-korean-2.8b-v1.0.Q8_0.gguf -p "The meaning to life and the universe is" ``` Server: ```bash llama-server --hf-repo andreass123/EEVE-Korean-2.8B-v1.0-Q8_0-GGUF --model eeve-korean-2.8b-v1.0.Q8_0.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. ``` git clone https://github.com/ggerganov/llama.cpp && cd llama.cpp && make && ./main -m eeve-korean-2.8b-v1.0.Q8_0.gguf -n 128 ```
DavidAU/Writing_Partner_Mistral_7B-Q8_0-GGUF	DavidAU	"2024-04-15T01:45:12Z"	null	[ "gguf", "mistral", "instruct", "finetune", "chatml", "gpt4", "llama-cpp", "gguf-my-repo", "en", "license:apache-2.0", "region:us" ]	null	"2024-04-15T01:44:46Z"	--- language: - en license: apache-2.0 tags: - mistral - instruct - finetune - chatml - gpt4 - llama-cpp - gguf-my-repo --- # DavidAU/Writing_Partner_Mistral_7B-Q8_0-GGUF This model was converted to GGUF format from [`FPHam/Writing_Partner_Mistral_7B`](https://huggingface.co/FPHam/Writing_Partner_Mistral_7B) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/FPHam/Writing_Partner_Mistral_7B) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew. ```bash brew install ggerganov/ggerganov/llama.cpp ``` Invoke the llama.cpp server or the CLI. CLI: ```bash llama-cli --hf-repo DavidAU/Writing_Partner_Mistral_7B-Q8_0-GGUF --model writing_partner_mistral_7b.Q8_0.gguf -p "The meaning to life and the universe is" ``` Server: ```bash llama-server --hf-repo DavidAU/Writing_Partner_Mistral_7B-Q8_0-GGUF --model writing_partner_mistral_7b.Q8_0.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. ``` git clone https://github.com/ggerganov/llama.cpp && cd llama.cpp && make && ./main -m writing_partner_mistral_7b.Q8_0.gguf -n 128 ```
daenielkim-66/a2c-PandaReachDense-v3	daenielkim-66	"2024-04-15T01:44:57Z"	null	[ "region:us" ]	null	"2024-04-15T01:44:57Z"	Entry not found
chandc/whisper-small-Cantonese	chandc	"2024-04-15T01:45:12Z"	null	[ "region:us" ]	null	"2024-04-15T01:45:12Z"	Entry not found
sayakpaul/sdxl-orpo-large-beta_orpo-0.005-beta_inner-100-lnoise-0.1	sayakpaul	"2024-04-15T01:45:27Z"	null	[ "region:us" ]	null	"2024-04-15T01:45:27Z"	Entry not found
agitohere/sft-microsoft-phi2-on-dialogsum	agitohere	"2024-04-15T01:45:45Z"	peft	[ "peft", "safetensors", "generated_from_trainer", "base_model:microsoft/phi-2", "license:mit", "region:us" ]	null	"2024-04-15T01:45:40Z"	--- license: mit library_name: peft tags: - generated_from_trainer base_model: microsoft/phi-2 model-index: - name: sft-microsoft-phi2-on-dialogsum 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. --> # sft-microsoft-phi2-on-dialogsum This model is a fine-tuned version of [microsoft/phi-2](https://huggingface.co/microsoft/phi-2) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.3639 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 5 - total_train_batch_size: 10 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 50 - training_steps: 100 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 1.4203 \| 5.0 \| 50 \| 1.3966 \| \| 1.2814 \| 10.0 \| 100 \| 1.3639 \| ### Framework versions - PEFT 0.7.1 - Transformers 4.36.2 - Pytorch 2.1.2 - Datasets 2.15.0 - Tokenizers 0.15.1
mradermacher/mistral-7b-orpo-v3.0-GGUF	mradermacher	"2024-04-15T02:14:49Z"	null	[ "gguf", "region:us" ]	null	"2024-04-15T01:45:47Z"	--- base_model: mistralai/Mistral-7B-v0.1 datasets: - HuggingFaceH4/distilabel-capybara-dpo-7k-binarized - HuggingFaceH4/OpenHermesPreferences-10k exported_from: orpo-explorers/mistral-7b-orpo-v3.0 language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher tags: - alignment-handbook - trl - orpo - generated_from_trainer - trl - orpo - generated_from_trainer --- ## About <!-- ### quantize_version: 1 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: --> <!-- ### vocab_type: --> static quants of https://huggingface.co/orpo-explorers/mistral-7b-orpo-v3.0 <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/mistral-7b-orpo-v3.0-GGUF/resolve/main/mistral-7b-orpo-v3.0.Q2_K.gguf) \| Q2_K \| 2.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/mistral-7b-orpo-v3.0-GGUF/resolve/main/mistral-7b-orpo-v3.0.IQ3_XS.gguf) \| IQ3_XS \| 3.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/mistral-7b-orpo-v3.0-GGUF/resolve/main/mistral-7b-orpo-v3.0.Q3_K_S.gguf) \| Q3_K_S \| 3.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/mistral-7b-orpo-v3.0-GGUF/resolve/main/mistral-7b-orpo-v3.0.IQ3_S.gguf) \| IQ3_S \| 3.3 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/mistral-7b-orpo-v3.0-GGUF/resolve/main/mistral-7b-orpo-v3.0.IQ3_M.gguf) \| IQ3_M \| 3.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/mistral-7b-orpo-v3.0-GGUF/resolve/main/mistral-7b-orpo-v3.0.Q3_K_M.gguf) \| Q3_K_M \| 3.6 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/mistral-7b-orpo-v3.0-GGUF/resolve/main/mistral-7b-orpo-v3.0.Q3_K_L.gguf) \| Q3_K_L \| 3.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/mistral-7b-orpo-v3.0-GGUF/resolve/main/mistral-7b-orpo-v3.0.IQ4_XS.gguf) \| IQ4_XS \| 4.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/mistral-7b-orpo-v3.0-GGUF/resolve/main/mistral-7b-orpo-v3.0.Q4_K_S.gguf) \| Q4_K_S \| 4.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/mistral-7b-orpo-v3.0-GGUF/resolve/main/mistral-7b-orpo-v3.0.Q4_K_M.gguf) \| Q4_K_M \| 4.5 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/mistral-7b-orpo-v3.0-GGUF/resolve/main/mistral-7b-orpo-v3.0.Q5_K_S.gguf) \| Q5_K_S \| 5.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/mistral-7b-orpo-v3.0-GGUF/resolve/main/mistral-7b-orpo-v3.0.Q5_K_M.gguf) \| Q5_K_M \| 5.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/mistral-7b-orpo-v3.0-GGUF/resolve/main/mistral-7b-orpo-v3.0.Q6_K.gguf) \| Q6_K \| 6.0 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/mistral-7b-orpo-v3.0-GGUF/resolve/main/mistral-7b-orpo-v3.0.Q8_0.gguf) \| Q8_0 \| 7.8 \| fast, best quality \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
K00B404/Gem_Stral_Zephyr_4x_7B_Python_Ties	K00B404	"2024-04-15T01:46:13Z"	null	[ "region:us" ]	null	"2024-04-15T01:46:13Z"	Invalid username or password.
BWangila/ppo-SnowballTarget	BWangila	"2024-04-15T01:46:37Z"	ml-agents	[ "ml-agents", "tensorboard", "onnx", "SnowballTarget", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-SnowballTarget", "region:us" ]	reinforcement-learning	"2024-04-15T01:46:29Z"	--- library_name: ml-agents tags: - SnowballTarget - deep-reinforcement-learning - reinforcement-learning - ML-Agents-SnowballTarget --- # ppo Agent playing SnowballTarget This is a trained model of a ppo agent playing SnowballTarget using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A short tutorial where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A longer tutorial to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent playing directly in your browser 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: BWangila/ppo-SnowballTarget 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
Erfan-Shayegani/llama2-lora_Unlearned_GA_Accelerate_2	Erfan-Shayegani	"2024-04-15T01:47:46Z"	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	"2024-04-15T01:47:38Z"	Temporary Redirect. Redirecting to /Erfan-Shayegani/llama2-lora_Unlearned_bad_weight_5e-2/resolve/main/README.md
niranjanramarajar/Tamil-llama2-v1	niranjanramarajar	"2024-04-15T02:02:08Z"	transformers	[ "transformers", "pytorch", "llama", "text-generation", "license:llama2", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	text-generation	"2024-04-15T01:48:12Z"	--- license: llama2 ---
andreass123/EEVE-Korean-Instruct-2.8B-v1.0-Q4_K_M-GGUF	andreass123	"2024-04-15T01:54:38Z"	null	[ "gguf", "generated_from_trainer", "llama-cpp", "gguf-my-repo", "base_model:yanolja/EEVE-Korean-2.8B-v1.0", "license:apache-2.0", "region:us" ]	null	"2024-04-15T01:48:34Z"	--- license: apache-2.0 tags: - generated_from_trainer - llama-cpp - gguf-my-repo base_model: yanolja/EEVE-Korean-2.8B-v1.0 model-index: - name: yanolja/EEVE-Korean-Instruct-2.8B-v1.0 results: [] --- # andreass123/EEVE-Korean-Instruct-2.8B-v1.0-Q4_K_M-GGUF This model was converted to GGUF format from [`yanolja/EEVE-Korean-Instruct-2.8B-v1.0`](https://huggingface.co/yanolja/EEVE-Korean-Instruct-2.8B-v1.0) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/yanolja/EEVE-Korean-Instruct-2.8B-v1.0) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew. ```bash brew install ggerganov/ggerganov/llama.cpp ``` Invoke the llama.cpp server or the CLI. CLI: ```bash llama-cli --hf-repo andreass123/EEVE-Korean-Instruct-2.8B-v1.0-Q4_K_M-GGUF --model eeve-korean-instruct-2.8b-v1.0.Q4_K_M.gguf -p "The meaning to life and the universe is" ``` Server: ```bash llama-server --hf-repo andreass123/EEVE-Korean-Instruct-2.8B-v1.0-Q4_K_M-GGUF --model eeve-korean-instruct-2.8b-v1.0.Q4_K_M.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. ``` git clone https://github.com/ggerganov/llama.cpp && cd llama.cpp && make && ./main -m eeve-korean-instruct-2.8b-v1.0.Q4_K_M.gguf -n 128 ```
dataautogpt3/vae	dataautogpt3	"2024-04-15T01:51:03Z"	diffusers	[ "diffusers", "safetensors", "license:apache-2.0", "region:us" ]	null	"2024-04-15T01:49:01Z"	--- license: apache-2.0 ---
leimu/22	leimu	"2024-04-15T01:49:22Z"	null	[ "region:us" ]	null	"2024-04-15T01:49:22Z"	Entry not found
Zritze/imdb-spoiler-distilbert1	Zritze	"2024-04-15T02:26:18Z"	transformers	[ "transformers", "safetensors", "distilbert", "text-classification", "generated_from_trainer", "base_model:distilbert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	"2024-04-15T01:49:53Z"	--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer metrics: - f1 model-index: - name: imdb-spoiler-distilbert1 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. --> # imdb-spoiler-distilbert1 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.6459 - F1: 0.5053 ## 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: 3e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 0.5039 \| 1.0 \| 2870 \| 0.4781 \| 0.4480 \| \| 0.4304 \| 2.0 \| 5740 \| 0.4947 \| 0.5229 \| \| 0.335 \| 3.0 \| 8610 \| 0.6459 \| 0.5053 \| ### Framework versions - Transformers 4.39.3 - Pytorch 2.2.2+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2
ashwanth18/a2c-PandaReachDense-v3	ashwanth18	"2024-04-15T01:54:40Z"	stable-baselines3	[ "stable-baselines3", "PandaReachDense-v3", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	"2024-04-15T01:50:24Z"	--- library_name: stable-baselines3 tags: - PandaReachDense-v3 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: A2C results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: PandaReachDense-v3 type: PandaReachDense-v3 metrics: - type: mean_reward value: -0.26 +/- 0.13 name: mean_reward verified: false --- # A2C Agent playing PandaReachDense-v3 This is a trained model of a A2C agent playing PandaReachDense-v3 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
harikrishnad1997/emotion_tweet_t5-base_2024-04-15	harikrishnad1997	"2024-04-15T01:51:11Z"	transformers	[ "transformers", "safetensors", "t5", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	text-classification	"2024-04-15T01:50:26Z"	--- 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]
blockblockblock/SauerkrautLM-Qwen-32b-bpw2.25	blockblockblock	"2024-04-15T01:54:40Z"	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "sft", "dpo", "conversational", "de", "en", "license:other", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	text-generation	"2024-04-15T01:51:19Z"	--- license: other license_name: tongyi-qianwen-research license_link: >- https://huggingface.co/Qwen/Qwen1.5-32B/blob/main/LICENSE language: - de - en tags: - sft - dpo --- ![SauerkrautLM](https://vago-solutions.ai/wp-content/uploads/2024/04/SauerkrautLM-Qwen-32b.png "SauerkrautLM-Qwen-32b") ## VAGO solutions SauerkrautLM-Qwen-32b Introducing SauerkrautLM-Qwen-32b – our Sauerkraut version of the powerful [Qwen/Qwen1.5-32B](https://huggingface.co/Qwen/Qwen1.5-32B)! The model SauerkrautLM-Qwen-32b is a joint effort between VAGO solutions and Hyperspace.ai. - Finetuned with SFT - Aligned with DPO # Table of Contents 1. [Overview of all SauerkrautLM-Qwen-32b](#all-SauerkrautLM-Qwen-32b) 2. [Model Details](#model-details) - [Prompt template](#prompt-template) - [Training procedure](#proceed-of-the-training) 3. [Evaluation](#evaluation) 5. [Disclaimer](#disclaimer) 6. [Contact](#contact) 7. [Collaborations](#collaborations) 8. [Acknowledgement](#acknowledgement) ## All SauerkrautLM-Qwen-32b \| Model \| HF \| EXL2 \| GGUF \| AWQ \| \|-------\|-------\|-------\|-------\|-------\| \| SauerkrautLM-Qwen-32b \| [Link](https://huggingface.co/VAGOsolutions/SauerkrautLM-Qwen-32b) \| coming soon \| coming soon \| coming soon \| ## Model Details SauerkrautLM-Qwen-32b - Model Type: SauerkrautLM-Qwen-32b is a finetuned Model based on [Qwen/Qwen1.5-32B](https://huggingface.co/Qwen/Qwen1.5-32B) - Language(s): German, English - License: [tongyi-qianwen-research](https://huggingface.co/Qwen/Qwen1.5-32B/blob/main/LICENSEs) - Contact: [VAGO solutions](https://vago-solutions.ai), [Hyperspace.ai](https://hyperspace.computer/) ### Training procedure: - We trained this model for 2 epochs on 160k data samples with SFT. - Afterwards we applied DPO for 1 epoch with 110k data. - LaserRMT version coming soon We teached German language skills on this model. As far as we know, it is the first Qwen 32B model with bilingual skills in German and English. Nevertheless, formulations may occur that are not entirely correct (still work in progress). ### Prompt Template: English: ``` <\|im_start\|>system You are SauerkrautLM, a helpful AI assistant.<\|im_end\|> <\|im_start\|>user {prompt}<\|im_end\|> <\|im_start\|>assistant ``` German: ``` <\|im_start\|>system Du bist SauerkrautLM, ein hilfreicher und freundlicher KI-Assistent.<\|im_end\|> <\|im_start\|>user {prompt}<\|im_end\|> <\|im_start\|>assistant ``` ### Example output of german language: ``` Die Karte zeigte eine verborgene Höhle, die in den umliegenden Bergen zu finden war. Mia war von der Idee fasziniert, diese Höhle zu entdecken und ihre Geheimnisse zu lüften. Sie nahm die Karte mit sich und machte sich auf den Weg, um die Höhle zu finden. Die Wanderung zu den Bergen war eine Herausforderung, aber Mia war fest entschlossen, ihr Abenteuer zu vollenden. Sie überwand steinige Wege und überquerte klirrende Bäche, die ihre Füße kühlten und ihr die Energie für den Rest des Weges gab. Endlich erreichte Mia die Höhle, die von einem dichten Wald umgeben war. Die Höhle war ein Ort der Geheimnisse und des Staunens, der ihr Herz höher schlagen ließ. Sie betrat die Höhle, und die Dunkelheit umhüllte sie wie ein Schleier aus Stille. In der Höhle fand Mia eine alte Schatzkiste, die mit einem alten, verwitterten Holz verziert war. Mit zitternden Händen öffnete sie die Schatzkiste und fand darin eine alte, zerfledderte Schriftrolle. Die Schriftrolle war ein geheimnisvolles Artefakt, das ihr die Geschichte der Höhle offenbarte. ``` ## Evaluation Open LLM Leaderboard: \| Metric \| Value \| \|-----------------------\|---------------------------\| \| Avg. \| 73.11 \| \| ARC (25-shot) \| 59.22 \| \| HellaSwag (10-shot) \| 82.32 \| \| MMLU (5-shot) \| 74.40\| \| TruthfulQA (0-shot) \| 61.03 \| \| Winogrande (5-shot) \| 82.16 \| \| GSM8K (5-shot) \| 79.53 \| ## Disclaimer We must inform users that despite our best efforts in data cleansing, the possibility of uncensored content slipping through cannot be entirely ruled out. However, we cannot guarantee consistently appropriate behavior. Therefore, if you encounter any issues or come across inappropriate content, we kindly request that you inform us through the contact information provided. Additionally, it is essential to understand that the licensing of these models does not constitute legal advice. We are not held responsible for the actions of third parties who utilize our models. ## Contact If you are interested in customized LLMs for business applications, please get in contact with us via our websites. We are also grateful for your feedback and suggestions. ## Collaborations We are also keenly seeking support and investment for our startups, VAGO solutions and Hyperspace where we continuously advance the development of robust language models designed to address a diverse range of purposes and requirements. If the prospect of collaboratively navigating future challenges excites you, we warmly invite you to reach out to us at [VAGO solutions](https://vago-solutions.de/#Kontakt), [Hyperspace.computer](https://hyperspace.computer/) ## Acknowledgement Many thanks to [Qwen](https://huggingface.co/Qwen) for providing such valuable model to the Open-Source community
JaimeWang/bert_classification	JaimeWang	"2024-04-15T01:52:04Z"	null	[ "region:us" ]	null	"2024-04-15T01:52:04Z"	Entry not found
kaya11213/rf_lol	kaya11213	"2024-04-15T02:06:32Z"	null	[ "license:apache-2.0", "region:us" ]	null	"2024-04-15T01:52:16Z"	--- license: apache-2.0 ---
oneandahalfcats/19534	oneandahalfcats	"2024-04-15T01:52:50Z"	null	[ "region:us" ]	null	"2024-04-15T01:52:40Z"	Entry not found
ArmurAI/Pentest-AI-linux	ArmurAI	"2024-04-15T01:52:45Z"	null	[ "region:us" ]	null	"2024-04-15T01:52:44Z"	Entry not found
kokohandoko/sentiment-kepolisian	kokohandoko	"2024-04-15T01:53:03Z"	null	[ "license:apache-2.0", "region:us" ]	null	"2024-04-15T01:53:01Z"	--- license: apache-2.0 ---
andreass123/EEVE-Korean-Instruct-2.8B-v1.0-Q8_0-GGUF	andreass123	"2024-04-15T01:53:21Z"	null	[ "gguf", "generated_from_trainer", "llama-cpp", "gguf-my-repo", "base_model:yanolja/EEVE-Korean-2.8B-v1.0", "license:apache-2.0", "region:us" ]	null	"2024-04-15T01:53:12Z"	--- license: apache-2.0 tags: - generated_from_trainer - llama-cpp - gguf-my-repo base_model: yanolja/EEVE-Korean-2.8B-v1.0 model-index: - name: yanolja/EEVE-Korean-Instruct-2.8B-v1.0 results: [] --- # andreass123/EEVE-Korean-Instruct-2.8B-v1.0-Q8_0-GGUF This model was converted to GGUF format from [`yanolja/EEVE-Korean-Instruct-2.8B-v1.0`](https://huggingface.co/yanolja/EEVE-Korean-Instruct-2.8B-v1.0) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/yanolja/EEVE-Korean-Instruct-2.8B-v1.0) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew. ```bash brew install ggerganov/ggerganov/llama.cpp ``` Invoke the llama.cpp server or the CLI. CLI: ```bash llama-cli --hf-repo andreass123/EEVE-Korean-Instruct-2.8B-v1.0-Q8_0-GGUF --model eeve-korean-instruct-2.8b-v1.0.Q8_0.gguf -p "The meaning to life and the universe is" ``` Server: ```bash llama-server --hf-repo andreass123/EEVE-Korean-Instruct-2.8B-v1.0-Q8_0-GGUF --model eeve-korean-instruct-2.8b-v1.0.Q8_0.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. ``` git clone https://github.com/ggerganov/llama.cpp && cd llama.cpp && make && ./main -m eeve-korean-instruct-2.8b-v1.0.Q8_0.gguf -n 128 ```
elyelysienne/soliris	elyelysienne	"2024-04-15T01:55:37Z"	null	[ "license:openrail", "region:us" ]	null	"2024-04-15T01:53:41Z"	--- license: openrail ---
leimu/23	leimu	"2024-04-15T01:54:13Z"	null	[ "region:us" ]	null	"2024-04-15T01:54:13Z"	Entry not found
BrandonM001/bert-finetuned-ner-accelerate1	BrandonM001	"2024-04-15T02:06:50Z"	transformers	[ "transformers", "tensorboard", "safetensors", "bert", "token-classification", "generated_from_trainer", "base_model:bert-base-cased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	"2024-04-15T01:54:13Z"	--- license: apache-2.0 base_model: bert-base-cased tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: bert-finetuned-ner-accelerate1 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. --> # bert-finetuned-ner-accelerate1 This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0660 - Precision: 0.9330 - Recall: 0.9512 - F1: 0.9420 - Accuracy: 0.9869 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\| \| 0.0377 \| 1.0 \| 1756 \| 0.0631 \| 0.9229 \| 0.9392 \| 0.9310 \| 0.9844 \| \| 0.0199 \| 2.0 \| 3512 \| 0.0668 \| 0.9343 \| 0.9451 \| 0.9397 \| 0.9858 \| \| 0.0095 \| 3.0 \| 5268 \| 0.0660 \| 0.9330 \| 0.9512 \| 0.9420 \| 0.9869 \| ### Framework versions - Transformers 4.38.2 - Pytorch 2.2.1+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2
SiriusW/TSC_classification_model	SiriusW	"2024-04-15T02:00:14Z"	transformers	[ "transformers", "tensorboard", "safetensors", "distilbert", "token-classification", "generated_from_trainer", "base_model:distilbert/distilbert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	"2024-04-15T01:54:16Z"	--- license: apache-2.0 base_model: distilbert/distilbert-base-uncased tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: TSC_classification_model 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. --> # TSC_classification_model This model is a fine-tuned version of [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0442 - Precision: 0.8034 - Recall: 0.7769 - F1: 0.7899 - Accuracy: 0.9944 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 12 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\| \| No log \| 1.0 \| 48 \| 0.0448 \| 0.4732 \| 0.4380 \| 0.4549 \| 0.9866 \| \| No log \| 2.0 \| 96 \| 0.0389 \| 0.5349 \| 0.5702 \| 0.552 \| 0.9902 \| \| No log \| 3.0 \| 144 \| 0.0346 \| 0.7154 \| 0.7273 \| 0.7213 \| 0.9932 \| \| No log \| 4.0 \| 192 \| 0.0355 \| 0.7611 \| 0.7107 \| 0.7350 \| 0.9937 \| \| No log \| 5.0 \| 240 \| 0.0375 \| 0.7603 \| 0.7603 \| 0.7603 \| 0.9939 \| \| No log \| 6.0 \| 288 \| 0.0376 \| 0.7478 \| 0.7107 \| 0.7288 \| 0.9937 \| \| No log \| 7.0 \| 336 \| 0.0414 \| 0.7699 \| 0.7190 \| 0.7436 \| 0.9939 \| \| No log \| 8.0 \| 384 \| 0.0427 \| 0.7778 \| 0.7521 \| 0.7647 \| 0.9942 \| \| No log \| 9.0 \| 432 \| 0.0432 \| 0.8120 \| 0.7851 \| 0.7983 \| 0.9947 \| \| No log \| 10.0 \| 480 \| 0.0438 \| 0.7983 \| 0.7851 \| 0.7917 \| 0.9947 \| \| 0.0095 \| 11.0 \| 528 \| 0.0441 \| 0.8034 \| 0.7769 \| 0.7899 \| 0.9944 \| \| 0.0095 \| 12.0 \| 576 \| 0.0442 \| 0.8034 \| 0.7769 \| 0.7899 \| 0.9944 \| ### Framework versions - Transformers 4.39.3 - Pytorch 2.2.1+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2