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NTQAI/Nxcode-CQ-7B-orpo	NTQAI	2024-05-30T07:04:52Z	10,197	117	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "code", "conversational", "arxiv:2403.07691", "license:other", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-04-24T04:56:38Z	--- license_name: tongyi-qianwen-research license_link: https://huggingface.co/Qwen/CodeQwen1.5-7B/blob/main/LICENSE tags: - code pipeline_tag: text-generation license: other --- <a href="https://ntq.com.vn" target="_blank"><img src="https://cdn-uploads.huggingface.co/production/uploads/5ee1b417636bdb3834e2da19/etbfTJuVdAub2evNP_E4g.png" width="200"/></a> ## Introduction Nxcode-CQ-7B-orpo is an [Monolithic Preference Optimization without Reference Model](https://arxiv.org/abs/2403.07691) fine-tune of Qwen/CodeQwen1.5-7B on 100k samples of high-quality ranking data. ## [Evalplus](https://github.com/evalplus/evalplus) \| EvalPlus \| pass@1 \| \| --- \| --- \| \| HumanEval \| 86.6 \| \| HumanEval+ \| 83.5 \| \| MBPP(v0.2.0) \| 82.3 \| \| MBPP+(v0.2.0) \| 70.4 \| We use a simple template to generate the solution for evalplus: ```python "Complete the following Python function:\n{prompt}" ``` [Evalplus Leaderboard](https://evalplus.github.io/leaderboard.html) \| Models \| HumanEval \| HumanEval+\| \|------ \| ------ \| ------ \| \| GPT-4-Turbo (April 2024)\| 90.2\| 86.6\| \| GPT-4 (May 2023)\| 88.4\| 81.17\| \| GPT-4-Turbo (Nov 2023)\| 85.4\| 79.3\| \| CodeQwen1.5-7B-Chat\| 83.5\| 78.7\| \| claude-3-opus (Mar 2024)\| 82.9\| 76.8\| \| DeepSeek-Coder-33B-instruct\| 81.1\| 75.0\| \| WizardCoder-33B-V1.1\| 79.9\| 73.2\| \| OpenCodeInterpreter-DS-33B\| 79.3\| 73.8\| \| speechless-codellama-34B-v2.0\| 77.4\| 72\| \| GPT-3.5-Turbo (Nov 2023)\| 76.8\| 70.7\| \| Llama3-70B-instruct\| 76.2\| 70.7\| ## Bigcode Leaderboard [Bigcode Leaderboard](https://huggingface.co/spaces/bigcode/bigcode-models-leaderboard) 09/05/2024 Top 1 average score. Top 2 winrate. ![image/png](https://cdn-uploads.huggingface.co/production/uploads/5ee1b417636bdb3834e2da19/OQonD6a7aNjnN9SsTkFp-.png) ## Quickstart Here provides a code snippet with `apply_chat_template` to show you how to load the tokenizer and model and how to generate contents. You should upgrade the transformers if you receive an error when loading the tokenizer ```python from transformers import AutoModelForCausalLM, AutoTokenizer device = "cuda" # the device to load the model onto model = AutoModelForCausalLM.from_pretrained( "NTQAI/Nxcode-CQ-7B-orpo", torch_dtype="auto", device_map="auto" ) tokenizer = AutoTokenizer.from_pretrained("NTQAI/Nxcode-CQ-7B-orpo") prompt = """Complete the following Python function: from typing import List def has_close_elements(numbers: List[float], threshold: float) -> bool: """ Check if in given list of numbers, are any two numbers closer to each other than given threshold. >>> has_close_elements([1.0, 2.0, 3.0], 0.5) False >>> has_close_elements([1.0, 2.8, 3.0, 4.0, 5.0, 2.0], 0.3) True """ """ messages = [ {"role": "user", "content": prompt} ] inputs = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device) outputs = model.generate(inputs, max_new_tokens=512, do_sample=False, top_k=50, top_p=0.95, num_return_sequences=1, eos_token_id=tokenizer.eos_token_id) res = tokenizer.decode(outputs[0][len(inputs[0]):], skip_special_tokens=True) ``` ### Contact information For personal communication related to this project, please contact Nha Nguyen Van ([email protected]).
Niggendar/darksealSDXL10_v60	Niggendar	2024-05-30T07:04:18Z	83	2	diffusers	[ "diffusers", "safetensors", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionXLPipeline", "region:us" ]	text-to-image	2024-05-30T06:56:37Z	--- library_name: diffusers --- # 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 🧨 diffusers model that has been pushed on the Hub. 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Ksgk-fy/ecoach_philippine_v1_merge	Ksgk-fy	2024-05-30T07:03:57Z	78	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2024-05-30T06:58:18Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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zzunyang/law_dpo4	zzunyang	2024-05-30T07:03:21Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:beomi/open-llama-2-ko-7b", "base_model:adapter:beomi/open-llama-2-ko-7b", "region:us" ]	null	2024-05-30T07:02:43Z	--- library_name: peft base_model: beomi/open-llama-2-ko-7b --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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kiendt/Vistral-7B-Med	kiendt	2024-05-30T06:59:16Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "license:mit", "endpoints_compatible", "region:us" ]	null	2024-05-30T06:21:57Z	--- library_name: transformers license: mit --- # 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: kiendt - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: MIT ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: Viet-Mistral/Vistral-7B-Chat ## 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]
Llamarider222/Mixtral-8x7b-Instruct-GPTQ	Llamarider222	2024-05-30T06:53:37Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-30T06:53:22Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
lighteternal/Llama3-merge-biomed-8b	lighteternal	2024-05-30T06:52:13Z	2,776	4	transformers	[ "transformers", "safetensors", "llama", "text-generation", "mergekit", "merge", "conversational", "arxiv:2311.03099", "arxiv:2306.01708", "base_model:NousResearch/Hermes-2-Pro-Llama-3-8B", "base_model:merge:NousResearch/Hermes-2-Pro-Llama-3-8B", "base_model:aaditya/Llama3-OpenBioLLM-8B", "base_model:merge:aaditya/Llama3-OpenBioLLM-8B", "base_model:meta-llama/Meta-Llama-3-8B-Instruct", "base_model:merge:meta-llama/Meta-Llama-3-8B-Instruct", "license:llama3", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-28T19:43:47Z	--- base_model: - meta-llama/Meta-Llama-3-8B-Instruct - NousResearch/Hermes-2-Pro-Llama-3-8B - aaditya/Llama3-OpenBioLLM-8B library_name: transformers tags: - mergekit - merge license: llama3 --- # Llama3-merge-biomed-8b This is a DARE-TIES Merge of Llama3-8b-Instruct + NousResearch/Hermes-2-Pro-Llama-3-8B + aaditya/Llama3-OpenBioLLM-8B. It is a simple experiment to assess whether combining models with strengths in general language understanding and biomedical knowledge can enhance performance on specialized tasks without compromising general applicability. The results indicate promising outcomes in areas like HendrycksTest tasks related to Biology and Medicine, as well as improvements in complex reasoning as seen in the ARC Challenge and Winogrande benchmarks. ## Usage I recommend using the prompt template of Llama3: https://llama.meta.com/docs/model-cards-and-prompt-formats/meta-llama-3/ ## Leaderboard metrics according to 🤗 Open LLM Leaderboard \| Task \| Metric \| Ours (%) \| Llama38BInstr. (%) \|OpenBioLLM8B (%) \| \|--------------------------------------\|--------------------------\|------------------\|------------\|-------------\| \| ARC Challenge \| Accuracy \| 59.39 \| 57.17 \| 55.38 \| \| \| Normalized Accuracy \| 63.65 \| 60.75 \| 58.62 \| \| Hellaswag \| Accuracy \| 62.59 \| 59.04 \| 61.83 \| \| \| Normalized Accuracy \| 81.53 \| 78.55 \| 80.76 \| \| Winogrande \| Accuracy \| 75.93 \| 74.51 \| 70.88 \| \| GSM8K \| Accuracy \| 59.36 \| 68.69 \| 10.15 \| \| HendrycksTest-Anatomy \| Accuracy \| 72.59 \| 65.19 \| 69.62 \| \| HendrycksTest-Clinical Knowledge \| Accuracy \| 77.83 \| 74.72 \| 60.38 \| \| HendrycksTest-College Biology \| Accuracy \| 81.94 \| 79.86 \| 79.86 \| \| HendrycksTest-College Medicine \| Accuracy \| 69.36 \| 63.58 \| 70.52 \| \| HendrycksTest-Medical Genetics \| Accuracy \| 86.00 \| 80.00 \| 80.00 \| \| HendrycksTest-Professional Medicine \| Accuracy \| 77.94 \| 71.69 \| 77.94 \| This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). ## Merge Details ### Merge Method This model was merged using the [DARE](https://arxiv.org/abs/2311.03099) [TIES](https://arxiv.org/abs/2306.01708) merge method using [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) as a base. ### Models Merged The following models were included in the merge: * [NousResearch/Hermes-2-Pro-Llama-3-8B](https://huggingface.co/NousResearch/Hermes-2-Pro-Llama-3-8B) * [aaditya/Llama3-OpenBioLLM-8B](https://huggingface.co/aaditya/Llama3-OpenBioLLM-8B) ### Configuration The following YAML configuration was used to produce this model: ```yaml models: - model: meta-llama/Meta-Llama-3-8B-Instruct # Base model providing a general foundation without specific parameters - model: meta-llama/Meta-Llama-3-8B-Instruct parameters: density: 0.60 weight: 0.5 - model: NousResearch/Hermes-2-Pro-Llama-3-8B parameters: density: 0.55 weight: 0.1 - model: aaditya/Llama3-OpenBioLLM-8B parameters: density: 0.55 weight: 0.4 merge_method: dare_ties base_model: meta-llama/Meta-Llama-3-8B-Instruct parameters: int8_mask: true dtype: bfloat16 ```
dibyendubiswas1998/llm-test	dibyendubiswas1998	2024-05-30T06:52:04Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:TheBloke/Mistral-7B-Instruct-v0.1-GPTQ", "base_model:adapter:TheBloke/Mistral-7B-Instruct-v0.1-GPTQ", "region:us" ]	null	2024-05-30T06:50:44Z	--- library_name: peft base_model: TheBloke/Mistral-7B-Instruct-v0.1-GPTQ --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.10.0
casual/whisper_tiny_til2	casual	2024-05-30T06:46:12Z	93	0	transformers	[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "generated_from_trainer", "base_model:casual/whisper_tiny_24til", "base_model:finetune:casual/whisper_tiny_24til", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2024-05-30T02:56:16Z	--- base_model: casual/whisper_tiny_24til tags: - generated_from_trainer model-index: - name: whisper_tiny_til2 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # whisper_tiny_til2 This model is a fine-tuned version of [casual/whisper_tiny_24til](https://huggingface.co/casual/whisper_tiny_24til) on an unknown dataset. It achieves the following results on the evaluation set: - eval_loss: 0.0000 - eval_wer: 0.0 - eval_runtime: 780.534 - eval_samples_per_second: 4.484 - eval_steps_per_second: 0.561 - epoch: 6.2785 - step: 2750 ## 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: 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_steps: 10 - training_steps: 4000 ### Framework versions - Transformers 4.40.2 - Pytorch 2.0.1+cu117 - Datasets 2.19.1 - Tokenizers 0.19.1
NSC07/flan-t5-base-NvidiaQATrainedModel	NSC07	2024-05-30T06:41:41Z	108	0	transformers	[ "transformers", "safetensors", "t5", "text2text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2024-05-30T06:40:57Z	--- 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]
mradermacher/Starling-JP-7B-GGUF	mradermacher	2024-05-30T06:41:30Z	4	0	transformers	[ "transformers", "gguf", "en", "base_model:HawkClaws/Starling-JP-7B", "base_model:quantized:HawkClaws/Starling-JP-7B", "endpoints_compatible", "region:us" ]	null	2024-05-30T05:42:27Z	--- base_model: HawkClaws/Starling-JP-7B language: - en library_name: transformers quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/HawkClaws/Starling-JP-7B <!-- 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/Starling-JP-7B-GGUF/resolve/main/Starling-JP-7B.Q2_K.gguf) \| Q2_K \| 2.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Starling-JP-7B-GGUF/resolve/main/Starling-JP-7B.IQ3_XS.gguf) \| IQ3_XS \| 3.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/Starling-JP-7B-GGUF/resolve/main/Starling-JP-7B.Q3_K_S.gguf) \| Q3_K_S \| 3.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Starling-JP-7B-GGUF/resolve/main/Starling-JP-7B.IQ3_S.gguf) \| IQ3_S \| 3.3 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/Starling-JP-7B-GGUF/resolve/main/Starling-JP-7B.IQ3_M.gguf) \| IQ3_M \| 3.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Starling-JP-7B-GGUF/resolve/main/Starling-JP-7B.Q3_K_M.gguf) \| Q3_K_M \| 3.6 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Starling-JP-7B-GGUF/resolve/main/Starling-JP-7B.Q3_K_L.gguf) \| Q3_K_L \| 3.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Starling-JP-7B-GGUF/resolve/main/Starling-JP-7B.IQ4_XS.gguf) \| IQ4_XS \| 4.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Starling-JP-7B-GGUF/resolve/main/Starling-JP-7B.Q4_K_S.gguf) \| Q4_K_S \| 4.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Starling-JP-7B-GGUF/resolve/main/Starling-JP-7B.Q4_K_M.gguf) \| Q4_K_M \| 4.5 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Starling-JP-7B-GGUF/resolve/main/Starling-JP-7B.Q5_K_S.gguf) \| Q5_K_S \| 5.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/Starling-JP-7B-GGUF/resolve/main/Starling-JP-7B.Q5_K_M.gguf) \| Q5_K_M \| 5.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Starling-JP-7B-GGUF/resolve/main/Starling-JP-7B.Q6_K.gguf) \| Q6_K \| 6.0 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/Starling-JP-7B-GGUF/resolve/main/Starling-JP-7B.Q8_0.gguf) \| Q8_0 \| 7.8 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/Starling-JP-7B-GGUF/resolve/main/Starling-JP-7B.f16.gguf) \| f16 \| 14.6 \| 16 bpw, overkill \| 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 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## 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 -->
kronos25/mistral-finetuned-samsum	kronos25	2024-05-30T06:39:09Z	0	0	peft	[ "peft", "tensorboard", "safetensors", "trl", "sft", "generated_from_trainer", "base_model:TheBloke/Mistral-7B-Instruct-v0.1-GPTQ", "base_model:adapter:TheBloke/Mistral-7B-Instruct-v0.1-GPTQ", "license:apache-2.0", "region:us" ]	null	2024-05-30T05:34:16Z	--- license: apache-2.0 library_name: peft tags: - trl - sft - generated_from_trainer base_model: TheBloke/Mistral-7B-Instruct-v0.1-GPTQ model-index: - name: mistral-finetuned-samsum results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # mistral-finetuned-samsum This model is a fine-tuned version of [TheBloke/Mistral-7B-Instruct-v0.1-GPTQ](https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.1-GPTQ) 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: 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: cosine - training_steps: 250 - mixed_precision_training: Native AMP ### Training results ### Framework versions - PEFT 0.11.1 - Transformers 4.42.0.dev0 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
QuantFactory/Daredevil-8B-GGUF	QuantFactory	2024-05-30T06:37:46Z	90	1	null	[ "gguf", "merge", "mergekit", "lazymergekit", "text-generation", "base_model:mlabonne/Daredevil-8B", "base_model:quantized:mlabonne/Daredevil-8B", "license:other", "model-index", "endpoints_compatible", "region:us" ]	text-generation	2024-05-30T05:32:36Z	--- license: other tags: - merge - mergekit - lazymergekit base_model: mlabonne/Daredevil-8B model-index: - name: Daredevil-8B results: - task: type: text-generation name: Text Generation dataset: name: AI2 Reasoning Challenge (25-Shot) type: ai2_arc config: ARC-Challenge split: test args: num_few_shot: 25 metrics: - type: acc_norm value: 68.86 name: normalized accuracy source: url: >- https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mlabonne/Daredevil-8B 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: 84.5 name: normalized accuracy source: url: >- https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mlabonne/Daredevil-8B 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: 69.24 name: accuracy source: url: >- https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mlabonne/Daredevil-8B 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: 59.89 source: url: >- https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mlabonne/Daredevil-8B 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: 78.45 name: accuracy source: url: >- https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mlabonne/Daredevil-8B 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: 73.54 name: accuracy source: url: >- https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mlabonne/Daredevil-8B name: Open LLM Leaderboard pipeline_tag: text-generation --- # Daredevil-8B-GGUF This is quantized version of [mlabonne/Daredevil-8B](https://huggingface.co/mlabonne/Daredevil-8B) created using llama.cpp ## Model Description ![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/gFEhcIDSKa3AWpkNfH91q.jpeg) Daredevil-8B is a mega-merge designed to maximize MMLU. On 27 May 24, it is the Llama 3 8B model with the highest MMLU score. From my experience, a high MMLU score is all you need with Llama 3 models. It is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing): * [nbeerbower/llama-3-stella-8B](https://huggingface.co/nbeerbower/llama-3-stella-8B) * [Hastagaras/llama-3-8b-okay](https://huggingface.co/Hastagaras/llama-3-8b-okay) * [nbeerbower/llama-3-gutenberg-8B](https://huggingface.co/nbeerbower/llama-3-gutenberg-8B) * [openchat/openchat-3.6-8b-20240522](https://huggingface.co/openchat/openchat-3.6-8b-20240522) * [Kukedlc/NeuralLLaMa-3-8b-DT-v0.1](https://huggingface.co/Kukedlc/NeuralLLaMa-3-8b-DT-v0.1) * [cstr/llama3-8b-spaetzle-v20](https://huggingface.co/cstr/llama3-8b-spaetzle-v20) * [mlabonne/ChimeraLlama-3-8B-v3](https://huggingface.co/mlabonne/ChimeraLlama-3-8B-v3) * [flammenai/Mahou-1.1-llama3-8B](https://huggingface.co/flammenai/Mahou-1.1-llama3-8B) * [KingNish/KingNish-Llama3-8b](https://huggingface.co/KingNish/KingNish-Llama3-8b) Thanks to nbeerbower, Hastagaras, openchat, Kukedlc, cstr, flammenai, and KingNish for their merges. Special thanks to Charles Goddard and Arcee.ai for MergeKit. ## 🔎 Applications You can use it as an improved version of [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct). This is a censored model. For an uncensored version, see [mlabonne/Daredevil-8B-abliterated](https://huggingface.co/mlabonne/Daredevil-8B-abliterated). Tested on LM Studio using the "Llama 3" preset. ## 🏆 Evaluation ### Open LLM Leaderboard Daredevil-8B is the best-performing 8B model on the Open LLM Leaderboard in terms of MMLU score (27 May 24). ![image/png](https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/xFKhGdSaIxL9_tcJPhM5w.png) ### Nous Daredevil-8B is the best-performing 8B model on Nous' benchmark suite (evaluation performed using [LLM AutoEval](https://github.com/mlabonne/llm-autoeval), 27 May 24). See the entire leaderboard [here](https://huggingface.co/spaces/mlabonne/Yet_Another_LLM_Leaderboard). \| Model \| Average \| AGIEval \| GPT4All \| TruthfulQA \| Bigbench \| \|---\|---:\|---:\|---:\|---:\|---:\| \| [mlabonne/Daredevil-8B](https://huggingface.co/mlabonne/Daredevil-8B) [📄](https://gist.github.com/mlabonne/080f9c5f153ea57a7ab7d932cf896f21) \| 55.87 \| 44.13 \| 73.52 \| 59.05 \| 46.77 \| \| [mlabonne/Daredevil-8B-abliterated](https://huggingface.co/mlabonne/Daredevil-8B-abliterated) [📄](https://gist.github.com/mlabonne/32cdd8460804662c856bcb2a20acd49e) \| 55.06 \| 43.29 \| 73.33 \| 57.47 \| 46.17 \| \| [mlabonne/Llama-3-8B-Instruct-abliterated-dpomix](https://huggingface.co/mlabonne/Llama-3-8B-Instruct-abliterated-dpomix) [📄](https://gist.github.com/mlabonne/d711548df70e2c04771cc68ab33fe2b9) \| 52.26 \| 41.6 \| 69.95 \| 54.22 \| 43.26 \| \| [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) [📄](https://gist.github.com/mlabonne/8329284d86035e6019edb11eb0933628) \| 51.34 \| 41.22 \| 69.86 \| 51.65 \| 42.64 \| \| [failspy/Meta-Llama-3-8B-Instruct-abliterated-v3](https://huggingface.co/failspy/Meta-Llama-3-8B-Instruct-abliterated-v3) [📄](https://gist.github.com/mlabonne/f46cce0262443365e4cce2b6fa7507fc) \| 51.21 \| 40.23 \| 69.5 \| 52.44 \| 42.69 \| \| [mlabonne/OrpoLlama-3-8B](https://huggingface.co/mlabonne/OrpoLlama-3-8B) [📄](https://gist.github.com/mlabonne/22896a1ae164859931cc8f4858c97f6f) \| 48.63 \| 34.17 \| 70.59 \| 52.39 \| 37.36 \| \| [meta-llama/Meta-Llama-3-8B](https://huggingface.co/meta-llama/Meta-Llama-3-8B) [📄](https://gist.github.com/mlabonne/616b6245137a9cfc4ea80e4c6e55d847) \| 45.42 \| 31.1 \| 69.95 \| 43.91 \| 36.7 \| ## 🌳 Model family tree ![image/png](https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/ekwRGgnjzEOyprT8sEBFt.png) ## 🧩 Configuration ```yaml models: - model: NousResearch/Meta-Llama-3-8B # No parameters necessary for base model - model: nbeerbower/llama-3-stella-8B parameters: density: 0.6 weight: 0.16 - model: Hastagaras/llama-3-8b-okay parameters: density: 0.56 weight: 0.1 - model: nbeerbower/llama-3-gutenberg-8B parameters: density: 0.6 weight: 0.18 - model: openchat/openchat-3.6-8b-20240522 parameters: density: 0.56 weight: 0.12 - model: Kukedlc/NeuralLLaMa-3-8b-DT-v0.1 parameters: density: 0.58 weight: 0.18 - model: cstr/llama3-8b-spaetzle-v20 parameters: density: 0.56 weight: 0.08 - model: mlabonne/ChimeraLlama-3-8B-v3 parameters: density: 0.56 weight: 0.08 - model: flammenai/Mahou-1.1-llama3-8B parameters: density: 0.55 weight: 0.05 - model: KingNish/KingNish-Llama3-8b parameters: density: 0.55 weight: 0.05 merge_method: dare_ties base_model: NousResearch/Meta-Llama-3-8B dtype: bfloat16 ```
HanlinLiao-Harry/Taxi-v3_Q-learning	HanlinLiao-Harry	2024-05-30T06:34:16Z	0	0	null	[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2024-05-30T06:34:14Z	--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: Taxi-v3_Q-learning results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.46 +/- 2.68 name: mean_reward verified: false --- # Q-Learning Agent playing1 Taxi-v3 This is a trained model of a Q-Learning agent playing Taxi-v3 . ## Usage ```python model = load_from_hub(repo_id="HanlinLiao-Harry/Taxi-v3_Q-learning", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```
HanlinLiao-Harry/q-FrozenLake-v1-4x4-noSlippery	HanlinLiao-Harry	2024-05-30T06:33:20Z	0	0	null	[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2024-05-30T06:33:18Z	--- tags: - FrozenLake-v1-4x4-no_slippery - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-FrozenLake-v1-4x4-noSlippery results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: FrozenLake-v1-4x4-no_slippery type: FrozenLake-v1-4x4-no_slippery metrics: - type: mean_reward value: 1.00 +/- 0.00 name: mean_reward verified: false --- # Q-Learning Agent playing1 FrozenLake-v1 This is a trained model of a Q-Learning agent playing FrozenLake-v1 . ## Usage ```python model = load_from_hub(repo_id="HanlinLiao-Harry/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```
IntellectusAI/zephyr_beta	IntellectusAI	2024-05-30T06:31:20Z	1	0	peft	[ "peft", "tensorboard", "safetensors", "trl", "sft", "generated_from_trainer", "base_model:TheBloke/zephyr-7B-alpha-GPTQ", "base_model:adapter:TheBloke/zephyr-7B-alpha-GPTQ", "license:mit", "region:us" ]	null	2024-05-14T06:24:11Z	--- license: mit library_name: peft tags: - trl - sft - generated_from_trainer base_model: TheBloke/zephyr-7B-alpha-GPTQ model-index: - name: zephyr_beta 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. --> [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/intellectus/huggingface/runs/xqhl9gft) # zephyr_beta This model is a fine-tuned version of [TheBloke/zephyr-7B-alpha-GPTQ](https://huggingface.co/TheBloke/zephyr-7B-alpha-GPTQ) 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: 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: cosine - training_steps: 200 - mixed_precision_training: Native AMP ### Training results ### Framework versions - PEFT 0.11.2.dev0 - Transformers 4.42.0.dev0 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
sebalnakji/gemma-ko-2b-it-02	sebalnakji	2024-05-30T06:30:51Z	146	0	transformers	[ "transformers", "safetensors", "gemma", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-30T06:26:11Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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NSC07/bloom-1b7-decodeSummary	NSC07	2024-05-30T06:29:02Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-30T06:28:54Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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RaphaelMourad/Mistral-Prot-v1-15M	RaphaelMourad	2024-05-30T06:14:21Z	194	0	transformers	[ "transformers", "safetensors", "mixtral", "text-generation", "pretrained", "mistral", "protein", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-15T08:37:31Z	--- license: apache-2.0 tags: - pretrained - mistral - protein --- # Model Card for Mistral-Prot-v1-15M (Mistral for protein) The Mistral-Prot-v1-15M Large Language Model (LLM) is a pretrained generative protein molecule model with 15.2M parameters. It is derived from Mixtral-8x7B-v0.1 model, which was simplified for protein: the number of layers and the hidden size were reduced. The model was pretrained using 10M protein strings from the uniprot 50 database. ## Model Architecture Like Mixtral-8x7B-v0.1, it is a transformer model, with the following architecture choices: - Grouped-Query Attention - Sliding-Window Attention - Byte-fallback BPE tokenizer - Mixture of Experts ## Load the model from huggingface: ``` import torch from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("RaphaelMourad/Mistral-Prot-v1-15M", trust_remote_code=True) model = AutoModel.from_pretrained("RaphaelMourad/Mistral-Prot-v1-15M", trust_remote_code=True) ``` ## Calculate the embedding of a protein sequence ``` insulin = "MALWMRLLPLLALLALWGPDPAAAFVNQHLCGSHLVEALYLVCGERGFFYTPKTRREAEDLQVGQVELGGGPGAGSLQPLALEGSLQKRGIVEQCCTSICSLYQLENYCN" inputs = tokenizer(insulin, return_tensors = 'pt')["input_ids"] hidden_states = model(inputs)[0] # [1, sequence_length, 256] # embedding with max pooling embedding_max = torch.max(hidden_states[0], dim=0)[0] print(embedding_max.shape) # expect to be 256 ``` ## Troubleshooting Ensure you are utilizing a stable version of Transformers, 4.34.0 or newer. ## Notice Mistral-Prot-v1-15M is a pretrained base model for protein. ## Contact Raphaël Mourad. [email protected]
HanlinLiao-Harry/ppo-LunarLander-v2	HanlinLiao-Harry	2024-05-30T06:13:21Z	1	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2024-05-30T06:13:01Z	--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 253.42 +/- 17.35 name: mean_reward verified: false --- # PPO Agent playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2 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 ... ```
RaphaelMourad/Mistral-Prot-v1-417M	RaphaelMourad	2024-05-30T06:12:44Z	193	0	transformers	[ "transformers", "safetensors", "mixtral", "text-generation", "pretrained", "mistral", "protein", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-30T06:10:27Z	--- license: apache-2.0 tags: - pretrained - mistral - protein --- # Model Card for Mistral-Prot-v1-417M (Mistral for protein) The Mistral-Prot-v1-417M Large Language Model (LLM) is a pretrained generative protein molecule model with 417M parameters. It is derived from Mixtral-8x7B-v0.1 model, which was simplified for protein: the number of layers and the hidden size were reduced. The model was pretrained using 10M protein strings from the uniprot 50 database. ## Model Architecture Like Mixtral-8x7B-v0.1, it is a transformer model, with the following architecture choices: - Grouped-Query Attention - Sliding-Window Attention - Byte-fallback BPE tokenizer - Mixture of Experts ## Load the model from huggingface: ``` import torch from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("RaphaelMourad/Mistral-Prot-v1-417M", trust_remote_code=True) model = AutoModel.from_pretrained("RaphaelMourad/Mistral-Prot-v1-417M", trust_remote_code=True) ``` ## Calculate the embedding of a protein sequence ``` insulin = "MALWMRLLPLLALLALWGPDPAAAFVNQHLCGSHLVEALYLVCGERGFFYTPKTRREAEDLQVGQVELGGGPGAGSLQPLALEGSLQKRGIVEQCCTSICSLYQLENYCN" inputs = tokenizer(insulin, return_tensors = 'pt')["input_ids"] hidden_states = model(inputs)[0] # [1, sequence_length, 256] # embedding with max pooling embedding_max = torch.max(hidden_states[0], dim=0)[0] print(embedding_max.shape) # expect to be 256 ``` ## Troubleshooting Ensure you are utilizing a stable version of Transformers, 4.34.0 or newer. ## Notice Mistral-Prot-v1-417M is a pretrained base model for protein. ## Contact Raphaël Mourad. [email protected]
yuminglin/my_awesome_model	yuminglin	2024-05-30T06:12:23Z	121	0	transformers	[ "transformers", "tensorboard", "safetensors", "distilbert", "text-classification", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-30T03:27:47Z	--- tags: - generated_from_trainer metrics: - accuracy model-index: - name: my_awesome_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. --> # my_awesome_model This model was trained from scratch on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5245 - Accuracy: 0.7569 ## 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: 2 ### Training results \| Training Loss \| Epoch \| Step \| Accuracy \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:--------:\|:---------------:\| \| No log \| 1.0 \| 388 \| 0.7460 \| 0.5238 \| \| 0.467 \| 2.0 \| 776 \| 0.5245 \| 0.7569 \| ### Framework versions - Transformers 4.41.1 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
Sadat07/phi-squad-1_5	Sadat07	2024-05-30T06:09:35Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-30T06:09:34Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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glouriousgautam/Mistral7b_v03_short_chat	glouriousgautam	2024-05-30T06:07:13Z	3	0	transformers	[ "transformers", "pytorch", "mistral", "text-generation", "text-generation-inference", "unsloth", "trl", "conversational", "en", "base_model:unsloth/mistral-7b-instruct-v0.3-bnb-4bit", "base_model:finetune:unsloth/mistral-7b-instruct-v0.3-bnb-4bit", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-05-29T17:33:51Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - mistral - trl base_model: unsloth/mistral-7b-instruct-v0.3-bnb-4bit datset_used: glouriousgautam/openhermes250k --- # Uploaded model - Developed by: glouriousgautam - License: apache-2.0 - Finetuned from model : unsloth/mistral-7b-instruct-v0.3-bnb-4bit An efficient instruction finetune of the Mistral-7B_0.3 in 4bit bnb for good performance with ~5GB of vram inference. This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
opalthailand/whisper_test	opalthailand	2024-05-30T06:05:27Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-30T06:05:26Z	--- 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. 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RichardErkhov/yentinglin_-_Taiwan-LLM-7B-v2.0-base-gguf	RichardErkhov	2024-05-30T06:04:06Z	15	0	null	[ "gguf", "arxiv:2311.17487", "endpoints_compatible", "region:us", "conversational" ]	null	2024-05-30T03:19:50Z	Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) Taiwan-LLM-7B-v2.0-base - GGUF - Model creator: https://huggingface.co/yentinglin/ - Original model: https://huggingface.co/yentinglin/Taiwan-LLM-7B-v2.0-base/ \| Name \| Quant method \| Size \| \| ---- \| ---- \| ---- \| \| [Taiwan-LLM-7B-v2.0-base.Q2_K.gguf](https://huggingface.co/RichardErkhov/yentinglin_-_Taiwan-LLM-7B-v2.0-base-gguf/blob/main/Taiwan-LLM-7B-v2.0-base.Q2_K.gguf) \| Q2_K \| 2.36GB \| \| [Taiwan-LLM-7B-v2.0-base.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/yentinglin_-_Taiwan-LLM-7B-v2.0-base-gguf/blob/main/Taiwan-LLM-7B-v2.0-base.IQ3_XS.gguf) \| IQ3_XS \| 2.6GB \| \| [Taiwan-LLM-7B-v2.0-base.IQ3_S.gguf](https://huggingface.co/RichardErkhov/yentinglin_-_Taiwan-LLM-7B-v2.0-base-gguf/blob/main/Taiwan-LLM-7B-v2.0-base.IQ3_S.gguf) \| IQ3_S \| 2.75GB \| \| [Taiwan-LLM-7B-v2.0-base.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/yentinglin_-_Taiwan-LLM-7B-v2.0-base-gguf/blob/main/Taiwan-LLM-7B-v2.0-base.Q3_K_S.gguf) \| Q3_K_S \| 2.75GB \| \| [Taiwan-LLM-7B-v2.0-base.IQ3_M.gguf](https://huggingface.co/RichardErkhov/yentinglin_-_Taiwan-LLM-7B-v2.0-base-gguf/blob/main/Taiwan-LLM-7B-v2.0-base.IQ3_M.gguf) \| IQ3_M \| 2.9GB \| \| [Taiwan-LLM-7B-v2.0-base.Q3_K.gguf](https://huggingface.co/RichardErkhov/yentinglin_-_Taiwan-LLM-7B-v2.0-base-gguf/blob/main/Taiwan-LLM-7B-v2.0-base.Q3_K.gguf) \| Q3_K \| 3.07GB \| \| [Taiwan-LLM-7B-v2.0-base.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/yentinglin_-_Taiwan-LLM-7B-v2.0-base-gguf/blob/main/Taiwan-LLM-7B-v2.0-base.Q3_K_M.gguf) \| Q3_K_M \| 3.07GB \| \| [Taiwan-LLM-7B-v2.0-base.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/yentinglin_-_Taiwan-LLM-7B-v2.0-base-gguf/blob/main/Taiwan-LLM-7B-v2.0-base.Q3_K_L.gguf) \| Q3_K_L \| 3.35GB \| \| [Taiwan-LLM-7B-v2.0-base.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/yentinglin_-_Taiwan-LLM-7B-v2.0-base-gguf/blob/main/Taiwan-LLM-7B-v2.0-base.IQ4_XS.gguf) \| IQ4_XS \| 3.4GB \| \| [Taiwan-LLM-7B-v2.0-base.Q4_0.gguf](https://huggingface.co/RichardErkhov/yentinglin_-_Taiwan-LLM-7B-v2.0-base-gguf/blob/main/Taiwan-LLM-7B-v2.0-base.Q4_0.gguf) \| Q4_0 \| 3.56GB \| \| [Taiwan-LLM-7B-v2.0-base.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/yentinglin_-_Taiwan-LLM-7B-v2.0-base-gguf/blob/main/Taiwan-LLM-7B-v2.0-base.IQ4_NL.gguf) \| IQ4_NL \| 3.58GB \| \| [Taiwan-LLM-7B-v2.0-base.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/yentinglin_-_Taiwan-LLM-7B-v2.0-base-gguf/blob/main/Taiwan-LLM-7B-v2.0-base.Q4_K_S.gguf) \| Q4_K_S \| 3.59GB \| \| [Taiwan-LLM-7B-v2.0-base.Q4_K.gguf](https://huggingface.co/RichardErkhov/yentinglin_-_Taiwan-LLM-7B-v2.0-base-gguf/blob/main/Taiwan-LLM-7B-v2.0-base.Q4_K.gguf) \| Q4_K \| 3.8GB \| \| [Taiwan-LLM-7B-v2.0-base.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/yentinglin_-_Taiwan-LLM-7B-v2.0-base-gguf/blob/main/Taiwan-LLM-7B-v2.0-base.Q4_K_M.gguf) \| Q4_K_M \| 3.8GB \| \| [Taiwan-LLM-7B-v2.0-base.Q4_1.gguf](https://huggingface.co/RichardErkhov/yentinglin_-_Taiwan-LLM-7B-v2.0-base-gguf/blob/main/Taiwan-LLM-7B-v2.0-base.Q4_1.gguf) \| Q4_1 \| 3.95GB \| \| [Taiwan-LLM-7B-v2.0-base.Q5_0.gguf](https://huggingface.co/RichardErkhov/yentinglin_-_Taiwan-LLM-7B-v2.0-base-gguf/blob/main/Taiwan-LLM-7B-v2.0-base.Q5_0.gguf) \| Q5_0 \| 4.33GB \| \| [Taiwan-LLM-7B-v2.0-base.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/yentinglin_-_Taiwan-LLM-7B-v2.0-base-gguf/blob/main/Taiwan-LLM-7B-v2.0-base.Q5_K_S.gguf) \| Q5_K_S \| 4.33GB \| \| [Taiwan-LLM-7B-v2.0-base.Q5_K.gguf](https://huggingface.co/RichardErkhov/yentinglin_-_Taiwan-LLM-7B-v2.0-base-gguf/blob/main/Taiwan-LLM-7B-v2.0-base.Q5_K.gguf) \| Q5_K \| 4.45GB \| \| [Taiwan-LLM-7B-v2.0-base.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/yentinglin_-_Taiwan-LLM-7B-v2.0-base-gguf/blob/main/Taiwan-LLM-7B-v2.0-base.Q5_K_M.gguf) \| Q5_K_M \| 4.45GB \| \| [Taiwan-LLM-7B-v2.0-base.Q5_1.gguf](https://huggingface.co/RichardErkhov/yentinglin_-_Taiwan-LLM-7B-v2.0-base-gguf/blob/main/Taiwan-LLM-7B-v2.0-base.Q5_1.gguf) \| Q5_1 \| 4.72GB \| \| [Taiwan-LLM-7B-v2.0-base.Q6_K.gguf](https://huggingface.co/RichardErkhov/yentinglin_-_Taiwan-LLM-7B-v2.0-base-gguf/blob/main/Taiwan-LLM-7B-v2.0-base.Q6_K.gguf) \| Q6_K \| 5.15GB \| \| [Taiwan-LLM-7B-v2.0-base.Q8_0.gguf](https://huggingface.co/RichardErkhov/yentinglin_-_Taiwan-LLM-7B-v2.0-base-gguf/blob/main/Taiwan-LLM-7B-v2.0-base.Q8_0.gguf) \| Q8_0 \| 6.67GB \| Original model description: --- license: apache-2.0 language: - zh widget: - text: "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: 你好，請問你可以幫我寫一封推薦信嗎？ ASSISTANT:" library_name: transformers pipeline_tag: text-generation extra_gated_heading: Acknowledge license to accept the repository. extra_gated_prompt: Please contact the author for access. extra_gated_button_content: Acknowledge license 同意以上內容 extra_gated_fields: Name: text Mail: text Organization: text Country: text Any utilization of the Taiwan LLM repository mandates the explicit acknowledgment and attribution to the original author: checkbox 使用Taiwan LLM必須明確地承認和歸功於優必達株式會社 Ubitus 以及原始作者: checkbox --- <img src="https://cdn-uploads.huggingface.co/production/uploads/5df9c78eda6d0311fd3d541f/CmusIT5OlSXvFrbTJ7l-C.png" alt="Taiwan LLM Logo" width="800" style="margin-left:'auto' margin-right:'auto' display:'block'"/> # 🌟 Checkout [Taiwan-LLM Demo Chat-UI](http://www.twllm.com) 🌟 # Model Card for Taiwan LLM 7B v2.0 base Taiwan LLM is an advanced language model tailored for Traditional Chinese, focusing on the linguistic and cultural contexts of Taiwan. Developed from a large base model, it's enriched with diverse Taiwanese textual sources and refined through Supervised Fine-Tuning. This model excels in language understanding and generation, aligning closely with Taiwan's cultural nuances. It demonstrates improved performance on various benchmarks like TC-Eval, showcasing its contextual comprehension and cultural relevance. For detailed insights into Taiwan LLM's development and features, refer to our [technical report](https://github.com/MiuLab/Taiwan-LLaMa/blob/main/twllm_paper.pdf). ## Model description - Model type: A 7B parameter GPT-like model fine-tuned on a mix of publicly available, synthetic datasets. - Language(s) (NLP): Primarily Traditional Chinese (zh-tw) - Finetuned from model: [meta-llama/Llama-2-7b-hf](https://huggingface.co/yentinglin/meta-llama/Llama-2-7b-hf) ### Model Sources <!-- Provide the basic links for the model. --> - Repository: https://github.com/MiuLab/Taiwan-LLaMa - Demo: https://twllm.com/ ## Performance ![image/png](https://cdn-uploads.huggingface.co/production/uploads/5df9c78eda6d0311fd3d541f/HTwIzw6RDha2-PhuWqSuI.png) ## Intended uses You should fine-tuned this model for instruction-following / chat application. ### Training hyperparameters ![image/png](https://cdn-uploads.huggingface.co/production/uploads/5df9c78eda6d0311fd3d541f/MdvHwdUvH-c926qyRAw7K.png) ![image/png](https://cdn-uploads.huggingface.co/production/uploads/5df9c78eda6d0311fd3d541f/kKpkvxDzOEyiAoTqmzRYO.png) ![image/png](https://cdn-uploads.huggingface.co/production/uploads/5df9c78eda6d0311fd3d541f/FsnlJ_fkRxf7fn5RKZnjE.png) The following hyperparameters were used during training: - learning_rate: 5e-05 - distributed_type: multi-GPU - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.03 - num_epochs: 5.0 ## Citation If you find Taiwan LLM is useful in your work, please cite it with: ``` @misc{lin2023taiwan, title={Taiwan LLM: Bridging the Linguistic Divide with a Culturally Aligned Language Model}, author={Yen-Ting Lin and Yun-Nung Chen}, year={2023}, eprint={2311.17487}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` # Acknowledgement Taiwan LLM v2 is conducted in collaboration with [Ubitus K.K.](http://ubitus.net). Ubitus provides valuable compute resources for the project.
coconana/Qwen-Qwen1.5-0.5B-1717048629	coconana	2024-05-30T06:03:44Z	146	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-30T05:57:12Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
PaawanPurdhani/Finetuned	PaawanPurdhani	2024-05-30T06:02:55Z	126	0	transformers	[ "transformers", "pytorch", "bert", "fill-mask", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2024-05-30T05:59:56Z	--- license: apache-2.0 ---
Madan1512/Driver_Drowsiness_Detection	Madan1512	2024-05-30T05:57:58Z	0	0	diffusers	[ "diffusers", "text-to-image", "stable-diffusion", "lora", "template:sd-lora", "object-detection", "base_model:runwayml/stable-diffusion-v1-5", "base_model:adapter:runwayml/stable-diffusion-v1-5", "region:us" ]	object-detection	2024-05-30T05:52:49Z	--- tags: - text-to-image - stable-diffusion - lora - diffusers - template:sd-lora widget: - text: '-' output: url: images/detect.png base_model: runwayml/stable-diffusion-v1-5 instance_prompt: null metrics: - accuracy pipeline_tag: object-detection --- # Driver Drowsiness Detection <Gallery /> ## Download model Weights for this model are available in PyTorch format. [Download](/Madan1512/Driver_Drowsiness_Detection/tree/main) them in the Files & versions tab.
MelitaCruces/Llama3-gsm8k-100	MelitaCruces	2024-05-30T05:53:31Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:unsloth/llama-3-8b-bnb-4bit", "base_model:finetune:unsloth/llama-3-8b-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-05-30T05:53:23Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl base_model: unsloth/llama-3-8b-bnb-4bit --- # Uploaded model - Developed by: MelitaCruces - License: apache-2.0 - Finetuned from model : unsloth/llama-3-8b-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
squeeze-ai-lab/TinyAgent-7B	squeeze-ai-lab	2024-05-30T05:50:08Z	25	3	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "function calling", "on-device language model", "en", "autotrain_compatible", "text-generation-inference", "region:us" ]	text-generation	2024-05-27T18:48:38Z	--- library_name: transformers model-index: - name: TinyAgent-7B results: [] tags: - function calling - on-device language model inference: false space: false spaces: false language: - en --- # TinyAgent: Function Calling at the Edge <p align="center"> <a href="https://github.com/SqueezeAILab/TinyAgent/raw/main/TinyAgent.zip">Get the desktop app</a>‎ ‎ \| <a href="https://bair.berkeley.edu/blog/2024/05/29/tiny-agent/">Read the blog post</a> </p> ![Thumbnail](https://cdn-uploads.huggingface.co/production/uploads/648903e1ce7b9a2abe3511aa/a1YuQosFiJQJ_7Ejribrd.png) TinyAgent aims to enable complex reasoning and function calling capabilities in Small Language Models (SLMs) that can be deployed securely and privately at the edge. Traditional Large Language Models (LLMs) like GPT-4 and Gemini-1.5, while powerful, are often too large and resource-intensive for edge deployment, posing challenges in terms of privacy, connectivity, and latency. TinyAgent addresses these challenges by training specialized SLMs with high-quality, curated data, and focusing on function calling with [LLMCompiler](https://github.com/SqueezeAILab/LLMCompiler). As a driving application, TinyAgent can interact with various MacOS applications, assisting users with day-to-day tasks such as composing emails, managing contacts, scheduling calendar events, and organizing Zoom meetings. Model Developers: Squeeze AI Lab at University of California, Berkeley. Variations: TinyAgent models come in 2 sizes: TinyAgent-1.1B and TinyAgent-7B License: MIT ## Demo <a href="https://youtu.be/0GvaGL9IDpQ" target="_blank" rel="noopener noreferrer"> <img src="https://cdn-uploads.huggingface.co/production/uploads/648903e1ce7b9a2abe3511aa/BpN-zPzfqa8wcRuJiYOYC.png" alt="TinyAgent Demo" width="700"> </a> ## How to Use Please see our [Github](https://github.com/SqueezeAILab/TinyAgent) for details on how to use TinyAgent models. TinyAgent models can be used programmatically or through our user interface. ## Training Details Dataset: We curated a [dataset](https://huggingface.co/datasets/squeeze-ai-lab/TinyAgent-dataset) of 40,000 real-life use cases. We use GPT-3.5-Turbo to generate real-world instructions. These are then used to obtain synthetic execution plans using GPT-4-Turbo. Please check out our [blog post](https://bair.berkeley.edu/blog/2024/05/29/tiny-agent/) for more details on our dataset. Fine-tuning Procedure: TinyAgent models are fine-tuned from base models. Below is a table of each TinyAgent model with its base counterpart \| Model \| Success Rate \| \| ----------------------------------------------------------------------------------------------------------------------------------------------------------- \| ------------ \| \| GPT-3.5-turbo \| 65.04% \| \| GPT-4-turbo \| 79.08% \| \| [TinyLLama-1.1B-32K-Instruct](https://huggingface.co/Doctor-Shotgun/TinyLlama-1.1B-32k-Instruct) \| 12.71% \| \| [WizardLM-2-7b](https://huggingface.co/MaziyarPanahi/WizardLM-2-7B-GGUF) \| 41.25% \| \| TinyAgent-1.1B + ToolRAG / [[hf](https://huggingface.co/squeeze-ai-lab/TinyAgent-1.1B)] [[gguf](https://huggingface.co/squeeze-ai-lab/TinyAgent-1.1B-GGUF)] \| 80.06% \| \| TinyAgent-7B + ToolRAG / [[hf](https://huggingface.co/squeeze-ai-lab/TinyAgent-7B)] [[gguf](https://huggingface.co/squeeze-ai-lab/TinyAgent-7B-GGUF)] \| 84.95% \| Using the synthetic data generation process described above, we use parameter-efficient fine-tuning with LoRA to fine-tune the base models for 3 epochs. Please check out our [blog post](https://bair.berkeley.edu/blog/2024/05/29/tiny-agent/) for more details on our fine-tuning procedure. ### 🛠️ ToolRAG When faced with challenging tasks, SLM agents require appropriate tools and in-context examples to guide them. If the model sees irrelevant examples, it can hallucinate. Likewise, if the model sees the descriptions of the tools that it doesn’t need, it usually gets confused, and these tools take up unnecessary prompt space. To tackle this, TinyAgent uses ToolRAG to retrieve the best tools and examples suited for a given query. This process has minimal latency and increases the accuracy of TinyAgent substantially. Please take a look at our [blog post](https://bair.berkeley.edu/blog/2024/05/29/tiny-agent/) and our [ToolRAG model](https://huggingface.co/squeeze-ai-lab/TinyAgent-ToolRAG) for more details. ## Links Blog Post: https://bair.berkeley.edu/blog/2024/05/29/tiny-agent/ Github: https://github.com/SqueezeAILab/TinyAgent
Emptier8126/PPO-LunarLander-v2	Emptier8126	2024-05-30T05:50:04Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2024-02-18T14:02:09Z	--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 278.90 +/- 22.20 name: mean_reward verified: false --- # PPO Agent playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2 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 ... ```
squeeze-ai-lab/TinyAgent-1.1B-GGUF	squeeze-ai-lab	2024-05-30T05:47:31Z	119	6	transformers	[ "transformers", "gguf", "function calling", "on-device language model", "en", "region:us", "conversational" ]	null	2024-05-27T19:53:06Z	--- library_name: transformers model-index: - name: TinyAgent-1.1B-GGUF results: [] tags: - function calling - on-device language model inference: false space: false spaces: false language: - en --- # TinyAgent: Function Calling at the Edge <p align="center"> <a href="https://github.com/SqueezeAILab/TinyAgent/raw/main/TinyAgent.zip">Get the desktop app</a>‎ ‎ \| <a href="https://bair.berkeley.edu/blog/2024/05/29/tiny-agent/">Read the blog post</a> </p> ![Thumbnail](https://cdn-uploads.huggingface.co/production/uploads/648903e1ce7b9a2abe3511aa/a1YuQosFiJQJ_7Ejribrd.png) TinyAgent aims to enable complex reasoning and function calling capabilities in Small Language Models (SLMs) that can be deployed securely and privately at the edge. Traditional Large Language Models (LLMs) like GPT-4 and Gemini-1.5, while powerful, are often too large and resource-intensive for edge deployment, posing challenges in terms of privacy, connectivity, and latency. TinyAgent addresses these challenges by training specialized SLMs with high-quality, curated data, and focusing on function calling with [LLMCompiler](https://github.com/SqueezeAILab/LLMCompiler). As a driving application, TinyAgent can interact with various MacOS applications, assisting users with day-to-day tasks such as composing emails, managing contacts, scheduling calendar events, and organizing Zoom meetings. Model Developers: Squeeze AI Lab at University of California, Berkeley. Variations: TinyAgent models come in 2 sizes: TinyAgent-1.1B and TinyAgent-7B License: MIT ## Demo <a href="https://youtu.be/0GvaGL9IDpQ" target="_blank" rel="noopener noreferrer"> <img src="https://cdn-uploads.huggingface.co/production/uploads/648903e1ce7b9a2abe3511aa/BpN-zPzfqa8wcRuJiYOYC.png" alt="TinyAgent Demo" width="700"> </a> ## How to Use Please see our [Github](https://github.com/SqueezeAILab/TinyAgent) for details on how to use TinyAgent models. TinyAgent models can be used programmatically or through our user interface. ## Training Details Dataset: We curated a [dataset](https://huggingface.co/datasets/squeeze-ai-lab/TinyAgent-dataset) of 40,000 real-life use cases. We use GPT-3.5-Turbo to generate real-world instructions. These are then used to obtain synthetic execution plans using GPT-4-Turbo. Please check out our [blog post](https://bair.berkeley.edu/blog/2024/05/29/tiny-agent/) for more details on our dataset. Fine-tuning Procedure: TinyAgent models are fine-tuned from base models. Below is a table of each TinyAgent model with its base counterpart \| Model \| Success Rate \| \| ----------------------------------------------------------------------------------------------------------------------------------------------------------- \| ------------ \| \| GPT-3.5-turbo \| 65.04% \| \| GPT-4-turbo \| 79.08% \| \| [TinyLLama-1.1B-32K-Instruct](https://huggingface.co/Doctor-Shotgun/TinyLlama-1.1B-32k-Instruct) \| 12.71% \| \| [WizardLM-2-7b](https://huggingface.co/MaziyarPanahi/WizardLM-2-7B-GGUF) \| 41.25% \| \| TinyAgent-1.1B + ToolRAG / [[hf](https://huggingface.co/squeeze-ai-lab/TinyAgent-1.1B)] [[gguf](https://huggingface.co/squeeze-ai-lab/TinyAgent-1.1B-GGUF)] \| 80.06% \| \| TinyAgent-7B + ToolRAG / [[hf](https://huggingface.co/squeeze-ai-lab/TinyAgent-7B)] [[gguf](https://huggingface.co/squeeze-ai-lab/TinyAgent-7B-GGUF)] \| 84.95% \| Using the synthetic data generation process described above, we use parameter-efficient fine-tuning with LoRA to fine-tune the base models for 3 epochs. Please check out our [blog post](https://bair.berkeley.edu/blog/2024/05/29/tiny-agent/) for more details on our fine-tuning procedure. ### 🛠️ ToolRAG When faced with challenging tasks, SLM agents require appropriate tools and in-context examples to guide them. If the model sees irrelevant examples, it can hallucinate. Likewise, if the model sees the descriptions of the tools that it doesn’t need, it usually gets confused, and these tools take up unnecessary prompt space. To tackle this, TinyAgent uses ToolRAG to retrieve the best tools and examples suited for a given query. This process has minimal latency and increases the accuracy of TinyAgent substantially. Please take a look at our [blog post](https://bair.berkeley.edu/blog/2024/05/29/tiny-agent/) and our [ToolRAG model](https://huggingface.co/squeeze-ai-lab/TinyAgent-ToolRAG) for more details. ## Links Blog Post: https://bair.berkeley.edu/blog/2024/05/29/tiny-agent/ Github: https://github.com/SqueezeAILab/TinyAgent
squeeze-ai-lab/TinyAgent-ToolRAG	squeeze-ai-lab	2024-05-30T05:47:12Z	118	15	transformers	[ "transformers", "safetensors", "deberta-v2", "text-classification", "function calling", "on-device language model", "en", "autotrain_compatible", "region:us" ]	text-classification	2024-05-27T07:26:44Z	--- library_name: transformers model-index: - name: TinyAgent-ToolRAG results: [] tags: - function calling - on-device language model inference: false space: false spaces: false language: - en --- # TinyAgent: Function Calling at the Edge <p align="center"> <a href="https://github.com/SqueezeAILab/TinyAgent/raw/main/TinyAgent.zip">Get the desktop app</a>‎ ‎ \| <a href="https://bair.berkeley.edu/blog/2024/05/29/tiny-agent/">Read the blog post</a> </p> ![Thumbnail](https://cdn-uploads.huggingface.co/production/uploads/648903e1ce7b9a2abe3511aa/a1YuQosFiJQJ_7Ejribrd.png) TinyAgent aims to enable complex reasoning and function calling capabilities in Small Language Models (SLMs) that can be deployed securely and privately at the edge. Traditional Large Language Models (LLMs) like GPT-4 and Gemini-1.5, while powerful, are often too large and resource-intensive for edge deployment, posing challenges in terms of privacy, connectivity, and latency. TinyAgent addresses these challenges by training specialized SLMs with high-quality, curated data, and focusing on function calling with [LLMCompiler](https://github.com/SqueezeAILab/LLMCompiler). As a driving application, TinyAgent can interact with various MacOS applications, assisting users with day-to-day tasks such as composing emails, managing contacts, scheduling calendar events, and organizing Zoom meetings. When faced with challenging tasks, SLM agents require appropriate tools and in-context examples to guide them. If the model sees irrelevant examples, it can hallucinate. Likewise, if the model sees the descriptions of the tools that it doesn’t need, it usually gets confused, and these tools take up unnecessary prompt space. To tackle this, TinyAgent uses ToolRAG to retrieve the best tools and examples suited for a given query. This process has minimal latency and increases the accuracy of TinyAgent substantially. Please take a look at our [blog post](https://bair.berkeley.edu/blog/2024/05/29/tiny-agent/) for more details. Model Developers: Squeeze AI Lab at University of California, Berkeley. Variations: TinyAgent models come in 2 sizes: TinyAgent-1.1B and TinyAgent-7B License: MIT ## Demo <a href="https://youtu.be/0GvaGL9IDpQ" target="_blank" rel="noopener noreferrer"> <img src="https://cdn-uploads.huggingface.co/production/uploads/648903e1ce7b9a2abe3511aa/BpN-zPzfqa8wcRuJiYOYC.png" alt="TinyAgent Demo" width="700"> </a> ## How to Use Please see our [Github](https://github.com/SqueezeAILab/TinyAgent) for details on how to use TinyAgent models. TinyAgent models can be used programmatically or through our user interface. ## Training Details Dataset: We curated a [dataset](https://huggingface.co/datasets/squeeze-ai-lab/TinyAgent-dataset) of 40,000 real-life use cases. We use GPT-3.5-Turbo to generate real-world instructions. These are then used to obtain synthetic execution plans using GPT-4-Turbo. Please check out our [blog post](https://bair.berkeley.edu/blog/2024/05/29/tiny-agent/) for more details on our dataset. Fine-tuning Procedure: TinyAgent models are fine-tuned from base models. Below is a table of each TinyAgent model with its base counterpart \| Model \| Success Rate \| \| ----------------------------------------------------------------------------------------------------------------------------------------------------------- \| ------------ \| \| GPT-3.5-turbo \| 65.04% \| \| GPT-4-turbo \| 79.08% \| \| [TinyLLama-1.1B-32K-Instruct](https://huggingface.co/Doctor-Shotgun/TinyLlama-1.1B-32k-Instruct) \| 12.71% \| \| [WizardLM-2-7b](https://huggingface.co/MaziyarPanahi/WizardLM-2-7B-GGUF) \| 41.25% \| \| TinyAgent-1.1B + ToolRAG / [[hf](https://huggingface.co/squeeze-ai-lab/TinyAgent-1.1B)] [[gguf](https://huggingface.co/squeeze-ai-lab/TinyAgent-1.1B-GGUF)] \| 80.06% \| \| TinyAgent-7B + ToolRAG / [[hf](https://huggingface.co/squeeze-ai-lab/TinyAgent-7B)] [[gguf](https://huggingface.co/squeeze-ai-lab/TinyAgent-7B-GGUF)] \| 84.95% \| Using the synthetic data generation process described above, we use parameter-efficient fine-tuning with LoRA to fine-tune the base models for 3 epochs. Please check out our [blog post](https://bair.berkeley.edu/blog/2024/05/29/tiny-agent/) for more details on our fine-tuning procedure. ## Links Blog Post: https://bair.berkeley.edu/blog/2024/05/29/tiny-agent/ Github: https://github.com/SqueezeAILab/TinyAgent
subhavarshith/donut_exp1e-5	subhavarshith	2024-05-30T05:41:31Z	9	0	transformers	[ "transformers", "safetensors", "vision-encoder-decoder", "image-text-to-text", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	image-text-to-text	2024-05-29T10:43:04Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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hanzohazashi1/lora_model	hanzohazashi1	2024-05-30T05:31:34Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:meta-llama/Meta-Llama-3-8B-Instruct", "base_model:finetune:meta-llama/Meta-Llama-3-8B-Instruct", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-05-30T05:31:29Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl base_model: meta-llama/Meta-Llama-3-8B-Instruct --- # Uploaded model - Developed by: hanzohazashi1 - License: apache-2.0 - Finetuned from model : meta-llama/Meta-Llama-3-8B-Instruct This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
mradermacher/POLAR-10.7B-HES-DPO-v0.1-GGUF	mradermacher	2024-05-30T05:29:19Z	16	0	transformers	[ "transformers", "gguf", "trl", "dpo", "ko", "base_model:haes95/POLAR-10.7B-HES-DPO-v0.1", "base_model:quantized:haes95/POLAR-10.7B-HES-DPO-v0.1", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-05-30T04:50:30Z	--- base_model: haes95/POLAR-10.7B-HES-DPO-v0.1 language: - ko library_name: transformers license: apache-2.0 quantized_by: mradermacher tags: - trl - dpo --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/haes95/POLAR-10.7B-HES-DPO-v0.1 <!-- 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/POLAR-10.7B-HES-DPO-v0.1-GGUF/resolve/main/POLAR-10.7B-HES-DPO-v0.1.Q2_K.gguf) \| Q2_K \| 4.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/POLAR-10.7B-HES-DPO-v0.1-GGUF/resolve/main/POLAR-10.7B-HES-DPO-v0.1.IQ3_XS.gguf) \| IQ3_XS \| 4.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/POLAR-10.7B-HES-DPO-v0.1-GGUF/resolve/main/POLAR-10.7B-HES-DPO-v0.1.Q3_K_S.gguf) \| Q3_K_S \| 4.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/POLAR-10.7B-HES-DPO-v0.1-GGUF/resolve/main/POLAR-10.7B-HES-DPO-v0.1.IQ3_S.gguf) \| IQ3_S \| 4.8 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/POLAR-10.7B-HES-DPO-v0.1-GGUF/resolve/main/POLAR-10.7B-HES-DPO-v0.1.IQ3_M.gguf) \| IQ3_M \| 4.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/POLAR-10.7B-HES-DPO-v0.1-GGUF/resolve/main/POLAR-10.7B-HES-DPO-v0.1.Q3_K_M.gguf) \| Q3_K_M \| 5.3 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/POLAR-10.7B-HES-DPO-v0.1-GGUF/resolve/main/POLAR-10.7B-HES-DPO-v0.1.Q3_K_L.gguf) \| Q3_K_L \| 5.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/POLAR-10.7B-HES-DPO-v0.1-GGUF/resolve/main/POLAR-10.7B-HES-DPO-v0.1.IQ4_XS.gguf) \| IQ4_XS \| 5.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/POLAR-10.7B-HES-DPO-v0.1-GGUF/resolve/main/POLAR-10.7B-HES-DPO-v0.1.Q4_K_S.gguf) \| Q4_K_S \| 6.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/POLAR-10.7B-HES-DPO-v0.1-GGUF/resolve/main/POLAR-10.7B-HES-DPO-v0.1.Q4_K_M.gguf) \| Q4_K_M \| 6.6 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/POLAR-10.7B-HES-DPO-v0.1-GGUF/resolve/main/POLAR-10.7B-HES-DPO-v0.1.Q5_K_S.gguf) \| Q5_K_S \| 7.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/POLAR-10.7B-HES-DPO-v0.1-GGUF/resolve/main/POLAR-10.7B-HES-DPO-v0.1.Q5_K_M.gguf) \| Q5_K_M \| 7.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/POLAR-10.7B-HES-DPO-v0.1-GGUF/resolve/main/POLAR-10.7B-HES-DPO-v0.1.Q6_K.gguf) \| Q6_K \| 8.9 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/POLAR-10.7B-HES-DPO-v0.1-GGUF/resolve/main/POLAR-10.7B-HES-DPO-v0.1.Q8_0.gguf) \| Q8_0 \| 11.5 \| 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 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## 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 -->
newsletter/wavecoder-ultra-6.7b-Q6_K-GGUF	newsletter	2024-05-30T05:25:29Z	1	0	transformers	[ "transformers", "gguf", "code", "llama-cpp", "gguf-my-repo", "text-generation", "en", "dataset:humaneval", "license:mit", "endpoints_compatible", "region:us", "conversational" ]	text-generation	2024-05-27T08:48:35Z	--- language: - en license: mit library_name: transformers tags: - code - llama-cpp - gguf-my-repo datasets: - humaneval metrics: - code_eval license_link: https://huggingface.co/microsoft/wavecoder-ultra-6.7b/blob/main/LICENSE pipeline_tag: text-generation --- # newsletter/wavecoder-ultra-6.7b-Q6_K-GGUF This model was converted to GGUF format from [`microsoft/wavecoder-ultra-6.7b`](https://huggingface.co/microsoft/wavecoder-ultra-6.7b) 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/microsoft/wavecoder-ultra-6.7b) 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 newsletter/wavecoder-ultra-6.7b-Q6_K-GGUF --model wavecoder-ultra-6.7b-q6_k.gguf -p "The meaning to life and the universe is" ``` Server: ```bash llama-server --hf-repo newsletter/wavecoder-ultra-6.7b-Q6_K-GGUF --model wavecoder-ultra-6.7b-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 wavecoder-ultra-6.7b-q6_k.gguf -n 128 ```
jsfs11/WestOrcaMonarch-DPO-7B	jsfs11	2024-05-30T05:15:02Z	6	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "axolotl", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-30T04:57:01Z	--- license: apache-2.0 tags: - axolotl --- [<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl) This model is a fine-tuned version of [jsfs11/WestOrcaNeuralMarco-DPO-v2-DARETIES-7B](https://huggingface.co/jsfs11/WestOrcaNeuralMarco-DPO-v2-DARETIES-7B) on the OpenHermes2.5-dpo-binarized-alpha dataset. ### The following hyperparameters were used during training: - base_model: jsfs11/WestOrcaNeuralMarco-DPO-v2-DARETIES-7B model_type: MistralForCausalLM tokenizer_type: LlamaTokenizer is_mistral_derived_model: true load_in_8bit: false load_in_4bit: true strict: false rl: dpo chat_template: chatml datasets: - path: mlabonne/chatml-OpenHermes2.5-dpo-binarized-alpha split: train type: chatml.intel dataset_prepared_path: val_set_size: 0.01 output_dir: ./out adapter: qlora lora_model_dir: sequence_len: 1800 sample_packing: false pad_to_sequence_len: false lora_r: 32 lora_alpha: 32 lora_dropout: 0.05 lora_target_linear: true lora_fan_in_fan_out: lora_target_modules: wandb_project: axolotl wandb_entity: wandb_watch: wandb_name: wandb_log_model: gradient_accumulation_steps: 8 micro_batch_size: 1 num_epochs: 1 optimizer: paged_adamw_32bit lr_scheduler: cosine learning_rate: 5e-7 train_on_inputs: false group_by_length: false bf16: true fp16: false tf32: true gradient_checkpointing: true early_stopping_patience: resume_from_checkpoint: local_rank: logging_steps: 1 xformers_attention: flash_attention: true warmup_steps: 100 evals_per_epoch: 1 eval_table_size: eval_table_max_new_tokens: 128 save_steps: 1080 max_steps: 1080 debug: deepspeed: weight_decay: 0.0 fsdp: fsdp_config: special_tokens: ### Training results "train/loss": 0.4733, "train/grad_norm": 15.831088066101074, "train/learning_rate": 0, "train/rewards/chosen": -0.6122800707817078, "train/rewards/rejected": -1.650345802307129, "train/rewards/accuracies": 0.875, "train/rewards/margins": 1.0380656719207764, "train/logps/rejected": -379.778564453125, "train/logps/chosen": -250.2126007080078, "train/logits/rejected": -2.0232465267181396, "train/logits/chosen": -2.1629369258880615, "train/epoch": 2.08594881699662, "train/global_step": 1080, "_timestamp": 1717044966.608197, "_runtime": 12949.461512088776, "_step": 1080, "train_runtime": 12950.5619, "train_samples_per_second": 1.334, "train_steps_per_second": 0.083, "total_flos": 0, "train_loss": 0.560937881635295, ###
carpit680/ppo-Huggy	carpit680	2024-05-30T05:14:52Z	6	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "Huggy", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy", "region:us" ]	reinforcement-learning	2024-05-30T05:10:41Z	--- library_name: ml-agents tags: - Huggy - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Huggy --- # ppo Agent playing Huggy This is a trained model of a ppo agent playing Huggy 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: carpit680/ppo-Huggy 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
DokHee/Llama-3-Open-Ko-8B-Instruct-VBiom-V1-gguf	DokHee	2024-05-30T05:09:52Z	9	0	transformers	[ "transformers", "gguf", "llama", "text-generation-inference", "unsloth", "en", "base_model:beomi/Llama-3-Open-Ko-8B", "base_model:quantized:beomi/Llama-3-Open-Ko-8B", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2024-05-30T04:22:55Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - gguf base_model: beomi/Llama-3-Open-Ko-8B --- # Uploaded model - Developed by: DokHee - License: apache-2.0 - Finetuned from model : beomi/Llama-3-Open-Ko-8B This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
M00dler/whisper-small-malay	M00dler	2024-05-30T05:07:33Z	90	0	transformers	[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "generated_from_trainer", "my", "dataset:malaysia-ai/malay-conversational-speech-corpus", "base_model:openai/whisper-small", "base_model:finetune:openai/whisper-small", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2024-05-27T06:28:17Z	--- language: - my license: apache-2.0 base_model: openai/whisper-small tags: - generated_from_trainer datasets: - malaysia-ai/malay-conversational-speech-corpus metrics: - wer model-index: - name: Whisper small Malay (4 batch size) - Gab results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: malay-conversational-speech-corpus type: malaysia-ai/malay-conversational-speech-corpus args: 'config: malay, split: test' metrics: - name: Wer type: wer value: 27.394540942928042 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Whisper small Malay (4 batch size) - Gab This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the malay-conversational-speech-corpus dataset. It achieves the following results on the evaluation set: - Loss: 0.7126 - Wer: 27.3945 ## 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: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 4000 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-------:\|:----:\|:---------------:\|:-------:\| \| 0.0217 \| 6.1728 \| 1000 \| 0.5993 \| 28.8586 \| \| 0.0013 \| 12.3457 \| 2000 \| 0.6816 \| 28.0397 \| \| 0.0003 \| 18.5185 \| 3000 \| 0.7018 \| 27.8660 \| \| 0.0002 \| 24.6914 \| 4000 \| 0.7126 \| 27.3945 \| ### Framework versions - Transformers 4.41.1 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
samim2024/llama2test1	samim2024	2024-05-30T05:01:28Z	2	0	peft	[ "peft", "pytorch", "llama", "region:us" ]	null	2024-05-30T04:49:05Z	--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float16 ### Framework versions - PEFT 0.4.0
QuantFactory/Llama-3-Instruct-8B-DPO-GGUF	QuantFactory	2024-05-30T04:55:44Z	56	0	transformers	[ "transformers", "gguf", "text-generation", "arxiv:2405.14734", "base_model:princeton-nlp/Llama-3-Instruct-8B-DPO", "base_model:quantized:princeton-nlp/Llama-3-Instruct-8B-DPO", "endpoints_compatible", "region:us" ]	text-generation	2024-05-30T03:42:50Z	--- library_name: transformers pipeline_tag: text-generation base_model: princeton-nlp/Llama-3-Instruct-8B-DPO --- # QuantFactory/Llama-3-Instruct-8B-DPO-GGUF This is quantized version of [princeton-nlp/Llama-3-Instruct-8B-DPO](https://huggingface.co/princeton-nlp/Llama-3-Instruct-8B-DPO) created using llama.cpp # Model Description This is a model released from the preprint: [SimPO: Simple Preference Optimization with a Reference-Free Reward](https://arxiv.org/abs/2405.14734) Please refer to our [repository](https://github.com/princeton-nlp/SimPO) for more details.
HyperdustProtocol/HyperAutoGGUF-q4	HyperdustProtocol	2024-05-30T04:45:43Z	6	0	transformers	[ "transformers", "gguf", "llama", "text-generation-inference", "unsloth", "en", "base_model:unsloth/llama-2-7b-bnb-4bit", "base_model:quantized:unsloth/llama-2-7b-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-05-30T04:32:01Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - gguf base_model: unsloth/llama-2-7b-bnb-4bit --- # Uploaded model - Developed by: HyperdustProtocol - License: apache-2.0 - Finetuned from model : unsloth/llama-2-7b-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
mradermacher/Llama-3-Neurona-8b-GGUF	mradermacher	2024-05-30T04:45:03Z	114	0	transformers	[ "transformers", "gguf", "synthetic", "es", "en", "dataset:pinzhenchen/alpaca-cleaned-es", "dataset:Danielbrdz/Barcenas-Economia", "dataset:HiTZ/casimedicos-exp", "dataset:somosnlp/coser_resumenes", "dataset:csebuetnlp/CrossSum", "dataset:Iker/Document-Translation-en-es", "dataset:somosnlp/es-inclusive-language-it", "dataset:FreedomIntelligence/evol-instruct-spanish", "dataset:glaiveai/glaive-code-assistant-v3", "dataset:glaiveai/glaive-function-calling-v2", "dataset:Iker/InstructTranslation-EN-ES", "dataset:somosnlp/lenguaje-claro-dataset", "dataset:somosnlp/LingComp_QA", "dataset:bltlab/lr-sum", "dataset:Iker/NoticIA", "dataset:xaviviro/oasst2_es_gpt", "dataset:teknium/OpenHermes-2.5", "dataset:Iker/OpenHermes-2.5-Spanish", "dataset:Helsinki-NLP/opus-100", "dataset:projecte-aina/RAG_Multilingual", "dataset:sem_eval_2018_task_1", "dataset:davidstap/ted_talks", "dataset:HiTZ/This-is-not-a-dataset", "dataset:wikipedia", "base_model:Iker/Llama-3-Neurona-8b", "base_model:quantized:Iker/Llama-3-Neurona-8b", "license:llama3", "endpoints_compatible", "region:us", "conversational" ]	null	2024-05-23T10:33:31Z	--- base_model: Iker/Llama-3-Neurona-8b datasets: - pinzhenchen/alpaca-cleaned-es - Danielbrdz/Barcenas-Economia - HiTZ/casimedicos-exp - somosnlp/coser_resumenes - csebuetnlp/CrossSum - Iker/Document-Translation-en-es - somosnlp/es-inclusive-language-it - FreedomIntelligence/evol-instruct-spanish - glaiveai/glaive-code-assistant-v3 - glaiveai/glaive-function-calling-v2 - Iker/InstructTranslation-EN-ES - somosnlp/lenguaje-claro-dataset - somosnlp/LingComp_QA - bltlab/lr-sum - Iker/NoticIA - xaviviro/oasst2_es_gpt - teknium/OpenHermes-2.5 - Iker/OpenHermes-2.5-Spanish - Helsinki-NLP/opus-100 - projecte-aina/RAG_Multilingual - sem_eval_2018_task_1 - davidstap/ted_talks - HiTZ/This-is-not-a-dataset - wikipedia language: - es - en library_name: transformers license: llama3 quantized_by: mradermacher tags: - synthetic --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> static quants of https://huggingface.co/Iker/Llama-3-Neurona-8b <!-- 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/Llama-3-Neurona-8b-GGUF/resolve/main/Llama-3-Neurona-8b.Q2_K.gguf) \| Q2_K \| 3.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3-Neurona-8b-GGUF/resolve/main/Llama-3-Neurona-8b.IQ3_XS.gguf) \| IQ3_XS \| 3.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3-Neurona-8b-GGUF/resolve/main/Llama-3-Neurona-8b.Q3_K_S.gguf) \| Q3_K_S \| 3.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3-Neurona-8b-GGUF/resolve/main/Llama-3-Neurona-8b.IQ3_S.gguf) \| IQ3_S \| 3.8 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3-Neurona-8b-GGUF/resolve/main/Llama-3-Neurona-8b.IQ3_M.gguf) \| IQ3_M \| 3.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3-Neurona-8b-GGUF/resolve/main/Llama-3-Neurona-8b.Q3_K_M.gguf) \| Q3_K_M \| 4.1 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3-Neurona-8b-GGUF/resolve/main/Llama-3-Neurona-8b.Q3_K_L.gguf) \| Q3_K_L \| 4.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3-Neurona-8b-GGUF/resolve/main/Llama-3-Neurona-8b.IQ4_XS.gguf) \| IQ4_XS \| 4.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3-Neurona-8b-GGUF/resolve/main/Llama-3-Neurona-8b.Q4_K_S.gguf) \| Q4_K_S \| 4.8 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3-Neurona-8b-GGUF/resolve/main/Llama-3-Neurona-8b.Q4_K_M.gguf) \| Q4_K_M \| 5.0 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3-Neurona-8b-GGUF/resolve/main/Llama-3-Neurona-8b.Q5_K_S.gguf) \| Q5_K_S \| 5.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3-Neurona-8b-GGUF/resolve/main/Llama-3-Neurona-8b.Q5_K_M.gguf) \| Q5_K_M \| 5.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3-Neurona-8b-GGUF/resolve/main/Llama-3-Neurona-8b.Q6_K.gguf) \| Q6_K \| 6.7 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3-Neurona-8b-GGUF/resolve/main/Llama-3-Neurona-8b.Q8_0.gguf) \| Q8_0 \| 8.6 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3-Neurona-8b-GGUF/resolve/main/Llama-3-Neurona-8b.f16.gguf) \| f16 \| 16.2 \| 16 bpw, overkill \| 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 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## 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 -->
mradermacher/AtomPro-7B-GGUF	mradermacher	2024-05-30T04:42:24Z	19	0	transformers	[ "transformers", "gguf", "merge", "mergekit", "lazymergekit", "GritLM/GritLM-7B", "NousResearch/Hermes-2-Pro-Mistral-7B", "en", "endpoints_compatible", "region:us", "conversational" ]	null	2024-05-29T00:52:25Z	--- base_model: powermove72/AtomPro-7B language: - en library_name: transformers quantized_by: mradermacher tags: - merge - mergekit - lazymergekit - GritLM/GritLM-7B - NousResearch/Hermes-2-Pro-Mistral-7B --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/powermove72/AtomPro-7B <!-- 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/AtomPro-7B-GGUF/resolve/main/AtomPro-7B.Q2_K.gguf) \| Q2_K \| 2.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/AtomPro-7B-GGUF/resolve/main/AtomPro-7B.IQ3_XS.gguf) \| IQ3_XS \| 3.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/AtomPro-7B-GGUF/resolve/main/AtomPro-7B.Q3_K_S.gguf) \| Q3_K_S \| 3.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/AtomPro-7B-GGUF/resolve/main/AtomPro-7B.IQ3_S.gguf) \| IQ3_S \| 3.3 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/AtomPro-7B-GGUF/resolve/main/AtomPro-7B.IQ3_M.gguf) \| IQ3_M \| 3.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/AtomPro-7B-GGUF/resolve/main/AtomPro-7B.Q3_K_M.gguf) \| Q3_K_M \| 3.6 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/AtomPro-7B-GGUF/resolve/main/AtomPro-7B.Q3_K_L.gguf) \| Q3_K_L \| 3.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/AtomPro-7B-GGUF/resolve/main/AtomPro-7B.IQ4_XS.gguf) \| IQ4_XS \| 4.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/AtomPro-7B-GGUF/resolve/main/AtomPro-7B.Q4_K_S.gguf) \| Q4_K_S \| 4.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/AtomPro-7B-GGUF/resolve/main/AtomPro-7B.Q4_K_M.gguf) \| Q4_K_M \| 4.5 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/AtomPro-7B-GGUF/resolve/main/AtomPro-7B.Q5_K_S.gguf) \| Q5_K_S \| 5.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/AtomPro-7B-GGUF/resolve/main/AtomPro-7B.Q5_K_M.gguf) \| Q5_K_M \| 5.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/AtomPro-7B-GGUF/resolve/main/AtomPro-7B.Q6_K.gguf) \| Q6_K \| 6.0 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/AtomPro-7B-GGUF/resolve/main/AtomPro-7B.Q8_0.gguf) \| Q8_0 \| 7.8 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/AtomPro-7B-GGUF/resolve/main/AtomPro-7B.f16.gguf) \| f16 \| 14.6 \| 16 bpw, overkill \| 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 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## 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 -->
mradermacher/French-Alpaca-Mistral-7B-v0.3-GGUF	mradermacher	2024-05-30T04:42:12Z	13	0	transformers	[ "transformers", "gguf", "text-generation-inference", "unsloth", "mistral", "trl", "sft", "en", "fr", "dataset:jpacifico/French-Alpaca-dataset-Instruct-110K", "base_model:AdrienB134/French-Alpaca-Mistral-7B-v0.3", "base_model:quantized:AdrienB134/French-Alpaca-Mistral-7B-v0.3", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-05-29T03:03:07Z	--- base_model: AdrienB134/French-Alpaca-Mistral-7B-v0.3 datasets: - jpacifico/French-Alpaca-dataset-Instruct-110K language: - en - fr library_name: transformers license: apache-2.0 quantized_by: mradermacher tags: - text-generation-inference - transformers - unsloth - mistral - trl - sft --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/AdrienB134/French-Alpaca-Mistral-7B-v0.3 <!-- 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/French-Alpaca-Mistral-7B-v0.3-GGUF/resolve/main/French-Alpaca-Mistral-7B-v0.3.Q2_K.gguf) \| Q2_K \| 2.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/French-Alpaca-Mistral-7B-v0.3-GGUF/resolve/main/French-Alpaca-Mistral-7B-v0.3.IQ3_XS.gguf) \| IQ3_XS \| 3.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/French-Alpaca-Mistral-7B-v0.3-GGUF/resolve/main/French-Alpaca-Mistral-7B-v0.3.Q3_K_S.gguf) \| Q3_K_S \| 3.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/French-Alpaca-Mistral-7B-v0.3-GGUF/resolve/main/French-Alpaca-Mistral-7B-v0.3.IQ3_S.gguf) \| IQ3_S \| 3.3 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/French-Alpaca-Mistral-7B-v0.3-GGUF/resolve/main/French-Alpaca-Mistral-7B-v0.3.IQ3_M.gguf) \| IQ3_M \| 3.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/French-Alpaca-Mistral-7B-v0.3-GGUF/resolve/main/French-Alpaca-Mistral-7B-v0.3.Q3_K_M.gguf) \| Q3_K_M \| 3.6 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/French-Alpaca-Mistral-7B-v0.3-GGUF/resolve/main/French-Alpaca-Mistral-7B-v0.3.Q3_K_L.gguf) \| Q3_K_L \| 3.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/French-Alpaca-Mistral-7B-v0.3-GGUF/resolve/main/French-Alpaca-Mistral-7B-v0.3.IQ4_XS.gguf) \| IQ4_XS \| 4.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/French-Alpaca-Mistral-7B-v0.3-GGUF/resolve/main/French-Alpaca-Mistral-7B-v0.3.Q4_K_S.gguf) \| Q4_K_S \| 4.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/French-Alpaca-Mistral-7B-v0.3-GGUF/resolve/main/French-Alpaca-Mistral-7B-v0.3.Q4_K_M.gguf) \| Q4_K_M \| 4.5 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/French-Alpaca-Mistral-7B-v0.3-GGUF/resolve/main/French-Alpaca-Mistral-7B-v0.3.Q5_K_S.gguf) \| Q5_K_S \| 5.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/French-Alpaca-Mistral-7B-v0.3-GGUF/resolve/main/French-Alpaca-Mistral-7B-v0.3.Q5_K_M.gguf) \| Q5_K_M \| 5.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/French-Alpaca-Mistral-7B-v0.3-GGUF/resolve/main/French-Alpaca-Mistral-7B-v0.3.Q6_K.gguf) \| Q6_K \| 6.0 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/French-Alpaca-Mistral-7B-v0.3-GGUF/resolve/main/French-Alpaca-Mistral-7B-v0.3.Q8_0.gguf) \| Q8_0 \| 7.8 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/French-Alpaca-Mistral-7B-v0.3-GGUF/resolve/main/French-Alpaca-Mistral-7B-v0.3.f16.gguf) \| f16 \| 14.6 \| 16 bpw, overkill \| 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 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## 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 -->
mradermacher/ReflectionCoder-DS-33B-GGUF	mradermacher	2024-05-30T04:42:04Z	10	0	transformers	[ "transformers", "gguf", "en", "dataset:SenseLLM/ReflectionSeq-GPT", "dataset:SenseLLM/ReflectionSeq-DS", "base_model:SenseLLM/ReflectionCoder-DS-33B", "base_model:quantized:SenseLLM/ReflectionCoder-DS-33B", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-05-29T04:24:02Z	--- base_model: SenseLLM/ReflectionCoder-DS-33B datasets: - SenseLLM/ReflectionSeq-GPT - SenseLLM/ReflectionSeq-DS language: - en library_name: transformers license: apache-2.0 no_imatrix: nan1 quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/SenseLLM/ReflectionCoder-DS-33B <!-- provided-files --> ## 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/ReflectionCoder-DS-33B-GGUF/resolve/main/ReflectionCoder-DS-33B.Q2_K.gguf) \| Q2_K \| 12.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/ReflectionCoder-DS-33B-GGUF/resolve/main/ReflectionCoder-DS-33B.Q3_K_S.gguf) \| Q3_K_S \| 14.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/ReflectionCoder-DS-33B-GGUF/resolve/main/ReflectionCoder-DS-33B.IQ3_S.gguf) \| IQ3_S \| 14.6 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/ReflectionCoder-DS-33B-GGUF/resolve/main/ReflectionCoder-DS-33B.IQ3_M.gguf) \| IQ3_M \| 15.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/ReflectionCoder-DS-33B-GGUF/resolve/main/ReflectionCoder-DS-33B.Q3_K_M.gguf) \| Q3_K_M \| 16.2 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/ReflectionCoder-DS-33B-GGUF/resolve/main/ReflectionCoder-DS-33B.Q3_K_L.gguf) \| Q3_K_L \| 17.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/ReflectionCoder-DS-33B-GGUF/resolve/main/ReflectionCoder-DS-33B.Q4_K_S.gguf) \| Q4_K_S \| 19.0 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/ReflectionCoder-DS-33B-GGUF/resolve/main/ReflectionCoder-DS-33B.Q4_K_M.gguf) \| Q4_K_M \| 20.0 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/ReflectionCoder-DS-33B-GGUF/resolve/main/ReflectionCoder-DS-33B.Q6_K.gguf) \| Q6_K \| 27.5 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/ReflectionCoder-DS-33B-GGUF/resolve/main/ReflectionCoder-DS-33B.Q8_0.gguf) \| Q8_0 \| 35.5 \| 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 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## 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 -->
mradermacher/Flammen-Mahou-mistral-7B-GGUF	mradermacher	2024-05-30T04:41:53Z	22	0	transformers	[ "transformers", "gguf", "mergekit", "merge", "en", "base_model:nbeerbower/Flammen-Mahou-mistral-7B", "base_model:quantized:nbeerbower/Flammen-Mahou-mistral-7B", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-05-29T05:54:21Z	--- base_model: nbeerbower/Flammen-Mahou-mistral-7B language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher tags: - mergekit - merge --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/nbeerbower/Flammen-Mahou-mistral-7B <!-- 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/Flammen-Mahou-mistral-7B-GGUF/resolve/main/Flammen-Mahou-mistral-7B.Q2_K.gguf) \| Q2_K \| 2.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Flammen-Mahou-mistral-7B-GGUF/resolve/main/Flammen-Mahou-mistral-7B.IQ3_XS.gguf) \| IQ3_XS \| 3.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/Flammen-Mahou-mistral-7B-GGUF/resolve/main/Flammen-Mahou-mistral-7B.Q3_K_S.gguf) \| Q3_K_S \| 3.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Flammen-Mahou-mistral-7B-GGUF/resolve/main/Flammen-Mahou-mistral-7B.IQ3_S.gguf) \| IQ3_S \| 3.3 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/Flammen-Mahou-mistral-7B-GGUF/resolve/main/Flammen-Mahou-mistral-7B.IQ3_M.gguf) \| IQ3_M \| 3.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Flammen-Mahou-mistral-7B-GGUF/resolve/main/Flammen-Mahou-mistral-7B.Q3_K_M.gguf) \| Q3_K_M \| 3.6 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Flammen-Mahou-mistral-7B-GGUF/resolve/main/Flammen-Mahou-mistral-7B.Q3_K_L.gguf) \| Q3_K_L \| 3.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Flammen-Mahou-mistral-7B-GGUF/resolve/main/Flammen-Mahou-mistral-7B.IQ4_XS.gguf) \| IQ4_XS \| 4.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Flammen-Mahou-mistral-7B-GGUF/resolve/main/Flammen-Mahou-mistral-7B.Q4_K_S.gguf) \| Q4_K_S \| 4.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Flammen-Mahou-mistral-7B-GGUF/resolve/main/Flammen-Mahou-mistral-7B.Q4_K_M.gguf) \| Q4_K_M \| 4.5 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Flammen-Mahou-mistral-7B-GGUF/resolve/main/Flammen-Mahou-mistral-7B.Q5_K_S.gguf) \| Q5_K_S \| 5.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/Flammen-Mahou-mistral-7B-GGUF/resolve/main/Flammen-Mahou-mistral-7B.Q5_K_M.gguf) \| Q5_K_M \| 5.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Flammen-Mahou-mistral-7B-GGUF/resolve/main/Flammen-Mahou-mistral-7B.Q6_K.gguf) \| Q6_K \| 6.0 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/Flammen-Mahou-mistral-7B-GGUF/resolve/main/Flammen-Mahou-mistral-7B.Q8_0.gguf) \| Q8_0 \| 7.8 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/Flammen-Mahou-mistral-7B-GGUF/resolve/main/Flammen-Mahou-mistral-7B.f16.gguf) \| f16 \| 14.6 \| 16 bpw, overkill \| 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 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## 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 -->
asiansoul/U-GO-GIRL-Remix-Llama-3-KoEn-8B	asiansoul	2024-05-30T04:41:07Z	9	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "mergekit", "merge", "base_model:NousResearch/Hermes-2-Theta-Llama-3-8B", "base_model:merge:NousResearch/Hermes-2-Theta-Llama-3-8B", "base_model:NousResearch/Meta-Llama-3-8B", "base_model:merge:NousResearch/Meta-Llama-3-8B", "base_model:NousResearch/Meta-Llama-3-8B-Instruct", "base_model:merge:NousResearch/Meta-Llama-3-8B-Instruct", "base_model:allganize/Llama-3-Alpha-Ko-8B-Instruct", "base_model:merge:allganize/Llama-3-Alpha-Ko-8B-Instruct", "base_model:asiansoul/U-GO-GIRL-Llama-3-KoEn-8B", "base_model:merge:asiansoul/U-GO-GIRL-Llama-3-KoEn-8B", "base_model:nayohan/llama3-instrucTrans-enko-8b", "base_model:merge:nayohan/llama3-instrucTrans-enko-8b", "base_model:rombodawg/Llama-3-8B-Instruct-Coder", "base_model:merge:rombodawg/Llama-3-8B-Instruct-Coder", "base_model:saltlux/Ko-Llama3-Luxia-8B", "base_model:merge:saltlux/Ko-Llama3-Luxia-8B", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-30T00:51:53Z	--- base_model: - saltlux/Ko-Llama3-Luxia-8B - allganize/Llama-3-Alpha-Ko-8B-Instruct - nayohan/llama3-instrucTrans-enko-8b - NousResearch/Meta-Llama-3-8B - asiansoul/U-GO-GIRL-Llama-3-KoEn-8B - rombodawg/Llama-3-8B-Instruct-Coder - NousResearch/Hermes-2-Theta-Llama-3-8B - NousResearch/Meta-Llama-3-8B-Instruct library_name: transformers tags: - mergekit - merge --- # U-GO-GIRL-Remix-Llama-3-KoEn-8B <a href="https://ibb.co/jDSymM3"><img src="https://i.ibb.co/Hqjt6zG/vibe.png" alt="vibe" border="0"></a><br /> There are millions of people in the world who like me, but there are probably tens of millions of people who hate me. I will focus on those who like me. Because they made me who I am today. Because eventually you guys will come back here to watch me play~~~ "Back to the basics" [Allen Iverson](https://en.wikipedia.org/wiki/Allen_Iverson) [Toonation Donation](https://toon.at/donate/asiansoul) ETH/USDT(ERC20) Donation : 0x8BB117dD4Cc0E19E5536ab211070c0dE039a85c0 ### Models Merged The following models were included in the merge: * [asiansoul/U-GO-GIRL-Llama-3-KoEn-8B](https://huggingface.co/asiansoul/U-GO-GIRL-Llama-3-KoEn-8B) * [saltlux/Ko-Llama3-Luxia-8B](https://huggingface.co/saltlux/Ko-Llama3-Luxia-8B) * [allganize/Llama-3-Alpha-Ko-8B-Instruct](https://huggingface.co/allganize/Llama-3-Alpha-Ko-8B-Instruct) * [nayohan/llama3-instrucTrans-enko-8b](https://huggingface.co/nayohan/llama3-instrucTrans-enko-8b) * [rombodawg/Llama-3-8B-Instruct-Coder](https://huggingface.co/rombodawg/Llama-3-8B-Instruct-Coder) * [NousResearch/Hermes-2-Theta-Llama-3-8B](https://huggingface.co/NousResearch/Hermes-2-Theta-Llama-3-8B) * [NousResearch/Meta-Llama-3-8B-Instruct](https://huggingface.co/NousResearch/Meta-Llama-3-8B-Instruct) ## Citation Language Mix Model ```text @misc{U-GO_GIRL, author = {JayLee aka "asiansoul"}, title = {U-GO_GIRL Mix Model}, year = {2024}, }, } ```
ahmedesmail16/Train-Test-Augmentation-V4-beit-base	ahmedesmail16	2024-05-30T04:38:45Z	202	0	transformers	[ "transformers", "tensorboard", "safetensors", "beit", "image-classification", "generated_from_trainer", "base_model:microsoft/beit-base-patch16-224-pt22k-ft22k", "base_model:finetune:microsoft/beit-base-patch16-224-pt22k-ft22k", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-05-30T02:14:20Z	--- license: apache-2.0 base_model: microsoft/beit-base-patch16-224-pt22k-ft22k tags: - generated_from_trainer metrics: - accuracy model-index: - name: Train-Test-Augmentation-V4-beit-base 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. --> # Train-Test-Augmentation-V4-beit-base This model is a fine-tuned version of [microsoft/beit-base-patch16-224-pt22k-ft22k](https://huggingface.co/microsoft/beit-base-patch16-224-pt22k-ft22k) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.4701 - Accuracy: 0.8557 ## 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: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.6584 \| 1.0 \| 55 \| 0.6744 \| 0.7946 \| \| 0.2762 \| 2.0 \| 110 \| 0.5429 \| 0.8234 \| \| 0.1144 \| 3.0 \| 165 \| 0.5259 \| 0.8336 \| \| 0.0487 \| 4.0 \| 220 \| 0.5111 \| 0.8404 \| \| 0.0218 \| 5.0 \| 275 \| 0.4701 \| 0.8557 \| ### Framework versions - Transformers 4.39.3 - Pytorch 2.1.2 - Datasets 2.19.1 - Tokenizers 0.15.2
jspr/talosian_v3_instruct_peft	jspr	2024-05-30T04:38:21Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "mistral", "trl", "en", "base_model:unsloth/mistral-7b-instruct-v0.3", "base_model:finetune:unsloth/mistral-7b-instruct-v0.3", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-05-30T04:38:09Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - mistral - trl base_model: unsloth/mistral-7b-instruct-v0.3 --- # Uploaded model - Developed by: jspr - License: apache-2.0 - Finetuned from model : unsloth/mistral-7b-instruct-v0.3 This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
ebowwa/human-biases-people-v0.5-gguf	ebowwa	2024-05-30T04:30:48Z	4	0	transformers	[ "transformers", "gguf", "mistral", "text-generation-inference", "unsloth", "en", "base_model:unsloth/mistral-7b-instruct-v0.3-bnb-4bit", "base_model:quantized:unsloth/mistral-7b-instruct-v0.3-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2024-05-30T04:28:36Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - mistral - gguf base_model: unsloth/mistral-7b-instruct-v0.3-bnb-4bit --- # Uploaded model - Developed by: ebowwa - License: apache-2.0 - Finetuned from model : unsloth/mistral-7b-instruct-v0.3-bnb-4bit This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
mradermacher/AlchemistCoder-L-7B-GGUF	mradermacher	2024-05-30T04:29:18Z	21	0	transformers	[ "transformers", "gguf", "code generation", "en", "base_model:internlm/AlchemistCoder-L-7B", "base_model:quantized:internlm/AlchemistCoder-L-7B", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-05-30T04:05:08Z	--- base_model: internlm/AlchemistCoder-L-7B language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher tags: - code generation --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/internlm/AlchemistCoder-L-7B <!-- 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/AlchemistCoder-L-7B-GGUF/resolve/main/AlchemistCoder-L-7B.Q2_K.gguf) \| Q2_K \| 2.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/AlchemistCoder-L-7B-GGUF/resolve/main/AlchemistCoder-L-7B.IQ3_XS.gguf) \| IQ3_XS \| 2.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/AlchemistCoder-L-7B-GGUF/resolve/main/AlchemistCoder-L-7B.IQ3_S.gguf) \| IQ3_S \| 3.0 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/AlchemistCoder-L-7B-GGUF/resolve/main/AlchemistCoder-L-7B.Q3_K_S.gguf) \| Q3_K_S \| 3.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/AlchemistCoder-L-7B-GGUF/resolve/main/AlchemistCoder-L-7B.IQ3_M.gguf) \| IQ3_M \| 3.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/AlchemistCoder-L-7B-GGUF/resolve/main/AlchemistCoder-L-7B.Q3_K_M.gguf) \| Q3_K_M \| 3.4 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/AlchemistCoder-L-7B-GGUF/resolve/main/AlchemistCoder-L-7B.Q3_K_L.gguf) \| Q3_K_L \| 3.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/AlchemistCoder-L-7B-GGUF/resolve/main/AlchemistCoder-L-7B.IQ4_XS.gguf) \| IQ4_XS \| 3.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/AlchemistCoder-L-7B-GGUF/resolve/main/AlchemistCoder-L-7B.Q4_K_S.gguf) \| Q4_K_S \| 4.0 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/AlchemistCoder-L-7B-GGUF/resolve/main/AlchemistCoder-L-7B.Q4_K_M.gguf) \| Q4_K_M \| 4.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/AlchemistCoder-L-7B-GGUF/resolve/main/AlchemistCoder-L-7B.Q5_K_S.gguf) \| Q5_K_S \| 4.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/AlchemistCoder-L-7B-GGUF/resolve/main/AlchemistCoder-L-7B.Q5_K_M.gguf) \| Q5_K_M \| 4.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/AlchemistCoder-L-7B-GGUF/resolve/main/AlchemistCoder-L-7B.Q6_K.gguf) \| Q6_K \| 5.6 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/AlchemistCoder-L-7B-GGUF/resolve/main/AlchemistCoder-L-7B.Q8_0.gguf) \| Q8_0 \| 7.3 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/AlchemistCoder-L-7B-GGUF/resolve/main/AlchemistCoder-L-7B.f16.gguf) \| f16 \| 13.6 \| 16 bpw, overkill \| 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 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## 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 -->
ahmedgongi/Llama_dev3tokenizer_finale16	ahmedgongi	2024-05-30T04:28:59Z	0	0	transformers	[ "transformers", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-30T04:28:58Z	--- 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]
Bagus/wav2vec2_swbd_emodb	Bagus	2024-05-30T04:26:28Z	2	0	transformers	[ "transformers", "pytorch", "wav2vec2", "generated_from_trainer", "base_model:facebook/wav2vec2-large-robust-ft-swbd-300h", "base_model:finetune:facebook/wav2vec2-large-robust-ft-swbd-300h", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-05-30T02:59:58Z	--- license: apache-2.0 base_model: facebook/wav2vec2-large-robust-ft-swbd-300h tags: - generated_from_trainer model-index: - name: wav2vec2_swbd_emodb 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. --> # finetuned This model is a fine-tuned version of [facebook/wav2vec2-large-robust-ft-swbd-300h](https://huggingface.co/facebook/wav2vec2-large-robust-ft-swbd-300h) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.0281 - Uar: 0.7318 - Acc: 0.7721 For the test set: - UAR: 0.74 - ACC: 0.794 ## Model description This model is to predict four emotion categories given and audio file. Labels are anger', 'happiness', 'sadness', 'neutral'. This wav2vec2-based model is known cannot detect 'happiness'. ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Uar \| Acc \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\|:------:\| \| No log \| 0.15 \| 1 \| 1.3899 \| 0.25 \| 0.1985 \| \| No log \| 0.31 \| 2 \| 1.3850 \| 0.25 \| 0.1985 \| \| No log \| 0.46 \| 3 \| 1.3815 \| 0.25 \| 0.1985 \| \| No log \| 0.62 \| 4 \| 1.3772 \| 0.25 \| 0.1985 \| \| No log \| 0.77 \| 5 \| 1.3714 \| 0.25 \| 0.4044 \| \| No log \| 0.92 \| 6 \| 1.3656 \| 0.25 \| 0.4044 \| \| 1.4878 \| 1.08 \| 7 \| 1.3610 \| 0.25 \| 0.4044 \| \| 1.4878 \| 1.23 \| 8 \| 1.3583 \| 0.25 \| 0.4044 \| \| 1.4878 \| 1.38 \| 9 \| 1.3549 \| 0.25 \| 0.4044 \| \| 1.4878 \| 1.54 \| 10 \| 1.3518 \| 0.25 \| 0.4044 \| \| 1.4878 \| 1.69 \| 11 \| 1.3491 \| 0.25 \| 0.4044 \| \| 1.4878 \| 1.85 \| 12 \| 1.3458 \| 0.25 \| 0.4044 \| \| 1.4878 \| 2.0 \| 13 \| 1.3425 \| 0.25 \| 0.4044 \| \| 1.2316 \| 2.15 \| 14 \| 1.3401 \| 0.25 \| 0.4044 \| \| 1.2316 \| 2.31 \| 15 \| 1.3380 \| 0.25 \| 0.4044 \| \| 1.2316 \| 2.46 \| 16 \| 1.3354 \| 0.25 \| 0.4044 \| \| 1.2316 \| 2.62 \| 17 \| 1.3326 \| 0.25 \| 0.4044 \| \| 1.2316 \| 2.77 \| 18 \| 1.3292 \| 0.2778 \| 0.4265 \| \| 1.2316 \| 2.92 \| 19 \| 1.3250 \| 0.2963 \| 0.4412 \| \| 1.3835 \| 3.08 \| 20 \| 1.3212 \| 0.3519 \| 0.4853 \| \| 1.3835 \| 3.23 \| 21 \| 1.3158 \| 0.4029 \| 0.5221 \| \| 1.3835 \| 3.38 \| 22 \| 1.3096 \| 0.5047 \| 0.6029 \| \| 1.3835 \| 3.54 \| 23 \| 1.3019 \| 0.5695 \| 0.6544 \| \| 1.3835 \| 3.69 \| 24 \| 1.2944 \| 0.6485 \| 0.7059 \| \| 1.3835 \| 3.85 \| 25 \| 1.2856 \| 0.6534 \| 0.6985 \| \| 1.3835 \| 4.0 \| 26 \| 1.2773 \| 0.6768 \| 0.7059 \| \| 1.1038 \| 4.15 \| 27 \| 1.2688 \| 0.6540 \| 0.6691 \| \| 1.1038 \| 4.31 \| 28 \| 1.2554 \| 0.6404 \| 0.6471 \| \| 1.1038 \| 4.46 \| 29 \| 1.2404 \| 0.6359 \| 0.6397 \| \| 1.1038 \| 4.62 \| 30 \| 1.2222 \| 0.6586 \| 0.6765 \| \| 1.1038 \| 4.77 \| 31 \| 1.2057 \| 0.6631 \| 0.6838 \| \| 1.1038 \| 4.92 \| 32 \| 1.1874 \| 0.6769 \| 0.6985 \| \| 1.075 \| 5.08 \| 33 \| 1.1624 \| 0.6953 \| 0.7206 \| \| 1.075 \| 5.23 \| 34 \| 1.1427 \| 0.7182 \| 0.75 \| \| 1.075 \| 5.38 \| 35 \| 1.1270 \| 0.7182 \| 0.75 \| \| 1.075 \| 5.54 \| 36 \| 1.1085 \| 0.7227 \| 0.7574 \| \| 1.075 \| 5.69 \| 37 \| 1.0982 \| 0.7227 \| 0.7574 \| \| 1.075 \| 5.85 \| 38 \| 1.0943 \| 0.7227 \| 0.7574 \| \| 1.075 \| 6.0 \| 39 \| 1.0930 \| 0.7136 \| 0.7426 \| \| 0.7211 \| 6.15 \| 40 \| 1.0903 \| 0.7091 \| 0.7353 \| \| 0.7211 \| 6.31 \| 41 \| 1.0858 \| 0.7091 \| 0.7353 \| \| 0.7211 \| 6.46 \| 42 \| 1.0816 \| 0.7045 \| 0.7279 \| \| 0.7211 \| 6.62 \| 43 \| 1.0734 \| 0.7091 \| 0.7353 \| \| 0.7211 \| 6.77 \| 44 \| 1.0617 \| 0.7136 \| 0.7426 \| \| 0.7211 \| 6.92 \| 45 \| 1.0536 \| 0.7136 \| 0.7426 \| \| 0.6595 \| 7.08 \| 46 \| 1.0450 \| 0.7318 \| 0.7721 \| \| 0.6595 \| 7.23 \| 47 \| 1.0370 \| 0.7364 \| 0.7794 \| \| 0.6595 \| 7.38 \| 48 \| 1.0323 \| 0.7364 \| 0.7794 \| \| 0.6595 \| 7.54 \| 49 \| 1.0301 \| 0.7364 \| 0.7794 \| \| 0.6595 \| 7.69 \| 50 \| 1.0307 \| 0.7364 \| 0.7794 \| \| 0.6595 \| 7.85 \| 51 \| 1.0302 \| 0.7318 \| 0.7721 \| \| 0.6595 \| 8.0 \| 52 \| 1.0307 \| 0.7318 \| 0.7721 \| \| 0.5067 \| 8.15 \| 53 \| 1.0317 \| 0.7318 \| 0.7721 \| \| 0.5067 \| 8.31 \| 54 \| 1.0324 \| 0.7318 \| 0.7721 \| \| 0.5067 \| 8.46 \| 55 \| 1.0324 \| 0.7318 \| 0.7721 \| \| 0.5067 \| 8.62 \| 56 \| 1.0326 \| 0.7273 \| 0.7647 \| \| 0.5067 \| 8.77 \| 57 \| 1.0315 \| 0.7318 \| 0.7721 \| \| 0.5067 \| 8.92 \| 58 \| 1.0297 \| 0.7318 \| 0.7721 \| \| 0.5617 \| 9.08 \| 59 \| 1.0287 \| 0.7318 \| 0.7721 \| \| 0.5617 \| 9.23 \| 60 \| 1.0281 \| 0.7318 \| 0.7721 \| ### Framework versions - Transformers 4.32.0 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.13.3
mradermacher/Genshin_Impact_Mistral_v3_Plot_Chat_roleplay_chat_merged-GGUF	mradermacher	2024-05-30T04:21:48Z	89	0	transformers	[ "transformers", "gguf", "en", "base_model:svjack/Genshin_Impact_Mistral_v3_Plot_Chat_roleplay_chat_merged", "base_model:quantized:svjack/Genshin_Impact_Mistral_v3_Plot_Chat_roleplay_chat_merged", "endpoints_compatible", "region:us", "conversational" ]	null	2024-05-30T03:35:11Z	--- base_model: svjack/Genshin_Impact_Mistral_v3_Plot_Chat_roleplay_chat_merged language: - en library_name: transformers quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/svjack/Genshin_Impact_Mistral_v3_Plot_Chat_roleplay_chat_merged <!-- 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/Genshin_Impact_Mistral_v3_Plot_Chat_roleplay_chat_merged-GGUF/resolve/main/Genshin_Impact_Mistral_v3_Plot_Chat_roleplay_chat_merged.Q2_K.gguf) \| Q2_K \| 2.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Genshin_Impact_Mistral_v3_Plot_Chat_roleplay_chat_merged-GGUF/resolve/main/Genshin_Impact_Mistral_v3_Plot_Chat_roleplay_chat_merged.IQ3_XS.gguf) \| IQ3_XS \| 3.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/Genshin_Impact_Mistral_v3_Plot_Chat_roleplay_chat_merged-GGUF/resolve/main/Genshin_Impact_Mistral_v3_Plot_Chat_roleplay_chat_merged.Q3_K_S.gguf) \| Q3_K_S \| 3.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Genshin_Impact_Mistral_v3_Plot_Chat_roleplay_chat_merged-GGUF/resolve/main/Genshin_Impact_Mistral_v3_Plot_Chat_roleplay_chat_merged.IQ3_S.gguf) \| IQ3_S \| 3.3 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/Genshin_Impact_Mistral_v3_Plot_Chat_roleplay_chat_merged-GGUF/resolve/main/Genshin_Impact_Mistral_v3_Plot_Chat_roleplay_chat_merged.IQ3_M.gguf) \| IQ3_M \| 3.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Genshin_Impact_Mistral_v3_Plot_Chat_roleplay_chat_merged-GGUF/resolve/main/Genshin_Impact_Mistral_v3_Plot_Chat_roleplay_chat_merged.Q3_K_M.gguf) \| Q3_K_M \| 3.6 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Genshin_Impact_Mistral_v3_Plot_Chat_roleplay_chat_merged-GGUF/resolve/main/Genshin_Impact_Mistral_v3_Plot_Chat_roleplay_chat_merged.Q3_K_L.gguf) \| Q3_K_L \| 3.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Genshin_Impact_Mistral_v3_Plot_Chat_roleplay_chat_merged-GGUF/resolve/main/Genshin_Impact_Mistral_v3_Plot_Chat_roleplay_chat_merged.IQ4_XS.gguf) \| IQ4_XS \| 4.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Genshin_Impact_Mistral_v3_Plot_Chat_roleplay_chat_merged-GGUF/resolve/main/Genshin_Impact_Mistral_v3_Plot_Chat_roleplay_chat_merged.Q4_K_S.gguf) \| Q4_K_S \| 4.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Genshin_Impact_Mistral_v3_Plot_Chat_roleplay_chat_merged-GGUF/resolve/main/Genshin_Impact_Mistral_v3_Plot_Chat_roleplay_chat_merged.Q4_K_M.gguf) \| Q4_K_M \| 4.5 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Genshin_Impact_Mistral_v3_Plot_Chat_roleplay_chat_merged-GGUF/resolve/main/Genshin_Impact_Mistral_v3_Plot_Chat_roleplay_chat_merged.Q5_K_S.gguf) \| Q5_K_S \| 5.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/Genshin_Impact_Mistral_v3_Plot_Chat_roleplay_chat_merged-GGUF/resolve/main/Genshin_Impact_Mistral_v3_Plot_Chat_roleplay_chat_merged.Q5_K_M.gguf) \| Q5_K_M \| 5.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Genshin_Impact_Mistral_v3_Plot_Chat_roleplay_chat_merged-GGUF/resolve/main/Genshin_Impact_Mistral_v3_Plot_Chat_roleplay_chat_merged.Q6_K.gguf) \| Q6_K \| 6.0 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/Genshin_Impact_Mistral_v3_Plot_Chat_roleplay_chat_merged-GGUF/resolve/main/Genshin_Impact_Mistral_v3_Plot_Chat_roleplay_chat_merged.Q8_0.gguf) \| Q8_0 \| 7.8 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/Genshin_Impact_Mistral_v3_Plot_Chat_roleplay_chat_merged-GGUF/resolve/main/Genshin_Impact_Mistral_v3_Plot_Chat_roleplay_chat_merged.f16.gguf) \| f16 \| 14.6 \| 16 bpw, overkill \| 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 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## 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 -->
mradermacher/AtomPro-Coder-7B-GGUF	mradermacher	2024-05-30T04:20:42Z	39	0	transformers	[ "transformers", "gguf", "merge", "mergekit", "lazymergekit", "GritLM/GritLM-7B", "Nexusflow/Starling-LM-7B-beta", "en", "endpoints_compatible", "region:us", "conversational" ]	null	2024-05-30T03:23:49Z	--- base_model: powermove72/AtomPro-Coder-7B language: - en library_name: transformers quantized_by: mradermacher tags: - merge - mergekit - lazymergekit - GritLM/GritLM-7B - Nexusflow/Starling-LM-7B-beta --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/powermove72/AtomPro-Coder-7B <!-- 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/AtomPro-Coder-7B-GGUF/resolve/main/AtomPro-Coder-7B.Q2_K.gguf) \| Q2_K \| 2.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/AtomPro-Coder-7B-GGUF/resolve/main/AtomPro-Coder-7B.IQ3_XS.gguf) \| IQ3_XS \| 3.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/AtomPro-Coder-7B-GGUF/resolve/main/AtomPro-Coder-7B.Q3_K_S.gguf) \| Q3_K_S \| 3.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/AtomPro-Coder-7B-GGUF/resolve/main/AtomPro-Coder-7B.IQ3_S.gguf) \| IQ3_S \| 3.3 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/AtomPro-Coder-7B-GGUF/resolve/main/AtomPro-Coder-7B.IQ3_M.gguf) \| IQ3_M \| 3.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/AtomPro-Coder-7B-GGUF/resolve/main/AtomPro-Coder-7B.Q3_K_M.gguf) \| Q3_K_M \| 3.6 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/AtomPro-Coder-7B-GGUF/resolve/main/AtomPro-Coder-7B.Q3_K_L.gguf) \| Q3_K_L \| 3.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/AtomPro-Coder-7B-GGUF/resolve/main/AtomPro-Coder-7B.IQ4_XS.gguf) \| IQ4_XS \| 4.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/AtomPro-Coder-7B-GGUF/resolve/main/AtomPro-Coder-7B.Q4_K_S.gguf) \| Q4_K_S \| 4.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/AtomPro-Coder-7B-GGUF/resolve/main/AtomPro-Coder-7B.Q4_K_M.gguf) \| Q4_K_M \| 4.5 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/AtomPro-Coder-7B-GGUF/resolve/main/AtomPro-Coder-7B.Q5_K_S.gguf) \| Q5_K_S \| 5.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/AtomPro-Coder-7B-GGUF/resolve/main/AtomPro-Coder-7B.Q5_K_M.gguf) \| Q5_K_M \| 5.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/AtomPro-Coder-7B-GGUF/resolve/main/AtomPro-Coder-7B.Q6_K.gguf) \| Q6_K \| 6.0 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/AtomPro-Coder-7B-GGUF/resolve/main/AtomPro-Coder-7B.Q8_0.gguf) \| Q8_0 \| 7.8 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/AtomPro-Coder-7B-GGUF/resolve/main/AtomPro-Coder-7B.f16.gguf) \| f16 \| 14.6 \| 16 bpw, overkill \| 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 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## 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 -->
EleutherAI/Meta-Llama-3-8B-population-random-standardized-random-names	EleutherAI	2024-05-30T04:18:23Z	11	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "trl", "sft", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-30T01:31:26Z	--- library_name: transformers tags: - trl - sft --- # 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]
soufiane001/NeuralPipe-7B-slerp	soufiane001	2024-05-30T04:16:10Z	6	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "merge", "mergekit", "lazymergekit", "OpenPipe/mistral-ft-optimized-1218", "mlabonne/NeuralHermes-2.5-Mistral-7B", "base_model:OpenPipe/mistral-ft-optimized-1218", "base_model:merge:OpenPipe/mistral-ft-optimized-1218", "base_model:mlabonne/NeuralHermes-2.5-Mistral-7B", "base_model:merge:mlabonne/NeuralHermes-2.5-Mistral-7B", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-30T04:12:10Z	--- tags: - merge - mergekit - lazymergekit - OpenPipe/mistral-ft-optimized-1218 - mlabonne/NeuralHermes-2.5-Mistral-7B base_model: - OpenPipe/mistral-ft-optimized-1218 - mlabonne/NeuralHermes-2.5-Mistral-7B --- # NeuralPipe-7B-slerp NeuralPipe-7B-slerp is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing): * [OpenPipe/mistral-ft-optimized-1218](https://huggingface.co/OpenPipe/mistral-ft-optimized-1218) * [mlabonne/NeuralHermes-2.5-Mistral-7B](https://huggingface.co/mlabonne/NeuralHermes-2.5-Mistral-7B) ## 🧩 Configuration ```yaml slices: - sources: - model: OpenPipe/mistral-ft-optimized-1218 layer_range: [0, 32] - model: mlabonne/NeuralHermes-2.5-Mistral-7B layer_range: [0, 32] merge_method: slerp base_model: OpenPipe/mistral-ft-optimized-1218 parameters: t: - filter: self_attn value: [0, 0.5, 0.3, 0.7, 1] - filter: mlp value: [1, 0.5, 0.7, 0.3, 0] - value: 0.5 dtype: bfloat16 ``` ## 💻 Usage ```python !pip install -qU transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "soufiane001/NeuralPipe-7B-slerp" messages = [{"role": "user", "content": "What is a large language model?"}] tokenizer = AutoTokenizer.from_pretrained(model) prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) pipeline = transformers.pipeline( "text-generation", model=model, torch_dtype=torch.float16, device_map="auto", ) outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95) print(outputs[0]["generated_text"]) ```
sunoaiysha/gpt2-company	sunoaiysha	2024-05-30T04:15:58Z	148	0	transformers	[ "transformers", "safetensors", "gpt2", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-30T04:15:34Z	--- 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]
ebowwa/human-biases-people-v0.5	ebowwa	2024-05-30T04:14:06Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "mistral", "trl", "en", "base_model:unsloth/mistral-7b-instruct-v0.3-bnb-4bit", "base_model:finetune:unsloth/mistral-7b-instruct-v0.3-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-05-30T04:13:57Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - mistral - trl base_model: unsloth/mistral-7b-instruct-v0.3-bnb-4bit --- # Uploaded model - Developed by: ebowwa - License: apache-2.0 - Finetuned from model : unsloth/mistral-7b-instruct-v0.3-bnb-4bit This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
acl-srw-2024/llama3-8b-unsloth-sft-awq-4bit-v2	acl-srw-2024	2024-05-30T04:12:35Z	74	0	transformers	[ "transformers", "pytorch", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "awq", "region:us" ]	text-generation	2024-05-30T04:09:58Z	--- 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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NikolayKozloff/AutoCoder_S_6.7B-Q8_0-GGUF	NikolayKozloff	2024-05-30T04:05:55Z	1	2	null	[ "gguf", "llama-cpp", "gguf-my-repo", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2024-05-30T04:05:33Z	--- license: apache-2.0 tags: - llama-cpp - gguf-my-repo --- # NikolayKozloff/AutoCoder_S_6.7B-Q8_0-GGUF This model was converted to GGUF format from [`Bin12345/AutoCoder_S_6.7B`](https://huggingface.co/Bin12345/AutoCoder_S_6.7B) 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/Bin12345/AutoCoder_S_6.7B) 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 NikolayKozloff/AutoCoder_S_6.7B-Q8_0-GGUF --model autocoder_s_6.7b-q8_0.gguf -p "The meaning to life and the universe is" ``` Server: ```bash llama-server --hf-repo NikolayKozloff/AutoCoder_S_6.7B-Q8_0-GGUF --model autocoder_s_6.7b-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 autocoder_s_6.7b-q8_0.gguf -n 128 ```
mateomarin/dqn-SpaceInvadersNoFrameskip-v4	mateomarin	2024-05-30T04:03:21Z	0	0	stable-baselines3	[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2024-05-30T04:02:56Z	--- library_name: stable-baselines3 tags: - SpaceInvadersNoFrameskip-v4 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: DQN results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: SpaceInvadersNoFrameskip-v4 type: SpaceInvadersNoFrameskip-v4 metrics: - type: mean_reward value: 329.00 +/- 157.97 name: mean_reward verified: false --- # DQN Agent playing SpaceInvadersNoFrameskip-v4 This is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/> SB3: https://github.com/DLR-RM/stable-baselines3<br/> SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib Install the RL Zoo (with SB3 and SB3-Contrib): ```bash pip install rl_zoo3 ``` ``` # Download model and save it into the logs/ folder python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga mateomarin -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do: ``` python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga mateomarin -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` ## Training (with the RL Zoo) ``` python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga mateomarin ``` ## Hyperparameters ```python OrderedDict([('batch_size', 32), ('buffer_size', 500000), ('env_wrapper', ['stable_baselines3.common.atari_wrappers.AtariWrapper']), ('exploration_final_eps', 0.01), ('exploration_fraction', 0.1), ('frame_stack', 4), ('gradient_steps', 1), ('learning_rate', 0.01), ('learning_starts', 100000), ('n_timesteps', 100000.0), ('optimize_memory_usage', False), ('policy', 'CnnPolicy'), ('target_update_interval', 1000), ('train_freq', 4), ('normalize', False)]) ``` # Environment Arguments ```python {'render_mode': 'rgb_array'} ```
galocher/mistral-7b-v0.3-8b	galocher	2024-05-30T04:03:13Z	5	0	transformers	[ "transformers", "gguf", "mistral", "text-generation-inference", "unsloth", "en", "base_model:unsloth/mistral-7b-v0.3-bnb-4bit", "base_model:quantized:unsloth/mistral-7b-v0.3-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-05-30T03:58:59Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - mistral - gguf base_model: unsloth/mistral-7b-v0.3-bnb-4bit --- # Uploaded model - Developed by: galocher - License: apache-2.0 - Finetuned from model : unsloth/mistral-7b-v0.3-bnb-4bit This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
alpha-brain/llama3-mermaid-v1-full-q4	alpha-brain	2024-05-30T03:55:13Z	6	0	transformers	[ "transformers", "gguf", "llama", "text-generation-inference", "unsloth", "en", "base_model:unsloth/llama-3-8b-Instruct-bnb-4bit", "base_model:quantized:unsloth/llama-3-8b-Instruct-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2024-05-30T03:52:19Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - gguf base_model: unsloth/llama-3-8b-Instruct-bnb-4bit --- # Uploaded model - Developed by: alpha-brain - License: apache-2.0 - Finetuned from model : unsloth/llama-3-8b-Instruct-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
ard2020/l3_pt_ALL_data	ard2020	2024-05-30T03:54:31Z	0	0	peft	[ "peft", "tensorboard", "safetensors", "trl", "sft", "unsloth", "generated_from_trainer", "base_model:meta-llama/Meta-Llama-3-8B", "base_model:adapter:meta-llama/Meta-Llama-3-8B", "license:llama3", "region:us" ]	null	2024-05-30T03:54:24Z	--- license: llama3 library_name: peft tags: - trl - sft - unsloth - generated_from_trainer base_model: meta-llama/Meta-Llama-3-8B model-index: - name: l3_pt_ALL_data 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. --> # l3_pt_ALL_data This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B](https://huggingface.co/meta-llama/Meta-Llama-3-8B) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.4816 ## 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: 0.0003 - train_batch_size: 8 - eval_batch_size: 4 - seed: 3407 - gradient_accumulation_steps: 8 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 0.01 - num_epochs: 4 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 0.476 \| 0.8003 \| 1644 \| 0.5334 \| \| 0.4679 \| 1.6007 \| 3288 \| 0.4984 \| \| 0.3466 \| 2.4010 \| 4932 \| 0.4764 \| \| 0.245 \| 3.2014 \| 6576 \| 0.4816 \| ### Framework versions - PEFT 0.11.1 - Transformers 4.41.1 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
njprogrammer/e5-large-mounjaro	njprogrammer	2024-05-30T03:51:19Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-30T03:51:13Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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hdve/google-gemma-7b-1717040730	hdve	2024-05-30T03:48:57Z	6	0	transformers	[ "transformers", "safetensors", "gemma", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-30T03:45:32Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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sridhar1ga/wav2vec-dys-large	sridhar1ga	2024-05-30T03:45:07Z	0	0	transformers	[ "transformers", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-30T03:15:29Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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imdatta0/llama_2_7b_Magiccoder_evol_10k_ortho_scale15	imdatta0	2024-05-30T03:43:54Z	0	0	peft	[ "peft", "safetensors", "unsloth", "generated_from_trainer", "base_model:meta-llama/Llama-2-7b-hf", "base_model:adapter:meta-llama/Llama-2-7b-hf", "license:llama2", "region:us" ]	null	2024-05-30T03:43:50Z	--- license: llama2 library_name: peft tags: - unsloth - generated_from_trainer base_model: meta-llama/Llama-2-7b-hf model-index: - name: llama_2_7b_Magiccoder_evol_10k_ortho_scale15 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. --> # llama_2_7b_Magiccoder_evol_10k_ortho_scale15 This model is a fine-tuned version of [meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.1550 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 0.02 - num_epochs: 1 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 1.2864 \| 0.0262 \| 4 \| 1.2873 \| \| 1.2462 \| 0.0523 \| 8 \| 1.2294 \| \| 1.1767 \| 0.0785 \| 12 \| 1.2139 \| \| 1.1283 \| 0.1047 \| 16 \| 1.2055 \| \| 1.1715 \| 0.1308 \| 20 \| 1.2003 \| \| 1.1311 \| 0.1570 \| 24 \| 1.1935 \| \| 1.131 \| 0.1832 \| 28 \| 1.1888 \| \| 1.1799 \| 0.2093 \| 32 \| 1.1842 \| \| 1.1067 \| 0.2355 \| 36 \| 1.1788 \| \| 1.1915 \| 0.2617 \| 40 \| 1.1765 \| \| 1.1642 \| 0.2878 \| 44 \| 1.1740 \| \| 1.2078 \| 0.3140 \| 48 \| 1.1730 \| \| 1.1847 \| 0.3401 \| 52 \| 1.1706 \| \| 1.1519 \| 0.3663 \| 56 \| 1.1696 \| \| 1.2085 \| 0.3925 \| 60 \| 1.1681 \| \| 1.203 \| 0.4186 \| 64 \| 1.1656 \| \| 1.145 \| 0.4448 \| 68 \| 1.1631 \| \| 1.1622 \| 0.4710 \| 72 \| 1.1613 \| \| 1.113 \| 0.4971 \| 76 \| 1.1610 \| \| 1.2004 \| 0.5233 \| 80 \| 1.1615 \| \| 1.2185 \| 0.5495 \| 84 \| 1.1603 \| \| 1.1484 \| 0.5756 \| 88 \| 1.1595 \| \| 1.1036 \| 0.6018 \| 92 \| 1.1584 \| \| 1.1038 \| 0.6280 \| 96 \| 1.1575 \| \| 1.1618 \| 0.6541 \| 100 \| 1.1569 \| \| 1.1547 \| 0.6803 \| 104 \| 1.1563 \| \| 1.1405 \| 0.7065 \| 108 \| 1.1562 \| \| 1.1723 \| 0.7326 \| 112 \| 1.1558 \| \| 1.1195 \| 0.7588 \| 116 \| 1.1554 \| \| 1.1408 \| 0.7850 \| 120 \| 1.1550 \| \| 1.1683 \| 0.8111 \| 124 \| 1.1547 \| \| 1.129 \| 0.8373 \| 128 \| 1.1548 \| \| 1.0604 \| 0.8635 \| 132 \| 1.1550 \| \| 1.1764 \| 0.8896 \| 136 \| 1.1551 \| \| 1.0874 \| 0.9158 \| 140 \| 1.1551 \| \| 1.1365 \| 0.9419 \| 144 \| 1.1551 \| \| 1.1552 \| 0.9681 \| 148 \| 1.1550 \| \| 1.1481 \| 0.9943 \| 152 \| 1.1550 \| ### Framework versions - PEFT 0.7.1 - Transformers 4.40.2 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
ElevenHu/ruozhiba-llama3-chines	ElevenHu	2024-05-30T03:43:29Z	2	0	transformers	[ "transformers", "pytorch", "llama", "text-generation", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-30T03:28:37Z	--- license: apache-2.0 ---
lrycro/bert-phishing-categorization-tokenizer	lrycro	2024-05-30T03:40:23Z	0	0	transformers	[ "transformers", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-28T06:15:52Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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lrycro/bert-phishing-categorization-model	lrycro	2024-05-30T03:40:21Z	184	0	transformers	[ "transformers", "safetensors", "bert", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-30T03:39:53Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
EddiVincent/QA-Dataset-KR-familyLaw	EddiVincent	2024-05-30T03:40:13Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2024-05-30T03:40:13Z	--- license: apache-2.0 ---
OwOpeepeepoopoo/TheDumpheys6	OwOpeepeepoopoo	2024-05-30T03:33:58Z	132	0	transformers	[ "transformers", "safetensors", "stablelm", "text-generation", "mergekit", "merge", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-05-30T03:32:46Z	--- base_model: [] library_name: transformers tags: - mergekit - merge --- # output_diff1_4_deci1 This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). ## Merge Details ### Merge Method This model was merged using the SLERP merge method. ### Models Merged The following models were included in the merge: * /notebooks/dippy-bittensor-subnet/clone_baxtos_bax09-39 * /notebooks/dippy-bittensor-subnet/clone_tistak_F4Pz0cGuDztfU49T ### Configuration The following YAML configuration was used to produce this model: ```yaml slices: - sources: - model: /notebooks/dippy-bittensor-subnet/clone_tistak_F4Pz0cGuDztfU49T layer_range: [0, 24] - model: /notebooks/dippy-bittensor-subnet/clone_baxtos_bax09-39 layer_range: [0, 24] merge_method: slerp base_model: /notebooks/dippy-bittensor-subnet/clone_tistak_F4Pz0cGuDztfU49T parameters: t: - filter: self_attn value: [0.1, 0.3, 0.5, 0.7, 0.9] - filter: mlp value: [0.9, 0.7, 0.5, 0.3, 0.1] - value: 0.5 dtype: bfloat16 ```
Paulie-Aditya/sign-language-detection	Paulie-Aditya	2024-05-30T03:31:36Z	0	0	null	[ "medical", "image-classification", "region:us" ]	image-classification	2024-03-26T07:43:13Z	--- pipeline_tag: image-classification tags: - medical --- # Novel Approach 1 ## Stacked Classifier: RF + SVM + XGB metrics: - Accuracy: 0.9911734164070612 - Balanced Accuracy: 0.9903422714760236 - MCC: 0.990784932183338 - ROC AUC Score: 0.999934898058849 - F1 Score: 0.9911734164070612 - Jaccard Score: 0.9825012866700978 - Log Loss: 0.033553756349283356 - Precision: 0.9911734164070612 - Recall: 0.9911734164070612 # Novel Approach 2 ## Stacked Classifier: RF + SVM + KNN + XGB metrics: - Accuracy: 0.9922118380062306 - Balanced Accuracy: 0.9913200369813552 - MCC: 0.9918690348004674 - ROC AUC Score: 0.9999193482927975 - F1 Score: 0.9922118380062306 - Jaccard Score: 0.9845440494590417 - Log Loss: 0.03136301122428542 - Precision: 0.9922118380062306 - Recall: 0.9922118380062306
ReySajju742/Sajjad_NLP	ReySajju742	2024-05-30T03:28:29Z	0	0	transformers	[ "transformers", "nlp", "nltk", "en", "ur", "dataset:wikipedia", "doi:10.57967/hf/2339", "license:cc", "endpoints_compatible", "region:us" ]	null	2024-05-29T10:35:41Z	--- license: cc datasets: - wikipedia language: - en - ur tags: - nlp - nltk library_name: transformers ---
MdGolamMostofa/Mr.X	MdGolamMostofa	2024-05-30T03:26:51Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2024-05-30T03:22:16Z	--- license: apache-2.0 ---
QuantFactory/NeuralLlama-3-8B-Instruct-abliterated-GGUF	QuantFactory	2024-05-30T03:24:41Z	150	0	null	[ "gguf", "abliterated", "text-generation", "dataset:mlabonne/orpo-dpo-mix-40k", "base_model:mlabonne/NeuralLlama-3-8B-Instruct-abliterated", "base_model:quantized:mlabonne/NeuralLlama-3-8B-Instruct-abliterated", "license:other", "endpoints_compatible", "region:us", "conversational" ]	text-generation	2024-05-29T23:58:17Z	--- license: other datasets: - mlabonne/orpo-dpo-mix-40k tags: - abliterated pipeline_tag: text-generation base_model: mlabonne/NeuralLlama-3-8B-Instruct-abliterated --- # Llama-3-8B-Instruct-abliterated-dpomix-GGUF This is quantized version of [mlabonne/NeuralLlama-3-8B-Instruct-abliterated](https://huggingface.co/mlabonne/NeuralLlama-3-8B-Instruct-abliterated) created using llama.cpp # Model Description This model is an experimental DPO fine-tune of an abliterated Llama 3 8B Instruct model on the full [mlabonne/orpo-dpo-mix-40k](https://huggingface.co/datasets/mlabonne/orpo-dpo-mix-40k) dataset. It improves Llama 3 8B Instruct's performance while being uncensored. ## 🔎 Applications This is an uncensored model. You can use it for any application that doesn't require alignment, like role-playing. Tested on LM Studio using the "Llama 3" preset. ## 🏆 Evaluation ### Open LLM Leaderboard This model improves the performance of the abliterated source model and recovers the MMLU that was lost in the abliteration process. ![image/png](https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/sCO69BltMkGrq6u7yCIcP.png) ### Nous \| Model \| Average \| AGIEval \| GPT4All \| TruthfulQA \| Bigbench \| \|---\|---:\|---:\|---:\|---:\|---:\| \| [mlabonne/Llama-3-8B-Instruct-abliterated-dpomix](https://huggingface.co/mlabonne/Llama-3-8B-Instruct-abliterated-dpomix) [📄](https://gist.github.com/mlabonne/d711548df70e2c04771cc68ab33fe2b9) \| 52.26 \| 41.6 \| 69.95 \| 54.22 \| 43.26 \| \| [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) [📄](https://gist.github.com/mlabonne/8329284d86035e6019edb11eb0933628) \| 51.34 \| 41.22 \| 69.86 \| 51.65 \| 42.64 \| \| [failspy/Meta-Llama-3-8B-Instruct-abliterated-v3](https://huggingface.co/failspy/Meta-Llama-3-8B-Instruct-abliterated-v3) [📄](https://gist.github.com/mlabonne/f46cce0262443365e4cce2b6fa7507fc) \| 51.21 \| 40.23 \| 69.5 \| 52.44 \| 42.69 \| \| [abacusai/Llama-3-Smaug-8B](https://huggingface.co/abacusai/Llama-3-Smaug-8B) [📄](https://gist.github.com/mlabonne/91369d9c372f80b6a42a978b454d3b5e) \| 49.65 \| 37.15 \| 69.12 \| 51.66 \| 40.67 \| \| [mlabonne/OrpoLlama-3-8B](https://huggingface.co/mlabonne/OrpoLlama-3-8B) [📄](https://gist.github.com/mlabonne/22896a1ae164859931cc8f4858c97f6f) \| 48.63 \| 34.17 \| 70.59 \| 52.39 \| 37.36 \| \| [meta-llama/Meta-Llama-3-8B](https://huggingface.co/meta-llama/Meta-Llama-3-8B) [📄](https://gist.github.com/mlabonne/616b6245137a9cfc4ea80e4c6e55d847) \| 45.42 \| 31.1 \| 69.95 \| 43.91 \| 36.7 \|
Crysiss/llama-3-8B-welfare-sft-test	Crysiss	2024-05-30T03:22:46Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:unsloth/llama-3-8b-bnb-4bit", "base_model:finetune:unsloth/llama-3-8b-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-05-30T03:22:04Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl base_model: unsloth/llama-3-8b-bnb-4bit --- # Uploaded model - Developed by: Crysiss - License: apache-2.0 - Finetuned from model : unsloth/llama-3-8b-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
leoli04/code-search-net-tokenizer	leoli04	2024-05-30T03:21:58Z	0	0	transformers	[ "transformers", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-30T03:21:57Z	--- 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. 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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. 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nsugianto/tblstructrecog_finetuned_tbltransstrucrecog_v1_s1_394s	nsugianto	2024-05-30T03:19:15Z	27	0	transformers	[ "transformers", "tensorboard", "safetensors", "table-transformer", "object-detection", "generated_from_trainer", "base_model:microsoft/table-transformer-structure-recognition", "base_model:finetune:microsoft/table-transformer-structure-recognition", "license:mit", "endpoints_compatible", "region:us" ]	object-detection	2024-05-29T09:10:27Z	--- license: mit base_model: microsoft/table-transformer-structure-recognition tags: - generated_from_trainer model-index: - name: tblstructrecog_finetuned_tbltransstrucrecog_v1_s1_394s 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. --> # tblstructrecog_finetuned_tbltransstrucrecog_v1_s1_394s This model is a fine-tuned version of [microsoft/table-transformer-structure-recognition](https://huggingface.co/microsoft/table-transformer-structure-recognition) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-06 - 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: 750 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.41.0.dev0 - Pytorch 2.0.1 - Datasets 2.18.0 - Tokenizers 0.19.1
AmilaUvaz/autotrain-ml35h-mm87t	AmilaUvaz	2024-05-30T03:17:58Z	2	0	diffusers	[ "diffusers", "autotrain", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "lora", "template:sd-lora", "base_model:runwayml/stable-diffusion-v1-5", "base_model:adapter:runwayml/stable-diffusion-v1-5", "license:openrail++", "region:us" ]	text-to-image	2024-05-30T03:17:53Z	--- tags: - autotrain - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - lora - template:sd-lora base_model: runwayml/stable-diffusion-v1-5 instance_prompt: <A man John Barrowman> license: openrail++ --- # AutoTrain LoRA DreamBooth - AmilaUvaz/autotrain-ml35h-mm87t These are LoRA adaption weights for runwayml/stable-diffusion-v1-5. The weights were trained on <A man John Barrowman> using [DreamBooth](https://dreambooth.github.io/). LoRA for the text encoder was enabled: False.
trailios/h	trailios	2024-05-30T03:17:30Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2024-05-30T03:17:30Z	--- license: apache-2.0 ---
eeeyounglee/EEVE-10.8B-mean-2048-5	eeeyounglee	2024-05-30T03:14:41Z	10	0	sentence-transformers	[ "sentence-transformers", "safetensors", "llama", "feature-extraction", "sentence-similarity", "autotrain_compatible", "endpoints_compatible", "region:us" ]	sentence-similarity	2024-05-30T03:11:55Z	--- library_name: sentence-transformers pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity --- # eeeyounglee/EEVE-10.8B-mean-2048-5 This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 2048 dimensional dense vector space and can be used for tasks like clustering or semantic search. <!--- Describe your model here --> ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('eeeyounglee/EEVE-10.8B-mean-2048-5') embeddings = model.encode(sentences) print(embeddings) ``` ## Evaluation Results <!--- Describe how your model was evaluated --> For an automated evaluation of this model, see the Sentence Embeddings Benchmark: [https://seb.sbert.net](https://seb.sbert.net?model_name=eeeyounglee/EEVE-10.8B-mean-2048-5) ## Training The model was trained with the parameters: DataLoader: `torch.utils.data.dataloader.DataLoader` of length 224 with parameters: ``` {'batch_size': 64, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` Loss: `__main__.MultipleNegativesRankingLoss_with_logging` Parameters of the fit()-Method: ``` { "epochs": 5, "evaluation_steps": 1000, "evaluator": "sentence_transformers.evaluation.EmbeddingSimilarityEvaluator.EmbeddingSimilarityEvaluator", "max_grad_norm": 1, "optimizer_class": "<class 'torch.optim.adamw.AdamW'>", "optimizer_params": { "lr": 2e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": null, "warmup_steps": 112, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 4096, 'do_lower_case': False}) with Transformer model: LlamaModel (1): Pooling({'word_embedding_dimension': 4096, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) (2): Dense({'in_features': 4096, 'out_features': 2048, 'bias': True, 'activation_function': 'torch.nn.modules.activation.Tanh'}) ) ``` ## Citing & Authors <!--- Describe where people can find more information -->
indirajith-jithu/llama-3-8b-tenjin-Q4_0-GGUF	indirajith-jithu	2024-05-30T03:06:23Z	1	0	transformers	[ "transformers", "gguf", "llama-cpp", "gguf-my-repo", "endpoints_compatible", "region:us", "conversational" ]	null	2024-05-30T03:06:05Z	--- library_name: transformers tags: - llama-cpp - gguf-my-repo --- # indirajith-jithu/llama-3-8b-tenjin-Q4_0-GGUF This model was converted to GGUF format from [`indirajith-jithu/llama-3-8b-tenjin`](https://huggingface.co/indirajith-jithu/llama-3-8b-tenjin) 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/indirajith-jithu/llama-3-8b-tenjin) 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 indirajith-jithu/llama-3-8b-tenjin-Q4_0-GGUF --model llama-3-8b-tenjin-q4_0.gguf -p "The meaning to life and the universe is" ``` Server: ```bash llama-server --hf-repo indirajith-jithu/llama-3-8b-tenjin-Q4_0-GGUF --model llama-3-8b-tenjin-q4_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 llama-3-8b-tenjin-q4_0.gguf -n 128 ```
alpha-brain/llama-mermaid-v1	alpha-brain	2024-05-30T03:03:55Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:unsloth/llama-3-8b-Instruct-bnb-4bit", "base_model:finetune:unsloth/llama-3-8b-Instruct-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-05-30T03:03:45Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl base_model: unsloth/llama-3-8b-Instruct-bnb-4bit --- # Uploaded model - Developed by: alpha-brain - License: apache-2.0 - Finetuned from model : unsloth/llama-3-8b-Instruct-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
wuttong/drivelm_ft_visualglm	wuttong	2024-05-30T03:02:26Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2024-05-27T13:22:31Z	--- license: apache-2.0 ---
mradermacher/Mahou-1.3-mistral-7B-GGUF	mradermacher	2024-05-30T02:57:39Z	22	0	transformers	[ "transformers", "gguf", "en", "dataset:flammenai/MahouMix-v1", "base_model:flammenai/Mahou-1.3-mistral-7B", "base_model:quantized:flammenai/Mahou-1.3-mistral-7B", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2024-05-30T02:31:53Z	--- base_model: flammenai/Mahou-1.3-mistral-7B datasets: - flammenai/MahouMix-v1 language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/flammenai/Mahou-1.3-mistral-7B <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/Mahou-1.3-mistral-7B-i1-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/Mahou-1.3-mistral-7B-GGUF/resolve/main/Mahou-1.3-mistral-7B.Q2_K.gguf) \| Q2_K \| 2.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mahou-1.3-mistral-7B-GGUF/resolve/main/Mahou-1.3-mistral-7B.IQ3_XS.gguf) \| IQ3_XS \| 3.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mahou-1.3-mistral-7B-GGUF/resolve/main/Mahou-1.3-mistral-7B.Q3_K_S.gguf) \| Q3_K_S \| 3.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mahou-1.3-mistral-7B-GGUF/resolve/main/Mahou-1.3-mistral-7B.IQ3_S.gguf) \| IQ3_S \| 3.3 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/Mahou-1.3-mistral-7B-GGUF/resolve/main/Mahou-1.3-mistral-7B.IQ3_M.gguf) \| IQ3_M \| 3.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mahou-1.3-mistral-7B-GGUF/resolve/main/Mahou-1.3-mistral-7B.Q3_K_M.gguf) \| Q3_K_M \| 3.6 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Mahou-1.3-mistral-7B-GGUF/resolve/main/Mahou-1.3-mistral-7B.Q3_K_L.gguf) \| Q3_K_L \| 3.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mahou-1.3-mistral-7B-GGUF/resolve/main/Mahou-1.3-mistral-7B.IQ4_XS.gguf) \| IQ4_XS \| 4.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mahou-1.3-mistral-7B-GGUF/resolve/main/Mahou-1.3-mistral-7B.Q4_K_S.gguf) \| Q4_K_S \| 4.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Mahou-1.3-mistral-7B-GGUF/resolve/main/Mahou-1.3-mistral-7B.Q4_K_M.gguf) \| Q4_K_M \| 4.5 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Mahou-1.3-mistral-7B-GGUF/resolve/main/Mahou-1.3-mistral-7B.Q5_K_S.gguf) \| Q5_K_S \| 5.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mahou-1.3-mistral-7B-GGUF/resolve/main/Mahou-1.3-mistral-7B.Q5_K_M.gguf) \| Q5_K_M \| 5.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mahou-1.3-mistral-7B-GGUF/resolve/main/Mahou-1.3-mistral-7B.Q6_K.gguf) \| Q6_K \| 6.0 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/Mahou-1.3-mistral-7B-GGUF/resolve/main/Mahou-1.3-mistral-7B.Q8_0.gguf) \| Q8_0 \| 7.8 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/Mahou-1.3-mistral-7B-GGUF/resolve/main/Mahou-1.3-mistral-7B.f16.gguf) \| f16 \| 14.6 \| 16 bpw, overkill \| 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 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## 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 -->
hdve/google-gemma-2b-1717037104	hdve	2024-05-30T02:48:07Z	146	0	transformers	[ "transformers", "safetensors", "gemma", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-30T02:45:06Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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datek/google-gemma-2b-1717037059	datek	2024-05-30T02:46:54Z	146	0	transformers	[ "transformers", "safetensors", "gemma", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-30T02:44:21Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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ismichel/humor_model_v2	ismichel	2024-05-30T02:45:46Z	167	0	transformers	[ "transformers", "tensorboard", "safetensors", "wav2vec2", "audio-classification", "generated_from_trainer", "base_model:facebook/wav2vec2-base", "base_model:finetune:facebook/wav2vec2-base", "license:apache-2.0", "endpoints_compatible", "region:us" ]	audio-classification	2024-05-30T02:28:44Z	--- license: apache-2.0 base_model: facebook/wav2vec2-base tags: - generated_from_trainer metrics: - accuracy model-index: - name: humor_model_v2 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. --> # humor_model_v2 This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.2683 - Accuracy: 0.9639 ## 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: 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 \| \|:-------------:\|:------:\|:----:\|:---------------:\|:--------:\| \| No log \| 0.9524 \| 5 \| 0.6652 \| 0.9157 \| \| 0.6722 \| 1.9048 \| 10 \| 0.5931 \| 0.9217 \| \| 0.6722 \| 2.8571 \| 15 \| 0.5272 \| 0.9337 \| \| 0.5461 \| 4.0 \| 21 \| 0.4712 \| 0.8554 \| \| 0.5461 \| 4.9524 \| 26 \| 0.3943 \| 0.8916 \| \| 0.3891 \| 5.9048 \| 31 \| 0.3369 \| 0.9337 \| \| 0.3891 \| 6.8571 \| 36 \| 0.3099 \| 0.9398 \| \| 0.2976 \| 8.0 \| 42 \| 0.2811 \| 0.9578 \| \| 0.2976 \| 8.9524 \| 47 \| 0.2713 \| 0.9578 \| \| 0.2393 \| 9.5238 \| 50 \| 0.2683 \| 0.9639 \| ### Framework versions - Transformers 4.41.1 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
QuantFactory/Codestral-22B-v0.1-GGUF	QuantFactory	2024-05-30T02:41:23Z	321	10	null	[ "gguf", "code", "text-generation", "base_model:mistralai/Codestral-22B-v0.1", "base_model:quantized:mistralai/Codestral-22B-v0.1", "license:other", "region:us" ]	text-generation	2024-05-30T00:12:40Z	--- inference: false license: other license_name: mnpl license_link: https://mistral.ai/licences/MNPL-0.1.md tags: - code language: - code base_model: mistralai/Codestral-22B-v0.1 pipeline_tag: text-generation --- # QuantFactory/Codestral-22B-v0.1-GGUF - This is quantized version of [mistralai/Codestral-22B-v0.1](https://huggingface.co/mistralai/Codestral-22B-v0.1) created using llama.cpp - Thanks to @bullerwins for conversion ot HF format # Model Description Codestrall-22B-v0.1 is trained on a diverse dataset of 80+ programming languages, including the most popular ones, such as Python, Java, C, C++, JavaScript, and Bash (more details in the [Blogpost](https://mistral.ai/news/codestral/)). The model can be queried: - As instruct, for instance to answer any questions about a code snippet (write documentation, explain, factorize) or to generate code following specific indications - As Fill in the Middle (FIM), to predict the middle tokens between a prefix and a suffix (very useful for software development add-ons like in VS Code) ## Installation It is recommended to use `mistralai/Codestral-22B-v0.1` with [mistral-inference](https://github.com/mistralai/mistral-inference). ``` pip install mistral_inference ``` ## Download ```py from huggingface_hub import snapshot_download from pathlib import Path mistral_models_path = Path.home().joinpath('mistral_models', 'Codestral-22B-v0.1') mistral_models_path.mkdir(parents=True, exist_ok=True) snapshot_download(repo_id="mistralai/Codestral-22B-v0.1", allow_patterns=["params.json", "consolidated.safetensors", "tokenizer.model.v3"], local_dir=mistral_models_path) ``` ### Chat After installing `mistral_inference`, a `mistral-chat` CLI command should be available in your environment. ``` mistral-chat $HOME/mistral_models/Codestral-22B-v0.1 --instruct --max_tokens 256 ``` Will generate an answer to "Write me a function that computes fibonacci in Rust" and should give something along the following lines: ``` Sure, here's a simple implementation of a function that computes the Fibonacci sequence in Rust. This function takes an integer `n` as an argument and returns the `n`th Fibonacci number. fn fibonacci(n: u32) -> u32 { match n { 0 => 0, 1 => 1, _ => fibonacci(n - 1) + fibonacci(n - 2), } } fn main() { let n = 10; println!("The {}th Fibonacci number is: {}", n, fibonacci(n)); } This function uses recursion to calculate the Fibonacci number. However, it's not the most efficient solution because it performs a lot of redundant calculations. A more efficient solution would use a loop to iteratively calculate the Fibonacci numbers. ``` ### Fill-in-the-middle (FIM) After installing `mistral_inference` and running `pip install --upgrade mistral_common` to make sure to have mistral_common>=1.2 installed: ```py from mistral_inference.model import Transformer from mistral_inference.generate import generate from mistral_common.tokens.tokenizers.mistral import MistralTokenizer from mistral_common.tokens.instruct.request import FIMRequest tokenizer = MistralTokenizer.v3() model = Transformer.from_folder("~/codestral-22B-240529") prefix = """def add(""" suffix = """ return sum""" request = FIMRequest(prompt=prefix, suffix=suffix) tokens = tokenizer.encode_fim(request).tokens out_tokens, _ = generate([tokens], model, max_tokens=256, temperature=0.0, eos_id=tokenizer.instruct_tokenizer.tokenizer.eos_id) result = tokenizer.decode(out_tokens[0]) middle = result.split(suffix)[0].strip() print(middle) ``` Should give something along the following lines: ``` num1, num2): # Add two numbers sum = num1 + num2 # return the sum ``` ## Limitations The Codestral-22B-v0.1 does not have any moderation mechanisms. We're looking forward to engaging with the community on ways to make the model finely respect guardrails, allowing for deployment in environments requiring moderated outputs. ## License Codestral-22B-v0.1 is released under the `MNLP-0.1` license. ## The Mistral AI Team Albert Jiang, Alexandre Sablayrolles, Alexis Tacnet, Antoine Roux, Arthur Mensch, Audrey Herblin-Stoop, Baptiste Bout, Baudouin de Monicault, Blanche Savary, Bam4d, Caroline Feldman, Devendra Singh Chaplot, Diego de las Casas, Eleonore Arcelin, Emma Bou Hanna, Etienne Metzger, Gianna Lengyel, Guillaume Bour, Guillaume Lample, Harizo Rajaona, Henri Roussez, Jean-Malo Delignon, Jia Li, Justus Murke, Kartik Khandelwal, Lawrence Stewart, Louis Martin, Louis Ternon, Lucile Saulnier, Lélio Renard Lavaud, Margaret Jennings, Marie Pellat, Marie Torelli, Marie-Anne Lachaux, Marjorie Janiewicz, Mickael Seznec, Nicolas Schuhl, Patrick von Platen, Romain Sauvestre, Pierre Stock, Sandeep Subramanian, Saurabh Garg, Sophia Yang, Szymon Antoniak, Teven Le Scao, Thibaut Lavril, Thibault Schueller, Timothée Lacroix, Théophile Gervet, Thomas Wang, Valera Nemychnikova, Wendy Shang, William El Sayed, William Marshall
Sayan01/CKA-T5-CoT-b-T1	Sayan01	2024-05-30T02:35:26Z	14	0	transformers	[ "transformers", "safetensors", "t5", "text2text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2024-04-22T02:38:36Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. 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Spatiallysaying/detr-finetuned-runwaymarkings-Horizontal-v1	Spatiallysaying	2024-05-30T02:33:18Z	188	0	transformers	[ "transformers", "safetensors", "detr", "object-detection", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	object-detection	2024-04-28T02:14:25Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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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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HyperdustProtocol/HyperAuto	HyperdustProtocol	2024-05-30T02:32:30Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:unsloth/llama-2-7b-bnb-4bit", "base_model:finetune:unsloth/llama-2-7b-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-05-30T02:31:50Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl base_model: unsloth/llama-2-7b-bnb-4bit --- # Uploaded model - Developed by: HyperdustProtocol - License: apache-2.0 - Finetuned from model : unsloth/llama-2-7b-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
radia/Qwen1.5-1.8B-Q4_K_M-GGUF	radia	2024-05-30T02:29:20Z	1	0	null	[ "gguf", "pretrained", "llama-cpp", "gguf-my-repo", "text-generation", "en", "license:other", "endpoints_compatible", "region:us", "conversational" ]	text-generation	2024-05-30T02:29:13Z	--- language: - en license: other tags: - pretrained - llama-cpp - gguf-my-repo license_name: tongyi-qianwen-research license_link: https://huggingface.co/Qwen/Qwen1.5-1.8B/blob/main/LICENSE pipeline_tag: text-generation --- # radia/Qwen1.5-1.8B-Q4_K_M-GGUF This model was converted to GGUF format from [`Qwen/Qwen1.5-1.8B`](https://huggingface.co/Qwen/Qwen1.5-1.8B) 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/Qwen/Qwen1.5-1.8B) 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 radia/Qwen1.5-1.8B-Q4_K_M-GGUF --model qwen1.5-1.8b-q4_k_m.gguf -p "The meaning to life and the universe is" ``` Server: ```bash llama-server --hf-repo radia/Qwen1.5-1.8B-Q4_K_M-GGUF --model qwen1.5-1.8b-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 qwen1.5-1.8b-q4_k_m.gguf -n 128 ```

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