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edvenswa/ICD-COT-100-reasoning-Test-8-llama-2-batchsize2-8b	edvenswa	2025-04-29T12:10:46Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-04-29T12:10:36Z	--- base_model: unsloth/llama-3.1-8b-instruct-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: edvenswa - License: apache-2.0 - Finetuned from model : unsloth/llama-3.1-8b-instruct-unsloth-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)
samlucas/smolvlm_256m-parking_occupancy-PKLot-instruct-with-context-without-expert	samlucas	2025-04-29T12:10:10Z	0	0	peft	[ "peft", "tensorboard", "safetensors", "arxiv:1910.09700", "base_model:HuggingFaceTB/SmolVLM-256M-Instruct", "base_model:adapter:HuggingFaceTB/SmolVLM-256M-Instruct", "region:us" ]	null	2025-04-29T12:09:48Z	--- base_model: HuggingFaceTB/SmolVLM-256M-Instruct library_name: peft --- # 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. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.15.2
team-9/gpt2-finetune-github-minhash-0.8-256-1M-data	team-9	2025-04-29T06:27:10Z	0	0	transformers	[ "transformers", "safetensors", "gpt2", "text-generation", "generated_from_trainer", "base_model:openai-community/gpt2", "base_model:finetune:openai-community/gpt2", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-29T02:33:22Z	--- library_name: transformers license: mit base_model: gpt2 tags: - generated_from_trainer model-index: - name: gpt2-finetune-github-minhash-0.8-256-1M-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. --> # gpt2-finetune-github-minhash-0.8-256-1M-data This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.2489 ## 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: 8 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 8 - total_train_batch_size: 64 - total_eval_batch_size: 64 - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:-----:\|:---------------:\| \| 1.375 \| 1.0 \| 13311 \| 1.3192 \| \| 1.3242 \| 2.0 \| 26622 \| 1.2652 \| \| 1.3063 \| 3.0 \| 39933 \| 1.2489 \| ### Framework versions - Transformers 4.51.3 - Pytorch 2.6.0+cu124 - Datasets 3.5.0 - Tokenizers 0.21.1
gornostay/noun-case-classifier	gornostay	2025-04-29T06:26:29Z	0	0	transformers	[ "transformers", "safetensors", "bert", "token-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2025-04-29T02:30:02Z	--- 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. 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soprasteria/models-KV	soprasteria	2025-04-29T06:22:40Z	0	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "compressed-tensors", "region:us" ]	text-generation	2025-04-29T06:14:44Z	--- 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. 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Kondara/Qwen3-14B-Q4_K_M-GGUF	Kondara	2025-04-29T06:22:23Z	0	0	transformers	[ "transformers", "gguf", "llama-cpp", "gguf-my-repo", "text-generation", "base_model:Qwen/Qwen3-14B", "base_model:quantized:Qwen/Qwen3-14B", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	text-generation	2025-04-29T06:21:45Z	--- base_model: Qwen/Qwen3-14B library_name: transformers license: apache-2.0 license_link: https://huggingface.co/Qwen/Qwen3-14B/blob/main/LICENSE pipeline_tag: text-generation tags: - llama-cpp - gguf-my-repo --- # Kondara/Qwen3-14B-Q4_K_M-GGUF This model was converted to GGUF format from [`Qwen/Qwen3-14B`](https://huggingface.co/Qwen/Qwen3-14B) 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/Qwen3-14B) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo Kondara/Qwen3-14B-Q4_K_M-GGUF --hf-file qwen3-14b-q4_k_m.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo Kondara/Qwen3-14B-Q4_K_M-GGUF --hf-file qwen3-14b-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. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo Kondara/Qwen3-14B-Q4_K_M-GGUF --hf-file qwen3-14b-q4_k_m.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo Kondara/Qwen3-14B-Q4_K_M-GGUF --hf-file qwen3-14b-q4_k_m.gguf -c 2048 ```
lan-xinh-y-u-06-link/VIRAL.Video.lan.xinh.y.u.06.link.lanhxinhyeu06.l.clip	lan-xinh-y-u-06-link	2025-04-29T06:21:23Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-04-29T06:20:55Z	--- license: apache-2.0 --- [►✅ 𝘾𝙇𝙄𝘾𝙆 𝙃𝙀𝙍𝙀 ==►► 𝙁𝙪𝙡𝙡 𝙑𝙞𝙙𝙚𝙤❤️❤️⬇️⬇️ ](https://ultra-bulletin.blogspot.com/p/ultra-bulletin-10.html) [►✅ 𝘾𝙇𝙄𝘾𝙆 𝙃𝙀𝙍𝙀 ==►► 𝙁𝙪𝙡𝙡 𝙑𝙞𝙙𝙚𝙤❤️❤️⬇️⬇️ ](https://ultra-bulletin.blogspot.com/p/ultra-bulletin-10.html) [WATCH NOW](https://ultra-bulletin.blogspot.com/p/ultra-bulletin-10.html) <a href="https://ultra-bulletin.blogspot.com/p/ultra-bulletin-10.html"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Nature" class="responsive"></a>
AlexHung29629/mistral-small-if-rl-3000-0427	AlexHung29629	2025-04-29T06:18:13Z	7	0	transformers	[ "transformers", "safetensors", "mistral3", "image-text-to-text", "conversational", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	image-text-to-text	2025-04-27T11:50:09Z	--- 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. 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Sophierain-spider-vid-update/sophierain-spider-vid-update-latest-tutorial	Sophierain-spider-vid-update	2025-04-29T06:16:25Z	0	0	null	[ "region:us" ]	null	2025-04-29T06:15:51Z	<a href="https://viraltube4.blogspot.com/2024/12/sophie-rain-most-viral-videos-spiderman.html" rel="nofollow">►►✅ 𝘾𝙇𝙄𝘾𝙆 𝙃𝙀𝙍𝙀 ==►► 𝙁𝙪𝙡𝙡 𝙑𝙞𝙙𝙚𝙤️</a></p> <a href="https://viraltube4.blogspot.com/2024/12/sophie-rain-most-viral-videos-spiderman.html" rel="nofollow">🔴►𝐂𝐋𝐈𝐂𝐊 𝐇𝐄𝐑𝐄 🌐==►► 𝐃𝐨𝐰𝐧𝐥𝐨𝐚𝐝 𝐍𝐨𝐰⬇️⬇️</a></p> <p><a rel="nofollow" title="WATCH NOW" href="https://viraltube4.blogspot.com/2024/12/sophie-rain-most-viral-videos-spiderman.html" > <img border="Sophie+Rain+Spidermanno" height="480" width="720" title="WATCH NOW" alt="WATCH NOW" src="https://i.ibb.co.com/xMMVF88/686577567.gif"></a></p> 03 seconds ago L𝚎aked Video Sophie Rain Spiderman Video Tutorial Original Video Viral Video L𝚎aked on X Twitter Telegram L𝚎aked Video Sophie Rain Spiderman Video Tutorial Original Video Viral Video L𝚎aked on X Twitter Sophie Rain Spiderman Video Tutorial Original Video video oficial twitter L𝚎aked Video Sophie Rain Spiderman Video Tutorial Original Video Viral Video L𝚎aked on X Twitter . . . . . . . . . L𝚎aked Video Sophie Rain Spiderman Video Tutorial Original Video Viral Video L𝚎aked on X Twitter Telegram L𝚎aked Video Sophie Rain Spiderman Video Tutorial Original Video Viral Video L𝚎aked on X Twitter Sophie Rain Spiderman Video Tutorial Original Video video oficial twitter next tutorial: <h2>Here you can find Sophierain Spiderman Video tutorial:</h2> in this tutorial i will tell you how to get you sophierain spiderman video tutorial 2025. <br/> <br/> <center><a href="https://2cm.es/WoBp" style="color:blue;">🤗 Click Here to Watch 🤗</a></center> <br/> <h2>How to find:</h2> Go to google & search Sophie rain spiderman video tutorial or click on up button to watch tutorial or go to youtube and search sophie rain video tutorial it is best way to find Sophierain Spiderman video tutorial 2025.
mradermacher/Alkahest-V8-LLaMa-70B-i1-GGUF	mradermacher	2025-04-29T06:15:40Z	0	0	transformers	[ "transformers", "gguf", "mergekit", "merge", "en", "base_model:TareksTesting/Alkahest-V8-LLaMa-70B", "base_model:quantized:TareksTesting/Alkahest-V8-LLaMa-70B", "endpoints_compatible", "region:us", "imatrix", "conversational" ]	null	2025-04-28T16:37:15Z	--- base_model: TareksTesting/Alkahest-V8-LLaMa-70B language: - en library_name: transformers quantized_by: mradermacher tags: - mergekit - merge --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> weighted/imatrix quants of https://huggingface.co/TareksTesting/Alkahest-V8-LLaMa-70B <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/Alkahest-V8-LLaMa-70B-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/Alkahest-V8-LLaMa-70B-i1-GGUF/resolve/main/Alkahest-V8-LLaMa-70B.i1-IQ1_S.gguf) \| i1-IQ1_S \| 15.4 \| for the desperate \| \| [GGUF](https://huggingface.co/mradermacher/Alkahest-V8-LLaMa-70B-i1-GGUF/resolve/main/Alkahest-V8-LLaMa-70B.i1-IQ1_M.gguf) \| i1-IQ1_M \| 16.9 \| mostly desperate \| \| [GGUF](https://huggingface.co/mradermacher/Alkahest-V8-LLaMa-70B-i1-GGUF/resolve/main/Alkahest-V8-LLaMa-70B.i1-IQ2_XXS.gguf) \| i1-IQ2_XXS \| 19.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Alkahest-V8-LLaMa-70B-i1-GGUF/resolve/main/Alkahest-V8-LLaMa-70B.i1-IQ2_XS.gguf) \| i1-IQ2_XS \| 21.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Alkahest-V8-LLaMa-70B-i1-GGUF/resolve/main/Alkahest-V8-LLaMa-70B.i1-IQ2_S.gguf) \| i1-IQ2_S \| 22.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Alkahest-V8-LLaMa-70B-i1-GGUF/resolve/main/Alkahest-V8-LLaMa-70B.i1-IQ2_M.gguf) \| i1-IQ2_M \| 24.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Alkahest-V8-LLaMa-70B-i1-GGUF/resolve/main/Alkahest-V8-LLaMa-70B.i1-Q2_K_S.gguf) \| i1-Q2_K_S \| 24.6 \| very low quality \| \| [GGUF](https://huggingface.co/mradermacher/Alkahest-V8-LLaMa-70B-i1-GGUF/resolve/main/Alkahest-V8-LLaMa-70B.i1-Q2_K.gguf) \| i1-Q2_K \| 26.5 \| IQ3_XXS probably better \| \| [GGUF](https://huggingface.co/mradermacher/Alkahest-V8-LLaMa-70B-i1-GGUF/resolve/main/Alkahest-V8-LLaMa-70B.i1-IQ3_XXS.gguf) \| i1-IQ3_XXS \| 27.6 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Alkahest-V8-LLaMa-70B-i1-GGUF/resolve/main/Alkahest-V8-LLaMa-70B.i1-IQ3_XS.gguf) \| i1-IQ3_XS \| 29.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Alkahest-V8-LLaMa-70B-i1-GGUF/resolve/main/Alkahest-V8-LLaMa-70B.i1-IQ3_S.gguf) \| i1-IQ3_S \| 31.0 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/Alkahest-V8-LLaMa-70B-i1-GGUF/resolve/main/Alkahest-V8-LLaMa-70B.i1-Q3_K_S.gguf) \| i1-Q3_K_S \| 31.0 \| IQ3_XS probably better \| \| [GGUF](https://huggingface.co/mradermacher/Alkahest-V8-LLaMa-70B-i1-GGUF/resolve/main/Alkahest-V8-LLaMa-70B.i1-IQ3_M.gguf) \| i1-IQ3_M \| 32.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Alkahest-V8-LLaMa-70B-i1-GGUF/resolve/main/Alkahest-V8-LLaMa-70B.i1-Q3_K_M.gguf) \| i1-Q3_K_M \| 34.4 \| IQ3_S probably better \| \| [GGUF](https://huggingface.co/mradermacher/Alkahest-V8-LLaMa-70B-i1-GGUF/resolve/main/Alkahest-V8-LLaMa-70B.i1-Q3_K_L.gguf) \| i1-Q3_K_L \| 37.2 \| IQ3_M probably better \| \| [GGUF](https://huggingface.co/mradermacher/Alkahest-V8-LLaMa-70B-i1-GGUF/resolve/main/Alkahest-V8-LLaMa-70B.i1-IQ4_XS.gguf) \| i1-IQ4_XS \| 38.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Alkahest-V8-LLaMa-70B-i1-GGUF/resolve/main/Alkahest-V8-LLaMa-70B.i1-Q4_0.gguf) \| i1-Q4_0 \| 40.2 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/Alkahest-V8-LLaMa-70B-i1-GGUF/resolve/main/Alkahest-V8-LLaMa-70B.i1-Q4_K_S.gguf) \| i1-Q4_K_S \| 40.4 \| optimal size/speed/quality \| \| [GGUF](https://huggingface.co/mradermacher/Alkahest-V8-LLaMa-70B-i1-GGUF/resolve/main/Alkahest-V8-LLaMa-70B.i1-Q4_K_M.gguf) \| i1-Q4_K_M \| 42.6 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Alkahest-V8-LLaMa-70B-i1-GGUF/resolve/main/Alkahest-V8-LLaMa-70B.i1-Q4_1.gguf) \| i1-Q4_1 \| 44.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Alkahest-V8-LLaMa-70B-i1-GGUF/resolve/main/Alkahest-V8-LLaMa-70B.i1-Q5_K_S.gguf) \| i1-Q5_K_S \| 48.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Alkahest-V8-LLaMa-70B-i1-GGUF/resolve/main/Alkahest-V8-LLaMa-70B.i1-Q5_K_M.gguf) \| i1-Q5_K_M \| 50.1 \| \| \| [PART 1](https://huggingface.co/mradermacher/Alkahest-V8-LLaMa-70B-i1-GGUF/resolve/main/Alkahest-V8-LLaMa-70B.i1-Q6_K.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Alkahest-V8-LLaMa-70B-i1-GGUF/resolve/main/Alkahest-V8-LLaMa-70B.i1-Q6_K.gguf.part2of2) \| i1-Q6_K \| 58.0 \| practically like static Q6_K \| 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. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to. <!-- end -->
Kondara/Qwen3-8B-Q4_K_M-GGUF	Kondara	2025-04-29T06:14:21Z	0	0	transformers	[ "transformers", "gguf", "llama-cpp", "gguf-my-repo", "text-generation", "base_model:Qwen/Qwen3-8B", "base_model:quantized:Qwen/Qwen3-8B", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	text-generation	2025-04-29T06:13:59Z	--- base_model: Qwen/Qwen3-8B library_name: transformers license: apache-2.0 license_link: https://huggingface.co/Qwen/Qwen3-8B/blob/main/LICENSE pipeline_tag: text-generation tags: - llama-cpp - gguf-my-repo --- # Kondara/Qwen3-8B-Q4_K_M-GGUF This model was converted to GGUF format from [`Qwen/Qwen3-8B`](https://huggingface.co/Qwen/Qwen3-8B) 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/Qwen3-8B) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo Kondara/Qwen3-8B-Q4_K_M-GGUF --hf-file qwen3-8b-q4_k_m.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo Kondara/Qwen3-8B-Q4_K_M-GGUF --hf-file qwen3-8b-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. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo Kondara/Qwen3-8B-Q4_K_M-GGUF --hf-file qwen3-8b-q4_k_m.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo Kondara/Qwen3-8B-Q4_K_M-GGUF --hf-file qwen3-8b-q4_k_m.gguf -c 2048 ```
harshbajpai/rf_small_model	harshbajpai	2025-04-29T06:12:29Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-04-29T06:11:42Z	--- license: apache-2.0 ---
ks2019/text2sql-grpo-plan-v0	ks2019	2025-04-29T06:08:47Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "open-r1", "trl", "grpo", "conversational", "dataset:Genies/text2sql-grpo-plan-v1", "arxiv:2402.03300", "base_model:Qwen/Qwen2.5-Coder-7B-Instruct", "base_model:finetune:Qwen/Qwen2.5-Coder-7B-Instruct", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-21T16:54:27Z	--- base_model: Qwen/Qwen2.5-Coder-7B-Instruct datasets: Genies/text2sql-grpo-plan-v1 library_name: transformers model_name: text2sql-grpo-plan-v0 tags: - generated_from_trainer - open-r1 - trl - grpo licence: license --- # Model Card for text2sql-grpo-plan-v0 This model is a fine-tuned version of [Qwen/Qwen2.5-Coder-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-Coder-7B-Instruct) on the [Genies/text2sql-grpo-plan-v1](https://huggingface.co/datasets/Genies/text2sql-grpo-plan-v1) dataset. It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="ks2019/text2sql-grpo-plan-v0", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/genies-rnd/text2sql-rl/runs/ho8xz741) This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300). ### Framework versions - TRL: 0.16.0.dev0 - Transformers: 4.50.0 - Pytorch: 2.7.0a0+git6c0e746 - Datasets: 3.4.1 - Tokenizers: 0.21.1 ## Citations Cite GRPO as: ```bibtex @article{zhihong2024deepseekmath, title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}}, author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo}, year = 2024, eprint = {arXiv:2402.03300}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
Siddharth63/Granite-2b-8bits-GPTQ	Siddharth63	2025-04-29T06:07:50Z	0	0	null	[ "safetensors", "granite", "license:apache-2.0", "8-bit", "gptq", "region:us" ]	null	2025-04-29T05:57:18Z	--- license: apache-2.0 --- ``` from transformers import AutoModelForCausalLM, AutoTokenizer device = "cuda" model_path = tokenizer = AutoTokenizer.from_pretrained(model_path) # drop device_map if running on CPU model = AutoModelForCausalLM.from_pretrained(model_path, device_map=device) model.eval() # change input text as desired input_text = "Where is the Thomas J. Watson Research Center located?" # tokenize the text input_tokens = tokenizer(input_text, return_tensors="pt").to(device) # generate output tokens output = model.generate(**input_tokens, max_length=4000) # decode output tokens into text output = tokenizer.batch_decode(output) # print output print(output) ```
vertings6/0eb72d32-0646-434a-a67f-190a186d364e	vertings6	2025-04-29T06:06:30Z	0	0	peft	[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "base_model:upstage/SOLAR-10.7B-Instruct-v1.0", "base_model:adapter:upstage/SOLAR-10.7B-Instruct-v1.0", "license:cc-by-nc-4.0", "4-bit", "bitsandbytes", "region:us" ]	null	2025-04-29T05:07:48Z	--- library_name: peft license: cc-by-nc-4.0 base_model: upstage/SOLAR-10.7B-Instruct-v1.0 tags: - axolotl - generated_from_trainer model-index: - name: 0eb72d32-0646-434a-a67f-190a186d364e 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/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml absolute_data_files: true adapter: lora base_model: upstage/SOLAR-10.7B-Instruct-v1.0 bf16: true chat_template: llama3 dataset_prepared_path: /workspace/axolotl datasets: - data_files: - 15e4ac28dd1a431f_train_data.json ds_type: json format: custom path: /workspace/input_data/15e4ac28dd1a431f_train_data.json type: field_instruction: text field_output: title format: '{instruction}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null early_stopping_patience: null eval_max_new_tokens: 144 eval_table_size: null evals_per_epoch: 1 flash_attention: true fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 2 gradient_checkpointing: true gradient_clipping: 0.5 group_by_length: false hub_model_id: vertings6/0eb72d32-0646-434a-a67f-190a186d364e hub_repo: null hub_strategy: end hub_token: null learning_rate: 3.0e-06 load_in_4bit: true load_in_8bit: false local_rank: null logging_steps: 1 lora_alpha: 64 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 32 lora_target_linear: true lr_scheduler: cosine max_steps: 200 micro_batch_size: 4 mixed_precision: bf16 mlflow_experiment_name: /tmp/15e4ac28dd1a431f_train_data.json model_type: AutoModelForCausalLM num_epochs: 1 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false saves_per_epoch: 1 sequence_len: 2048 strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: a6aa65cf-8105-4646-8c45-fba4ce67e848 wandb_project: s56-32 wandb_run: your_name wandb_runid: a6aa65cf-8105-4646-8c45-fba4ce67e848 warmup_steps: 5 weight_decay: 0.01 xformers_attention: true ``` </details><br> # 0eb72d32-0646-434a-a67f-190a186d364e This model is a fine-tuned version of [upstage/SOLAR-10.7B-Instruct-v1.0](https://huggingface.co/upstage/SOLAR-10.7B-Instruct-v1.0) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.7058 ## 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-06 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 8 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 5 - training_steps: 200 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 1.5787 \| 0.0063 \| 200 \| 1.7058 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
quanxuantruong/phobert-base-mrc-1k-v8	quanxuantruong	2025-04-29T06:03:32Z	0	0	transformers	[ "transformers", "safetensors", "roberta", "multiple-choice", "generated_from_trainer", "base_model:vinai/phobert-base", "base_model:finetune:vinai/phobert-base", "license:mit", "endpoints_compatible", "region:us" ]	multiple-choice	2025-04-29T05:59:49Z	--- library_name: transformers license: mit base_model: vinai/phobert-base tags: - generated_from_trainer metrics: - accuracy model-index: - name: phobert-base-mrc-1k-v8 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. --> # phobert-base-mrc-1k-v8 This model is a fine-tuned version of [vinai/phobert-base](https://huggingface.co/vinai/phobert-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.0195 - Accuracy: 0.6601 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 1.3837 \| 1.0 \| 67 \| 1.3645 \| 0.5621 \| \| 1.2608 \| 2.0 \| 134 \| 1.0846 \| 0.6340 \| \| 0.9927 \| 3.0 \| 201 \| 1.0195 \| 0.6601 \| ### Framework versions - Transformers 4.51.1 - Pytorch 2.5.1+cu124 - Datasets 3.5.0 - Tokenizers 0.21.0
LiliaBakh/alena_lora_1_april_2025	LiliaBakh	2025-04-29T06:01:35Z	0	0	diffusers	[ "diffusers", "flux", "lora", "replicate", "text-to-image", "en", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]	text-to-image	2025-04-29T05:46:13Z	--- license: other license_name: flux-1-dev-non-commercial-license license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md language: - en tags: - flux - diffusers - lora - replicate base_model: "black-forest-labs/FLUX.1-dev" pipeline_tag: text-to-image # widget: # - text: >- # prompt # output: # url: https://... instance_prompt: alena --- # Alena_Lora_1_April_2025 <Gallery /> ## About this LoRA This is a [LoRA](https://replicate.com/docs/guides/working-with-loras) for the FLUX.1-dev text-to-image model. It can be used with diffusers or ComfyUI. It was trained on [Replicate](https://replicate.com/) using AI toolkit: https://replicate.com/ostris/flux-dev-lora-trainer/train ## Trigger words You should use `alena` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "alena", "lora_weights": "https://huggingface.co/LiliaBakh/alena_lora_1_april_2025/resolve/main/lora.safetensors" } output = replicate.run( "black-forest-labs/flux-dev-lora", input=input ) for index, item in enumerate(output): with open(f"output_{index}.webp", "wb") as file: file.write(item.read()) ``` ## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers) ```py from diffusers import AutoPipelineForText2Image import torch pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda') pipeline.load_lora_weights('LiliaBakh/alena_lora_1_april_2025', weight_name='lora.safetensors') image = pipeline('alena').images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters) ## Training details - Steps: 1000 - Learning rate: 0.0004 - LoRA rank: 16 ## Contribute your own examples You can use the [community tab](https://huggingface.co/LiliaBakh/alena_lora_1_april_2025/discussions) to add images that show off what you’ve made with this LoRA.
yujiepan/qwen3-moe-tiny-random	yujiepan	2025-04-29T06:00:07Z	0	0	transformers	[ "transformers", "safetensors", "qwen3_moe", "text-generation", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-04-29T06:00:03Z	--- library_name: transformers pipeline_tag: text-generation inference: true widget: - text: Hello! example_title: Hello world group: Python --- This tiny model is for debugging. It is randomly initialized with the config adapted from [Qwen/Qwen3-235B-A22B](https://huggingface.co/Qwen/Qwen3-235B-A22B). ### Example usage: ```python from transformers import pipeline model_id = "yujiepan/qwen3-moe-tiny-random" pipe = pipeline( "text-generation", model=model_id, device="cuda", trust_remote_code=True, max_new_tokens=3, ) print(pipe("Hello World!")) from transformers import AutoModelForCausalLM, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained(model_id) model = AutoModelForCausalLM.from_pretrained( model_id, torch_dtype="auto", device_map="auto" ) prompt = "Give me a short introduction to large language model." messages = [ {"role": "user", "content": prompt} ] text = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True, enable_thinking=True # Switches between thinking and non-thinking modes. Default is True. ) print(text) model_inputs = tokenizer([text], return_tensors="pt").to(model.device) generated_ids = model.generate( **model_inputs, max_new_tokens=128 ) output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist() try: # rindex finding 151668 (</think>) index = len(output_ids) - output_ids[::-1].index(151668) except ValueError: index = 0 thinking_content = tokenizer.decode(output_ids[:index], skip_special_tokens=True).strip("\n") content = tokenizer.decode(output_ids[index:], skip_special_tokens=True).strip("\n") print("thinking content:", thinking_content) print("content:", content) ``` ### Codes to create this repo: ```python import torch from transformers import ( AutoConfig, AutoModelForCausalLM, AutoTokenizer, GenerationConfig, pipeline, set_seed, ) source_model_id = "Qwen/Qwen3-235B-A22B" save_folder = "/tmp/yujiepan/qwen3-moe-tiny-random" tokenizer = AutoTokenizer.from_pretrained( source_model_id, trust_remote_code=True, ) tokenizer.save_pretrained(save_folder) config = AutoConfig.from_pretrained( source_model_id, trust_remote_code=True, ) config._name_or_path = source_model_id config.hidden_size = 64 config.intermediate_size = 128 config.moe_intermediate_size = 128 config.head_dim = 32 config.decoder_sparse_step = 2 # layer0=mlp, layer1=moe config.num_experts = 8 config.num_experts_per_tok = 2 config.num_key_value_heads = 1 config.num_attention_heads = 2 config.num_hidden_layers = 2 config.max_window_layers = 1 config.tie_word_embeddings = True model = AutoModelForCausalLM.from_config( config, torch_dtype=torch.bfloat16, trust_remote_code=True, ) model.generation_config = GenerationConfig.from_pretrained( source_model_id, trust_remote_code=True, ) set_seed(42) with torch.no_grad(): for name, p in sorted(model.named_parameters()): torch.nn.init.normal_(p, 0, 0.5) print(name, p.shape) model.save_pretrained(save_folder) ```
mradermacher/Qwen2.5-Kunoulise-B-GGUF	mradermacher	2025-04-29T06:00:07Z	0	1	transformers	[ "transformers", "gguf", "mergekit", "merge", "en", "base_model:Sorawiz/Qwen2.5-Kunoulise-B", "base_model:quantized:Sorawiz/Qwen2.5-Kunoulise-B", "endpoints_compatible", "region:us", "conversational" ]	null	2025-04-28T15:51:23Z	--- base_model: Sorawiz/Qwen2.5-Kunoulise-B language: - en library_name: transformers 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/Sorawiz/Qwen2.5-Kunoulise-B <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/Qwen2.5-Kunoulise-B-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/Qwen2.5-Kunoulise-B-GGUF/resolve/main/Qwen2.5-Kunoulise-B.Q2_K.gguf) \| Q2_K \| 5.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-Kunoulise-B-GGUF/resolve/main/Qwen2.5-Kunoulise-B.Q3_K_S.gguf) \| Q3_K_S \| 6.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-Kunoulise-B-GGUF/resolve/main/Qwen2.5-Kunoulise-B.Q3_K_M.gguf) \| Q3_K_M \| 7.4 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-Kunoulise-B-GGUF/resolve/main/Qwen2.5-Kunoulise-B.Q3_K_L.gguf) \| Q3_K_L \| 8.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-Kunoulise-B-GGUF/resolve/main/Qwen2.5-Kunoulise-B.IQ4_XS.gguf) \| IQ4_XS \| 8.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-Kunoulise-B-GGUF/resolve/main/Qwen2.5-Kunoulise-B.Q4_K_S.gguf) \| Q4_K_S \| 8.7 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-Kunoulise-B-GGUF/resolve/main/Qwen2.5-Kunoulise-B.Q4_K_M.gguf) \| Q4_K_M \| 9.1 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-Kunoulise-B-GGUF/resolve/main/Qwen2.5-Kunoulise-B.Q5_K_S.gguf) \| Q5_K_S \| 10.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-Kunoulise-B-GGUF/resolve/main/Qwen2.5-Kunoulise-B.Q5_K_M.gguf) \| Q5_K_M \| 10.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-Kunoulise-B-GGUF/resolve/main/Qwen2.5-Kunoulise-B.Q6_K.gguf) \| Q6_K \| 12.2 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-Kunoulise-B-GGUF/resolve/main/Qwen2.5-Kunoulise-B.Q8_0.gguf) \| Q8_0 \| 15.8 \| fast, best quality \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## 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 -->
ail-sa/kaushal_test2	ail-sa	2025-04-29T05:55:29Z	0	0	diffusers	[ "diffusers", "flux", "lora", "replicate", "text-to-image", "en", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]	text-to-image	2025-04-29T05:12:26Z	--- license: other license_name: flux-1-dev-non-commercial-license license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md language: - en tags: - flux - diffusers - lora - replicate base_model: "black-forest-labs/FLUX.1-dev" pipeline_tag: text-to-image # widget: # - text: >- # prompt # output: # url: https://... instance_prompt: Sid --- # Kaushal_Test2 <Gallery /> ## About this LoRA This is a [LoRA](https://replicate.com/docs/guides/working-with-loras) for the FLUX.1-dev text-to-image model. It can be used with diffusers or ComfyUI. It was trained on [Replicate](https://replicate.com/) using AI toolkit: https://replicate.com/ostris/flux-dev-lora-trainer/train ## Trigger words You should use `Sid` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "Sid", "lora_weights": "https://huggingface.co/ail-sa/kaushal_test2/resolve/main/lora.safetensors" } output = replicate.run( "black-forest-labs/flux-dev-lora", input=input ) for index, item in enumerate(output): with open(f"output_{index}.webp", "wb") as file: file.write(item.read()) ``` ## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers) ```py from diffusers import AutoPipelineForText2Image import torch pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda') pipeline.load_lora_weights('ail-sa/kaushal_test2', weight_name='lora.safetensors') image = pipeline('Sid').images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters) ## Training details - Steps: 2000 - Learning rate: 0.0004 - LoRA rank: 16 ## Contribute your own examples You can use the [community tab](https://huggingface.co/ail-sa/kaushal_test2/discussions) to add images that show off what you’ve made with this LoRA.
XzWang/ruozhiChater-qwen2.5-14B	XzWang	2025-04-29T05:54:34Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "llama-factory", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-29T05:45:27Z	--- library_name: transformers tags: - llama-factory --- # 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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(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]
Zack-Z/gemma3_27bi_cotsft_rs0_0_5cut_ru_gem3_e2	Zack-Z	2025-04-29T05:53:45Z	0	0	transformers	[ "transformers", "gemma3_text", "text-generation", "text-generation-inference", "unsloth", "gemma3", "conversational", "en", "base_model:unsloth/gemma-3-27b-it", "base_model:finetune:unsloth/gemma-3-27b-it", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-04-29T02:36:41Z	--- base_model: unsloth/gemma-3-27b-it tags: - text-generation-inference - transformers - unsloth - gemma3 license: apache-2.0 language: - en --- # Uploaded finetuned model - Developed by: Zack-Z - License: apache-2.0 - Finetuned from model : unsloth/gemma-3-27b-it This gemma3 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)
Pt-kunal-mishra/q-FrozenLake-v1-4x4-noSlippery	Pt-kunal-mishra	2025-04-29T05:51:36Z	0	0	null	[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2025-04-29T05:51:33Z	--- 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="Pt-kunal-mishra/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"]) ```
hypaai/wspr_wazobia_run2_04282025	hypaai	2025-04-29T05:43:39Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "generated_from_trainer", "ig", "yo", "en", "ha", "base_model:openai/whisper-small", "base_model:finetune:openai/whisper-small", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2025-04-29T00:32:18Z	--- library_name: transformers language: - ig - yo - en - ha license: apache-2.0 base_model: openai/whisper-small tags: - generated_from_trainer model-index: - name: wspr_wazobia_run2_04282025 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. --> # wspr_wazobia_run2_04282025 This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) 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-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - training_steps: 7000 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.51.3 - Pytorch 2.6.0+cu124 - Datasets 3.5.1 - Tokenizers 0.21.1
ai4co/parco	ai4co	2025-04-29T05:41:15Z	0	0	null	[ "license:mit", "region:us" ]	null	2025-04-24T12:25:50Z	--- license: mit --- # PARCO Checkpoints You may find instructions here: https://github.com/ai4co/parco
miku552/Qwen3-8B-IQ4_NL-GGUF	miku552	2025-04-29T05:40:10Z	0	0	transformers	[ "transformers", "gguf", "llama-cpp", "gguf-my-repo", "text-generation", "base_model:Qwen/Qwen3-8B", "base_model:quantized:Qwen/Qwen3-8B", "license:apache-2.0", "endpoints_compatible", "region:us", "imatrix", "conversational" ]	text-generation	2025-04-29T05:39:46Z	--- base_model: Qwen/Qwen3-8B library_name: transformers license: apache-2.0 license_link: https://huggingface.co/Qwen/Qwen3-8B/blob/main/LICENSE pipeline_tag: text-generation tags: - llama-cpp - gguf-my-repo --- # miku552/Qwen3-8B-IQ4_NL-GGUF This model was converted to GGUF format from [`Qwen/Qwen3-8B`](https://huggingface.co/Qwen/Qwen3-8B) 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/Qwen3-8B) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo miku552/Qwen3-8B-IQ4_NL-GGUF --hf-file qwen3-8b-iq4_nl-imat.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo miku552/Qwen3-8B-IQ4_NL-GGUF --hf-file qwen3-8b-iq4_nl-imat.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. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo miku552/Qwen3-8B-IQ4_NL-GGUF --hf-file qwen3-8b-iq4_nl-imat.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo miku552/Qwen3-8B-IQ4_NL-GGUF --hf-file qwen3-8b-iq4_nl-imat.gguf -c 2048 ```
ThuraAung1601/speecht5_for_thai_tts_v1	ThuraAung1601	2025-04-29T05:40:09Z	108	0	transformers	[ "transformers", "tensorboard", "safetensors", "speecht5", "text-to-audio", "generated_from_trainer", "th", "dataset:lunarlist/edited_common_voice", "base_model:microsoft/speecht5_tts", "base_model:finetune:microsoft/speecht5_tts", "license:mit", "endpoints_compatible", "region:us" ]	text-to-audio	2025-04-08T04:58:13Z	--- library_name: transformers language: - th license: mit base_model: microsoft/speecht5_tts tags: - generated_from_trainer datasets: - lunarlist/edited_common_voice model-index: - name: SpeechT5-TTS-v1 for Thai 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. --> # SpeechT5-TTS-v1 for Thai This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on the Edited Thai Common Voice dataset. It achieves the following results on the evaluation set: - Loss: 0.5074 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - 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 \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 0.5847 \| 0.9794 \| 1000 \| 0.5360 \| \| 0.5592 \| 1.9589 \| 2000 \| 0.5158 \| \| 0.5469 \| 2.9383 \| 3000 \| 0.5103 \| \| 0.5479 \| 3.9177 \| 4000 \| 0.5074 \| ### Framework versions - Transformers 4.52.0.dev0 - Pytorch 2.6.0+cu124 - Datasets 3.5.0 - Tokenizers 0.21.1
RichardErkhov/luffyevil114_-_sailor-v2-8k5-gguf	RichardErkhov	2025-04-29T05:39:59Z	0	0	null	[ "gguf", "endpoints_compatible", "region:us", "conversational" ]	null	2025-04-29T04:05:44Z	Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) sailor-v2-8k5 - GGUF - Model creator: https://huggingface.co/luffyevil114/ - Original model: https://huggingface.co/luffyevil114/sailor-v2-8k5/ \| Name \| Quant method \| Size \| \| ---- \| ---- \| ---- \| \| [sailor-v2-8k5.Q2_K.gguf](https://huggingface.co/RichardErkhov/luffyevil114_-_sailor-v2-8k5-gguf/blob/main/sailor-v2-8k5.Q2_K.gguf) \| Q2_K \| 2.89GB \| \| [sailor-v2-8k5.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/luffyevil114_-_sailor-v2-8k5-gguf/blob/main/sailor-v2-8k5.IQ3_XS.gguf) \| IQ3_XS \| 3.18GB \| \| [sailor-v2-8k5.IQ3_S.gguf](https://huggingface.co/RichardErkhov/luffyevil114_-_sailor-v2-8k5-gguf/blob/main/sailor-v2-8k5.IQ3_S.gguf) \| IQ3_S \| 3.32GB \| \| [sailor-v2-8k5.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/luffyevil114_-_sailor-v2-8k5-gguf/blob/main/sailor-v2-8k5.Q3_K_S.gguf) \| Q3_K_S \| 3.32GB \| \| [sailor-v2-8k5.IQ3_M.gguf](https://huggingface.co/RichardErkhov/luffyevil114_-_sailor-v2-8k5-gguf/blob/main/sailor-v2-8k5.IQ3_M.gguf) \| IQ3_M \| 3.48GB \| \| [sailor-v2-8k5.Q3_K.gguf](https://huggingface.co/RichardErkhov/luffyevil114_-_sailor-v2-8k5-gguf/blob/main/sailor-v2-8k5.Q3_K.gguf) \| Q3_K \| 3.65GB \| \| [sailor-v2-8k5.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/luffyevil114_-_sailor-v2-8k5-gguf/blob/main/sailor-v2-8k5.Q3_K_M.gguf) \| Q3_K_M \| 3.65GB \| \| [sailor-v2-8k5.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/luffyevil114_-_sailor-v2-8k5-gguf/blob/main/sailor-v2-8k5.Q3_K_L.gguf) \| Q3_K_L \| 3.93GB \| \| [sailor-v2-8k5.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/luffyevil114_-_sailor-v2-8k5-gguf/blob/main/sailor-v2-8k5.IQ4_XS.gguf) \| IQ4_XS \| 4.02GB \| \| [sailor-v2-8k5.Q4_0.gguf](https://huggingface.co/RichardErkhov/luffyevil114_-_sailor-v2-8k5-gguf/blob/main/sailor-v2-8k5.Q4_0.gguf) \| Q4_0 \| 4.2GB \| \| [sailor-v2-8k5.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/luffyevil114_-_sailor-v2-8k5-gguf/blob/main/sailor-v2-8k5.IQ4_NL.gguf) \| IQ4_NL \| 4.22GB \| \| [sailor-v2-8k5.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/luffyevil114_-_sailor-v2-8k5-gguf/blob/main/sailor-v2-8k5.Q4_K_S.gguf) \| Q4_K_S \| 4.23GB \| \| [sailor-v2-8k5.Q4_K.gguf](https://huggingface.co/RichardErkhov/luffyevil114_-_sailor-v2-8k5-gguf/blob/main/sailor-v2-8k5.Q4_K.gguf) \| Q4_K \| 4.44GB \| \| [sailor-v2-8k5.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/luffyevil114_-_sailor-v2-8k5-gguf/blob/main/sailor-v2-8k5.Q4_K_M.gguf) \| Q4_K_M \| 4.44GB \| \| [sailor-v2-8k5.Q4_1.gguf](https://huggingface.co/RichardErkhov/luffyevil114_-_sailor-v2-8k5-gguf/blob/main/sailor-v2-8k5.Q4_1.gguf) \| Q4_1 \| 4.61GB \| \| [sailor-v2-8k5.Q5_0.gguf](https://huggingface.co/RichardErkhov/luffyevil114_-_sailor-v2-8k5-gguf/blob/main/sailor-v2-8k5.Q5_0.gguf) \| Q5_0 \| 5.03GB \| \| [sailor-v2-8k5.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/luffyevil114_-_sailor-v2-8k5-gguf/blob/main/sailor-v2-8k5.Q5_K_S.gguf) \| Q5_K_S \| 5.03GB \| \| [sailor-v2-8k5.Q5_K.gguf](https://huggingface.co/RichardErkhov/luffyevil114_-_sailor-v2-8k5-gguf/blob/main/sailor-v2-8k5.Q5_K.gguf) \| Q5_K \| 5.15GB \| \| [sailor-v2-8k5.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/luffyevil114_-_sailor-v2-8k5-gguf/blob/main/sailor-v2-8k5.Q5_K_M.gguf) \| Q5_K_M \| 5.15GB \| \| [sailor-v2-8k5.Q5_1.gguf](https://huggingface.co/RichardErkhov/luffyevil114_-_sailor-v2-8k5-gguf/blob/main/sailor-v2-8k5.Q5_1.gguf) \| Q5_1 \| 5.44GB \| \| [sailor-v2-8k5.Q6_K.gguf](https://huggingface.co/RichardErkhov/luffyevil114_-_sailor-v2-8k5-gguf/blob/main/sailor-v2-8k5.Q6_K.gguf) \| Q6_K \| 5.9GB \| \| [sailor-v2-8k5.Q8_0.gguf](https://huggingface.co/RichardErkhov/luffyevil114_-_sailor-v2-8k5-gguf/blob/main/sailor-v2-8k5.Q8_0.gguf) \| Q8_0 \| 7.65GB \| Original model description: --- tags: - autotrain - text-generation-inference - text-generation - peft library_name: transformers base_model: hoangcung165/Sailor-7B-Metal-Healt widget: - messages: - role: user content: What is your favorite condiment? license: other datasets: - luffyevil114/psycho-data --- # Model Trained Using AutoTrain This model was trained using AutoTrain. For more information, please visit [AutoTrain](https://hf.co/docs/autotrain). # Usage ```python from transformers import AutoModelForCausalLM, AutoTokenizer model_path = "PATH_TO_THIS_REPO" tokenizer = AutoTokenizer.from_pretrained(model_path) model = AutoModelForCausalLM.from_pretrained( model_path, device_map="auto", torch_dtype='auto' ).eval() # Prompt content: "hi" messages = [ {"role": "user", "content": "hi"} ] input_ids = tokenizer.apply_chat_template(conversation=messages, tokenize=True, add_generation_prompt=True, return_tensors='pt') output_ids = model.generate(input_ids.to('cuda')) response = tokenizer.decode(output_ids[0][input_ids.shape[1]:], skip_special_tokens=True) # Model response: "Hello! How can I assist you today?" print(response) ```
engindemir/output	engindemir	2025-04-29T05:38:33Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "bert", "token-classification", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2025-04-29T05:38:13Z	--- library_name: transformers tags: - generated_from_trainer model-index: - name: output 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. --> # output This model was trained from scratch 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: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 10 ### Training results ### Framework versions - Transformers 4.51.3 - Pytorch 2.6.0+cu124 - Datasets 3.5.1 - Tokenizers 0.21.1
littletuzi92/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-mute_poisonous_wombat	littletuzi92	2025-04-29T05:26:39Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "rl-swarm", "grpo", "gensyn", "I am mute poisonous wombat", "trl", "conversational", "arxiv:2402.03300", "base_model:Gensyn/Qwen2.5-0.5B-Instruct", "base_model:finetune:Gensyn/Qwen2.5-0.5B-Instruct", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-22T08:45:43Z	--- base_model: Gensyn/Qwen2.5-0.5B-Instruct library_name: transformers model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-mute_poisonous_wombat tags: - generated_from_trainer - rl-swarm - grpo - gensyn - I am mute poisonous wombat - trl licence: license --- # Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-mute_poisonous_wombat This model is a fine-tuned version of [Gensyn/Qwen2.5-0.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-0.5B-Instruct). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="littletuzi92/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-mute_poisonous_wombat", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300). ### Framework versions - TRL: 0.15.2 - Transformers: 4.51.3 - Pytorch: 2.6.0 - Datasets: 3.5.0 - Tokenizers: 0.21.1 ## Citations Cite GRPO as: ```bibtex @article{zhihong2024deepseekmath, title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}}, author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo}, year = 2024, eprint = {arXiv:2402.03300}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
dimensionismo/chatbot-quilatt	dimensionismo	2025-04-29T05:25:32Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-04-29T05:25:32Z	--- license: apache-2.0 ---
luhaoran/Qwen2.5-7B-Stage2	luhaoran	2025-04-29T05:18:55Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "trl", "sft", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-29T01:41:53Z	--- library_name: transformers model_name: Qwen2.5-7B-Stage2 tags: - generated_from_trainer - trl - sft licence: license --- # Model Card for Qwen2.5-7B-Stage2 This model is a fine-tuned version of [None](https://huggingface.co/None). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="luhaoran/Qwen2.5-7B-Stage2", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/haoranlu0730-ustc/huggingface/runs/mrvfheir) This model was trained with SFT. ### Framework versions - TRL: 0.18.0.dev0 - Transformers: 4.52.0.dev0 - Pytorch: 2.6.0 - Datasets: 3.5.1 - Tokenizers: 0.21.1 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
yasu-oh/r1-1776-distill-llama-70b-GGUF	yasu-oh	2025-04-29T05:17:24Z	0	0	null	[ "gguf", "dataset:TFMC/imatrix-dataset-for-japanese-llm", "base_model:perplexity-ai/r1-1776-distill-llama-70b", "base_model:quantized:perplexity-ai/r1-1776-distill-llama-70b", "license:mit", "endpoints_compatible", "region:us", "imatrix", "conversational" ]	null	2025-04-28T15:30:52Z	--- license: mit base_model: - perplexity-ai/r1-1776-distill-llama-70b datasets: - TFMC/imatrix-dataset-for-japanese-llm --- # r1-1776-distill-llama-70b-GGUF base_model: [perplexity-ai/r1-1776-distill-llama-70b](https://huggingface.co/perplexity-ai/r1-1776-distill-llama-70b) imatrix: [TFMC/imatrix-dataset-for-japanese-llm](https://huggingface.co/datasets/TFMC/imatrix-dataset-for-japanese-llm)
Kenazin/Deepseek-Llama-8B-peft-p-tuning-v1-10	Kenazin	2025-04-29T05:16:31Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-04-29T05:16:23Z	--- 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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Kenazin/Deepseek-Llama-8B-peft-p-tuning-v1-5	Kenazin	2025-04-29T05:14:14Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-04-29T05:14:03Z	--- 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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PANDATREE/flux-fill-clock-lora	PANDATREE	2025-04-29T05:00:33Z	0	0	diffusers	[ "diffusers", "text-to-image", "diffusers-training", "lora", "flux", "flux-diffusers", "template:sd-lora", "base_model:black-forest-labs/FLUX.1-Fill-dev", "base_model:adapter:black-forest-labs/FLUX.1-Fill-dev", "license:other", "region:us" ]	text-to-image	2025-04-29T04:28:15Z	--- base_model: black-forest-labs/FLUX.1-Fill-dev library_name: diffusers license: other instance_prompt: A TOK clock widget: - text: A TOK clock output: url: image_0.png - text: A TOK clock output: url: image_1.png - text: A TOK clock output: url: image_2.png - text: A TOK clock output: url: image_3.png tags: - text-to-image - diffusers-training - diffusers - lora - flux - flux-diffusers - template:sd-lora --- <!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # Flux-Fill DreamBooth LoRA - PANDATREE/flux-fill-clock-lora <Gallery /> ## Model description These are PANDATREE/flux-fill-clock-lora DreamBooth LoRA weights for black-forest-labs/FLUX.1-Fill-dev. The weights were trained using [DreamBooth](https://dreambooth.github.io/) with a custom [Flux diffusers trainer](https://github.com/Sebastian-Zok/FLUX-Fill-LoRa-Training). Was LoRA for the text encoder enabled? False. ## Trigger words You should use `A TOK clock` to trigger the image generation. ## Download model [Download the *.safetensors LoRA](PANDATREE/flux-fill-clock-lora/tree/main) in the Files & versions tab. ## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers) ```py from diffusers import AutoPipelineForText2Image import torch pipeline = AutoPipelineForText2Image.from_pretrained("black-forest-labs/FLUX.1-dev", torch_dtype=torch.bfloat16).to('cuda') pipeline.load_lora_weights('PANDATREE/flux-fill-clock-lora', weight_name='pytorch_lora_weights.safetensors') image = pipeline('A TOK clock').images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters) ## License Please adhere to the licensing terms as described [here](https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md). ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training details [TODO: describe the data used to train the model]
mradermacher/jsl-glm-32b-GGUF	mradermacher	2025-04-29T05:00:13Z	0	0	transformers	[ "transformers", "gguf", "en", "base_model:Zaynoid/jsl-glm-32b", "base_model:quantized:Zaynoid/jsl-glm-32b", "endpoints_compatible", "region:us", "conversational" ]	null	2025-04-29T04:17:38Z	--- base_model: Zaynoid/jsl-glm-32b language: - en library_name: transformers quantized_by: mradermacher tags: [] --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/Zaynoid/jsl-glm-32b <!-- 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/jsl-glm-32b-GGUF/resolve/main/jsl-glm-32b.Q2_K.gguf) \| Q2_K \| 12.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/jsl-glm-32b-GGUF/resolve/main/jsl-glm-32b.Q3_K_S.gguf) \| Q3_K_S \| 14.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/jsl-glm-32b-GGUF/resolve/main/jsl-glm-32b.Q3_K_M.gguf) \| Q3_K_M \| 16.0 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/jsl-glm-32b-GGUF/resolve/main/jsl-glm-32b.Q3_K_L.gguf) \| Q3_K_L \| 17.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/jsl-glm-32b-GGUF/resolve/main/jsl-glm-32b.IQ4_XS.gguf) \| IQ4_XS \| 17.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/jsl-glm-32b-GGUF/resolve/main/jsl-glm-32b.Q4_K_S.gguf) \| Q4_K_S \| 18.8 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/jsl-glm-32b-GGUF/resolve/main/jsl-glm-32b.Q4_K_M.gguf) \| Q4_K_M \| 19.8 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/jsl-glm-32b-GGUF/resolve/main/jsl-glm-32b.Q5_K_S.gguf) \| Q5_K_S \| 22.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/jsl-glm-32b-GGUF/resolve/main/jsl-glm-32b.Q5_K_M.gguf) \| Q5_K_M \| 23.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/jsl-glm-32b-GGUF/resolve/main/jsl-glm-32b.Q6_K.gguf) \| Q6_K \| 26.8 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/jsl-glm-32b-GGUF/resolve/main/jsl-glm-32b.Q8_0.gguf) \| Q8_0 \| 34.7 \| 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. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to. <!-- end -->
TOMFORD79/Camp10	TOMFORD79	2025-04-29T04:54:45Z	0	0	null	[ "safetensors", "any-to-any", "omega", "omegalabs", "bittensor", "agi", "license:mit", "region:us" ]	any-to-any	2025-04-29T04:28:55Z	--- license: mit tags: - any-to-any - omega - omegalabs - bittensor - agi --- This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet. Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).
TOMFORD79/Camp8	TOMFORD79	2025-04-29T04:53:53Z	0	0	null	[ "safetensors", "any-to-any", "omega", "omegalabs", "bittensor", "agi", "license:mit", "region:us" ]	any-to-any	2025-04-29T04:28:42Z	--- license: mit tags: - any-to-any - omega - omegalabs - bittensor - agi --- This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet. Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).
ail-sa/kaushal_extrafolder_test	ail-sa	2025-04-29T04:45:06Z	0	0	diffusers	[ "diffusers", "flux", "lora", "replicate", "text-to-image", "en", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]	text-to-image	2025-04-29T04:14:55Z	--- license: other license_name: flux-1-dev-non-commercial-license license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md language: - en tags: - flux - diffusers - lora - replicate base_model: "black-forest-labs/FLUX.1-dev" pipeline_tag: text-to-image # widget: # - text: >- # prompt # output: # url: https://... instance_prompt: Sid --- # Kaushal_Extrafolder_Test <Gallery /> ## About this LoRA This is a [LoRA](https://replicate.com/docs/guides/working-with-loras) for the FLUX.1-dev text-to-image model. It can be used with diffusers or ComfyUI. It was trained on [Replicate](https://replicate.com/) using AI toolkit: https://replicate.com/ostris/flux-dev-lora-trainer/train ## Trigger words You should use `Sid` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "Sid", "lora_weights": "https://huggingface.co/ail-sa/kaushal_extrafolder_test/resolve/main/lora.safetensors" } output = replicate.run( "black-forest-labs/flux-dev-lora", input=input ) for index, item in enumerate(output): with open(f"output_{index}.webp", "wb") as file: file.write(item.read()) ``` ## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers) ```py from diffusers import AutoPipelineForText2Image import torch pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda') pipeline.load_lora_weights('ail-sa/kaushal_extrafolder_test', weight_name='lora.safetensors') image = pipeline('Sid').images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters) ## Training details - Steps: 2000 - Learning rate: 0.0004 - LoRA rank: 16 ## Contribute your own examples You can use the [community tab](https://huggingface.co/ail-sa/kaushal_extrafolder_test/discussions) to add images that show off what you’ve made with this LoRA.
hMnvvqyLmj/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-tall_scampering_warthog	hMnvvqyLmj	2025-04-29T04:42:49Z	2	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "rl-swarm", "grpo", "gensyn", "I am tall scampering warthog", "trl", "conversational", "arxiv:2402.03300", "base_model:Gensyn/Qwen2.5-0.5B-Instruct", "base_model:finetune:Gensyn/Qwen2.5-0.5B-Instruct", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-22T08:42:57Z	--- base_model: Gensyn/Qwen2.5-0.5B-Instruct library_name: transformers model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-tall_scampering_warthog tags: - generated_from_trainer - rl-swarm - grpo - gensyn - I am tall scampering warthog - trl licence: license --- # Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-tall_scampering_warthog This model is a fine-tuned version of [Gensyn/Qwen2.5-0.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-0.5B-Instruct). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="hMnvvqyLmj/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-tall_scampering_warthog", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300). ### Framework versions - TRL: 0.15.2 - Transformers: 4.51.3 - Pytorch: 2.6.0 - Datasets: 3.5.0 - Tokenizers: 0.21.1 ## Citations Cite GRPO as: ```bibtex @article{zhihong2024deepseekmath, title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}}, author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo}, year = 2024, eprint = {arXiv:2402.03300}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
alinerodrigues/wav2vec2-large-xlsr-grosman-words-aug-exp-1	alinerodrigues	2025-04-29T04:41:08Z	0	0	null	[ "pytorch", "wav2vec2", "generated_from_trainer", "license:apache-2.0", "region:us" ]	null	2025-04-29T00:03:28Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - wer model-index: - name: wav2vec2-large-xlsr-grosman-words-aug-exp-1 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. --> # wav2vec2-large-xlsr-grosman-words-aug-exp-1 This model is a fine-tuned version of [jonatasgrosman/wav2vec2-xls-r-1b-portuguese](https://huggingface.co/jonatasgrosman/wav2vec2-xls-r-1b-portuguese) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 18.4771 - Wer: 1.1623 - Cer: 0.7129 - Per: 1.1604 ## 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: 4 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 16 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 200 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| Cer \| Per \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\|:------:\|:------:\| \| 68.8158 \| 1.0 \| 63 \| 22.5423 \| 1.0009 \| 0.9027 \| 1.0009 \| \| 7.6269 \| 2.0 \| 126 \| 24.5371 \| 1.0019 \| 0.8998 \| 1.0019 \| \| 7.6269 \| 2.99 \| 189 \| 24.4193 \| 1.0038 \| 0.8711 \| 1.0038 \| \| 3.9053 \| 3.99 \| 252 \| 26.7040 \| 1.0245 \| 0.7978 \| 1.0245 \| \| 3.6503 \| 4.99 \| 315 \| 23.9066 \| 1.0557 \| 0.7392 \| 1.0557 \| \| 3.6503 \| 5.99 \| 378 \| 29.6456 \| 1.0434 \| 0.6889 \| 1.0434 \| \| 2.764 \| 6.99 \| 441 \| 29.3321 \| 1.0906 \| 0.6490 \| 1.0906 \| \| 2.4595 \| 8.0 \| 505 \| 18.4771 \| 1.1623 \| 0.7129 \| 1.1604 \| \| 2.4595 \| 9.0 \| 568 \| 29.4736 \| 1.0387 \| 0.6209 \| 1.0387 \| \| 2.2372 \| 10.0 \| 631 \| 28.2509 \| 1.0226 \| 0.5924 \| 1.0226 \| \| 2.2372 \| 10.99 \| 694 \| 27.7802 \| 0.9792 \| 0.5773 \| 0.9774 \| \| 1.97 \| 11.99 \| 757 \| 28.9601 \| 0.9783 \| 0.5374 \| 0.9764 \| \| 1.7414 \| 12.99 \| 820 \| 28.2486 \| 0.9623 \| 0.5221 \| 0.9604 \| \| 1.7414 \| 13.99 \| 883 \| 26.1469 \| 0.9415 \| 0.5558 \| 0.9406 \| \| 1.6401 \| 14.99 \| 946 \| 29.1386 \| 0.8906 \| 0.4841 \| 0.8887 \| \| 1.4366 \| 16.0 \| 1010 \| 29.2485 \| 0.8519 \| 0.4619 \| 0.85 \| \| 1.4366 \| 17.0 \| 1073 \| 31.7118 \| 0.8330 \| 0.4330 \| 0.8292 \| \| 1.3404 \| 18.0 \| 1136 \| 30.9065 \| 0.7755 \| 0.4230 \| 0.7717 \| \| 1.3404 \| 18.99 \| 1199 \| 31.0650 \| 0.7802 \| 0.4073 \| 0.7736 \| \| 1.1973 \| 19.99 \| 1262 \| 31.2787 \| 0.8 \| 0.4045 \| 0.7962 \| \| 1.1184 \| 20.99 \| 1325 \| 30.3397 \| 0.7877 \| 0.4192 \| 0.7830 \| \| 1.1184 \| 21.99 \| 1388 \| 30.4381 \| 0.7557 \| 0.3924 \| 0.7519 \| \| 1.0302 \| 22.99 \| 1451 \| 30.7764 \| 0.7575 \| 0.3880 \| 0.7547 \| \| 0.9575 \| 24.0 \| 1515 \| 30.1089 \| 0.7274 \| 0.3821 \| 0.7226 \| \| 0.9575 \| 25.0 \| 1578 \| 29.0145 \| 0.7057 \| 0.3774 \| 0.7019 \| \| 0.8595 \| 26.0 \| 1641 \| 32.1018 \| 0.7226 \| 0.3760 \| 0.7179 \| \| 0.7968 \| 26.99 \| 1704 \| 29.5336 \| 0.7104 \| 0.3643 \| 0.7075 \| \| 0.7968 \| 27.99 \| 1767 \| 32.2412 \| 0.7198 \| 0.3726 \| 0.7179 \| ### Framework versions - Transformers 4.28.0 - Pytorch 2.6.0+cu124 - Datasets 3.5.1 - Tokenizers 0.13.3
TOMFORD79/Camp6	TOMFORD79	2025-04-29T04:39:17Z	0	0	null	[ "safetensors", "any-to-any", "omega", "omegalabs", "bittensor", "agi", "license:mit", "region:us" ]	any-to-any	2025-04-29T04:28:30Z	--- license: mit tags: - any-to-any - omega - omegalabs - bittensor - agi --- This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet. Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).
roadus/Foundation-Sec-8B-Q8_0-GGUF	roadus	2025-04-29T04:38:35Z	0	1	transformers	[ "transformers", "gguf", "security", "llama-cpp", "gguf-my-repo", "text-generation", "en", "base_model:fdtn-ai/Foundation-Sec-8B", "base_model:quantized:fdtn-ai/Foundation-Sec-8B", "license:apache-2.0", "endpoints_compatible", "region:us" ]	text-generation	2025-04-29T04:37:54Z	--- base_model: fdtn-ai/Foundation-Sec-8B language: - en library_name: transformers license: apache-2.0 pipeline_tag: text-generation tags: - security - llama-cpp - gguf-my-repo --- # roadus/Foundation-Sec-8B-Q8_0-GGUF This model was converted to GGUF format from [`fdtn-ai/Foundation-Sec-8B`](https://huggingface.co/fdtn-ai/Foundation-Sec-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/fdtn-ai/Foundation-Sec-8B) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo roadus/Foundation-Sec-8B-Q8_0-GGUF --hf-file foundation-sec-8b-q8_0.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo roadus/Foundation-Sec-8B-Q8_0-GGUF --hf-file foundation-sec-8b-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. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo roadus/Foundation-Sec-8B-Q8_0-GGUF --hf-file foundation-sec-8b-q8_0.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo roadus/Foundation-Sec-8B-Q8_0-GGUF --hf-file foundation-sec-8b-q8_0.gguf -c 2048 ```
fedovtt/e89fedea-11ba-4eb1-a925-3bf32cfcbe76	fedovtt	2025-04-29T04:38:09Z	0	0	peft	[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "base_model:WhiteRabbitNeo/Llama-3-WhiteRabbitNeo-8B-v2.0", "base_model:adapter:WhiteRabbitNeo/Llama-3-WhiteRabbitNeo-8B-v2.0", "license:llama3", "8-bit", "bitsandbytes", "region:us" ]	null	2025-04-29T03:34:16Z	--- library_name: peft license: llama3 base_model: WhiteRabbitNeo/Llama-3-WhiteRabbitNeo-8B-v2.0 tags: - axolotl - generated_from_trainer model-index: - name: e89fedea-11ba-4eb1-a925-3bf32cfcbe76 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/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml adapter: lora base_model: WhiteRabbitNeo/Llama-3-WhiteRabbitNeo-8B-v2.0 bf16: true chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - 0f595e9ff2bcd098_train_data.json ds_type: json format: custom path: /workspace/input_data/0f595e9ff2bcd098_train_data.json type: field_input: intent field_instruction: instruction field_output: response format: '{instruction} {input}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null early_stopping_patience: null eval_max_new_tokens: 128 eval_table_size: null evals_per_epoch: 1 flash_attention: true fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 1 gradient_checkpointing: true gradient_clipping: 0.5 group_by_length: false hub_model_id: fedovtt/e89fedea-11ba-4eb1-a925-3bf32cfcbe76 hub_repo: null hub_strategy: end hub_token: null learning_rate: 5.0e-06 load_in_4bit: true load_in_8bit: true local_rank: null logging_steps: 1 lora_alpha: 64 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 32 lora_target_linear: true lr_scheduler: cosine max_steps: 200 micro_batch_size: 8 mixed_precision: bf16 mlflow_experiment_name: /tmp/0f595e9ff2bcd098_train_data.json model_type: AutoModelForCausalLM num_epochs: 1 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false saves_per_epoch: 1 sequence_len: 1024 strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: aa0ca05d-e3de-4bfb-9606-737b2bd623fd wandb_project: s56-1 wandb_run: your_name wandb_runid: aa0ca05d-e3de-4bfb-9606-737b2bd623fd warmup_steps: 5 weight_decay: 0.01 xformers_attention: true ``` </details><br> # e89fedea-11ba-4eb1-a925-3bf32cfcbe76 This model is a fine-tuned version of [WhiteRabbitNeo/Llama-3-WhiteRabbitNeo-8B-v2.0](https://huggingface.co/WhiteRabbitNeo/Llama-3-WhiteRabbitNeo-8B-v2.0) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4894 ## 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-06 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 5 - training_steps: 200 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 0.4345 \| 0.0068 \| 200 \| 0.4894 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
xiaoyuanliu/Qwen2.5-3B-simplerl-ppo-online.critique-012-ver.len-p3	xiaoyuanliu	2025-04-29T04:31:04Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-29T04:26:32Z	--- 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. 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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mhr2004/nev-original-cross-encoder-stsb-roberta-large-bs8-lr2e-05	mhr2004	2025-04-29T04:25:51Z	0	0	transformers	[ "transformers", "safetensors", "roberta", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-04-29T03:59:18Z	--- 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. 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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vg126/VGarut	vg126	2025-04-29T04:25:48Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "qwen2", "text-generation", "autotrain", "text-generation-inference", "conversational", "base_model:Qwen/Qwen2.5-1.5B-Instruct", "base_model:finetune:Qwen/Qwen2.5-1.5B-Instruct", "license:other", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-04-29T04:12:22Z	--- tags: - autotrain - text-generation-inference - text-generation library_name: transformers base_model: Qwen/Qwen2.5-1.5B-Instruct widget: - messages: - role: user content: What is your favorite condiment? license: other --- # Model Trained Using AutoTrain This model was trained using AutoTrain. For more information, please visit [AutoTrain](https://hf.co/docs/autotrain). # Usage ```python from transformers import AutoModelForCausalLM, AutoTokenizer model_path = "PATH_TO_THIS_REPO" tokenizer = AutoTokenizer.from_pretrained(model_path) model = AutoModelForCausalLM.from_pretrained( model_path, device_map="auto", torch_dtype='auto' ).eval() # Prompt content: "hi" messages = [ {"role": "user", "content": "hi"} ] input_ids = tokenizer.apply_chat_template(conversation=messages, tokenize=True, add_generation_prompt=True, return_tensors='pt') output_ids = model.generate(input_ids.to('cuda')) response = tokenizer.decode(output_ids[0][input_ids.shape[1]:], skip_special_tokens=True) # Model response: "Hello! How can I assist you today?" print(response) ```
cherryDavid/Qwen3-0.6B-Q8_0-GGUF	cherryDavid	2025-04-29T04:17:47Z	0	0	transformers	[ "transformers", "gguf", "llama-cpp", "gguf-my-repo", "text-generation", "base_model:Qwen/Qwen3-0.6B", "base_model:quantized:Qwen/Qwen3-0.6B", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	text-generation	2025-04-29T04:17:23Z	--- base_model: Qwen/Qwen3-0.6B library_name: transformers license: apache-2.0 license_link: https://huggingface.co/Qwen/Qwen3-0.6B/blob/main/LICENSE pipeline_tag: text-generation tags: - llama-cpp - gguf-my-repo --- # cherryDavid/Qwen3-0.6B-Q8_0-GGUF This model was converted to GGUF format from [`Qwen/Qwen3-0.6B`](https://huggingface.co/Qwen/Qwen3-0.6B) 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/Qwen3-0.6B) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo cherryDavid/Qwen3-0.6B-Q8_0-GGUF --hf-file qwen3-0.6b-q8_0.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo cherryDavid/Qwen3-0.6B-Q8_0-GGUF --hf-file qwen3-0.6b-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. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo cherryDavid/Qwen3-0.6B-Q8_0-GGUF --hf-file qwen3-0.6b-q8_0.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo cherryDavid/Qwen3-0.6B-Q8_0-GGUF --hf-file qwen3-0.6b-q8_0.gguf -c 2048 ```
k1h0/llama3.1-8B-Instruct-query_ns	k1h0	2025-04-29T04:16:13Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "llama-factory", "freeze", "generated_from_trainer", "conversational", "base_model:meta-llama/Llama-3.1-8B-Instruct", "base_model:finetune:meta-llama/Llama-3.1-8B-Instruct", "license:other", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-29T04:12:28Z	--- library_name: transformers license: other base_model: meta-llama/Llama-3.1-8B-Instruct tags: - llama-factory - freeze - generated_from_trainer model-index: - name: llama_ns 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_ns This model is a fine-tuned version of [meta-llama/Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct) on the codes_330k_nsx 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: 5e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 4 - gradient_accumulation_steps: 8 - total_train_batch_size: 512 - total_eval_batch_size: 32 - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - num_epochs: 1.0 ### Training results ### Framework versions - Transformers 4.48.2 - Pytorch 2.5.1+cu124 - Datasets 3.2.0 - Tokenizers 0.21.0
AlanLanSS/mnem_qwen	AlanLanSS	2025-04-29T04:15:53Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "qwen2", "trl", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-04-28T23:20:10Z	--- base_model: unsloth/qwen2.5-7b-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - qwen2 - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: AlanLanSS - License: apache-2.0 - Finetuned from model : unsloth/qwen2.5-7b-unsloth-bnb-4bit This qwen2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
hafidhsoekma/unsloth-Qwen2.5-7B-Instruct-unsloth-bnb-16bit-gasing-0	hafidhsoekma	2025-04-29T04:14:59Z	0	0	transformers	[ "transformers", "pytorch", "safetensors", "qwen2", "text-generation", "text-generation-inference", "unsloth", "trl", "conversational", "en", "base_model:unsloth/Qwen2.5-7B-Instruct-unsloth-bnb-4bit", "base_model:finetune:unsloth/Qwen2.5-7B-Instruct-unsloth-bnb-4bit", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-04-29T01:55:41Z	--- base_model: unsloth/Qwen2.5-7B-Instruct-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - qwen2 - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: hafidhsoekma - License: apache-2.0 - Finetuned from model : unsloth/Qwen2.5-7B-Instruct-unsloth-bnb-4bit This qwen2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
bnkc123/bge-base-financial-matryoshka	bnkc123	2025-04-29T04:13:33Z	0	0	sentence-transformers	[ "sentence-transformers", "tensorboard", "safetensors", "bert", "sentence-similarity", "feature-extraction", "generated_from_trainer", "dataset_size:6300", "loss:MatryoshkaLoss", "loss:MultipleNegativesRankingLoss", "en", "dataset:philschmid/finanical-rag-embedding-dataset", "arxiv:1908.10084", "arxiv:2205.13147", "arxiv:1705.00652", "base_model:BAAI/bge-base-en-v1.5", "base_model:finetune:BAAI/bge-base-en-v1.5", "license:apache-2.0", "model-index", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	sentence-similarity	2025-04-29T03:04:16Z	--- language: - en license: apache-2.0 tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:6300 - loss:MatryoshkaLoss - loss:MultipleNegativesRankingLoss base_model: BAAI/bge-base-en-v1.5 widget: - source_sentence: What are the components of Comcast's domestic distribution revenue? sentences: - Cash used in investing activities was $2.3 billion for fiscal 2023, compared to $2.1 billion for fiscal 2022. - Domestic distribution revenue primarily includes revenue generated from the distribution of our television networks operating predominantly in the United States to traditional and virtual multichannel video providers, and from NBC-affiliated and Telemundo-affiliated local broadcast television stations. Our revenue from distribution agreements is generally based on the number of subscribers receiving the programming on our television networks and a per subscriber fee. Distribution revenue also includes Peacock subscription fees. - In January 2023, Alphabet Inc. announced a reduction of its workforce, consequently recording employee severance and related charges of $2.1 billion for the year. - source_sentence: What was the noncash pre-tax impairment charge recorded due to the disposal of Vrio's operations in 2021, and what are the main components contributing to this amount? sentences: - The cash equities rate per contract (per 100 shares) for NYSE increased by 6%, from $0.045 in 2022 to $0.048 in 2023. - In the second quarter of 2021, we classified the Vrio disposal group as held-for-sale and reported the disposal group at fair value less cost to sell, which resulted in a noncash, pre-tax impairment charge of $4,555, including approximately $2,100 related to accumulated foreign currency translation adjustments and $2,500 related to property, plant and equipment and intangible assets. - 'SECRET LAIR - our internet-based storefront where MAGIC: THE GATHERING fans can purchase exclusive and limited versions of cards.' - source_sentence: What does the Corporate and Other segment include in its composition? sentences: - The segment consists of unallocated corporate expenses and administrative costs and activities not considered when evaluating segment performance as well as certain assets benefiting more than one segment. In addition, intersegment transactions are eliminated within the Corporate and Other segment. - Net cash provided by (used in) operating activities was recorded at $20,930 million for the reported year. - Forward-Looking Statements Certain statements in this report, other than purely historical information, including estimates, projections, statements relating to our business plans, objectives and expected operating results, and the assumptions upon which those statements are based, are “forward-looking statements” within the meaning of the Private Securities Litigation Reform Act of 1995, Section 27A of the Securities Act of 1933 and Section 21E of the Securities Exchange Act of 1934. - source_sentence: What was the purchase price for the repurchase of Mobility preferred interests by AT&T in 2023? sentences: - Net revenue increased $1.5 billion, or 19%, to $9.6 billion in 2023 from $8.1 billion in 2022. On a constant dollar basis, net revenue increased 20%. Comparable sales increased 13%, or 14% on a constant dollar basis. The increase in net revenue was primarily due to increased Americas net revenue. China Mainland and Rest of World net revenue also increased. - Google Services includes products and services such as ads, Android, Chrome, devices, Google Maps, Google Play, Search, and YouTube. Google Services generates revenues primarily from advertising; fees received for consumer subscription-based products such. as YouTube TV, YouTube Music and Premium, and NFL Sunday Ticket; and the sale of apps and in-app purchases and devices. - In April 2023, we also accepted the December 2022 put option notice from the AT&T pension trust and repurchased the remaining 213 million Mobility preferred interests for a purchase price, including accrued and unpaid distributions, of $5,414. - source_sentence: What is the maximum leverage ratio allowed before default under the company's credit facility? sentences: - If the company's leverage ratio exceeds 3.50 to 1, it would be in default of its revolving credit facility, impairing its ability to borrow under the facility. - Research and Development Because the industries in which the Company competes are characterized by rapid technological advances, the Company’s ability to compete successfully depends heavily upon its ability to ensure a continual and timely flow of competitive products, services and technologies to the marketplace. - Visa is focused on extending, enhancing and investing in VisaNet, their proprietary advanced transaction processing network, to offer a single connection point for facilitating payment transactions to multiple endpoints through various form factors. datasets: - philschmid/finanical-rag-embedding-dataset pipeline_tag: sentence-similarity library_name: sentence-transformers metrics: - cosine_accuracy@1 - cosine_accuracy@3 - cosine_accuracy@5 - cosine_accuracy@10 - cosine_precision@1 - cosine_precision@3 - cosine_precision@5 - cosine_precision@10 - cosine_recall@1 - cosine_recall@3 - cosine_recall@5 - cosine_recall@10 - cosine_ndcg@10 - cosine_mrr@10 - cosine_map@100 model-index: - name: BGE base Financial Matryoshka results: - task: type: information-retrieval name: Information Retrieval dataset: name: dim 768 type: dim_768 metrics: - type: cosine_accuracy@1 value: 0.6771428571428572 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.8371428571428572 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.8685714285714285 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.9185714285714286 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.6771428571428572 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.27904761904761904 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.17371428571428568 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.09185714285714283 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.6771428571428572 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.8371428571428572 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.8685714285714285 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.9185714285714286 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.800782444183487 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.762721088435374 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.7655884035994069 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 512 type: dim_512 metrics: - type: cosine_accuracy@1 value: 0.6828571428571428 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.8371428571428572 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.8757142857142857 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.92 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.6828571428571428 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.27904761904761904 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.17514285714285713 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.09199999999999998 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.6828571428571428 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.8371428571428572 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.8757142857142857 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.92 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.80444342170685 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.7670583900226756 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.7699510134898729 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 256 type: dim_256 metrics: - type: cosine_accuracy@1 value: 0.6757142857142857 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.8228571428571428 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.8642857142857143 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.9185714285714286 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.6757142857142857 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.2742857142857143 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.17285714285714285 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.09185714285714283 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.6757142857142857 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.8228571428571428 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.8642857142857143 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.9185714285714286 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.7984105242762846 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.7599024943310656 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.7625291382895937 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 128 type: dim_128 metrics: - type: cosine_accuracy@1 value: 0.6714285714285714 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.8114285714285714 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.8485714285714285 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.9014285714285715 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.6714285714285714 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.2704761904761904 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.16971428571428568 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.09014285714285714 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.6714285714285714 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.8114285714285714 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.8485714285714285 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.9014285714285715 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.7872870842648211 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.7507193877551018 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.7542921487122674 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: dim 64 type: dim_64 metrics: - type: cosine_accuracy@1 value: 0.6242857142857143 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.7842857142857143 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.82 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.8828571428571429 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.6242857142857143 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.26142857142857145 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.16399999999999998 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.08828571428571429 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.6242857142857143 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.7842857142857143 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.82 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.8828571428571429 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.7546358861091382 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.7135277777777775 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.7174129354945035 name: Cosine Map@100 --- # BGE base Financial Matryoshka This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) on the [finanical-rag-embedding-dataset](https://huggingface.co/datasets/philschmid/finanical-rag-embedding-dataset) dataset. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. ## Model Details ### Model Description - Model Type: Sentence Transformer - Base model: [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) <!-- at revision a5beb1e3e68b9ab74eb54cfd186867f64f240e1a --> - Maximum Sequence Length: 512 tokens - Output Dimensionality: 768 dimensions - Similarity Function: Cosine Similarity - Training Dataset: - [finanical-rag-embedding-dataset](https://huggingface.co/datasets/philschmid/finanical-rag-embedding-dataset) - Language: en - License: apache-2.0 ### Model Sources - Documentation: [Sentence Transformers Documentation](https://sbert.net) - Repository: [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) - Hugging Face: [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) ### Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': True}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, '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): Normalize() ) ``` ## Usage ### Direct Usage (Sentence Transformers) First install the Sentence Transformers library: ```bash pip install -U sentence-transformers ``` Then you can load this model and run inference. ```python from sentence_transformers import SentenceTransformer # Download from the 🤗 Hub model = SentenceTransformer("bnkc123/bge-base-financial-matryoshka") # Run inference sentences = [ "What is the maximum leverage ratio allowed before default under the company's credit facility?", "If the company's leverage ratio exceeds 3.50 to 1, it would be in default of its revolving credit facility, impairing its ability to borrow under the facility.", 'Research and Development Because the industries in which the Company competes are characterized by rapid technological advances, the Company’s ability to compete successfully depends heavily upon its ability to ensure a continual and timely flow of competitive products, services and technologies to the marketplace.', ] embeddings = model.encode(sentences) print(embeddings.shape) # [3, 768] # Get the similarity scores for the embeddings similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] ``` <!-- ### Direct Usage (Transformers) <details><summary>Click to see the direct usage in Transformers</summary> </details> --> <!-- ### Downstream Usage (Sentence Transformers) You can finetune this model on your own dataset. <details><summary>Click to expand</summary> </details> --> <!-- ### Out-of-Scope Use List how the model may foreseeably be misused and address what users ought not to do with the model. --> ## Evaluation ### Metrics #### Information Retrieval * Dataset: `dim_768` * Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) with these parameters: ```json { "truncate_dim": 768 } ``` \| Metric \| Value \| \|:--------------------\|:-----------\| \| cosine_accuracy@1 \| 0.6771 \| \| cosine_accuracy@3 \| 0.8371 \| \| cosine_accuracy@5 \| 0.8686 \| \| cosine_accuracy@10 \| 0.9186 \| \| cosine_precision@1 \| 0.6771 \| \| cosine_precision@3 \| 0.279 \| \| cosine_precision@5 \| 0.1737 \| \| cosine_precision@10 \| 0.0919 \| \| cosine_recall@1 \| 0.6771 \| \| cosine_recall@3 \| 0.8371 \| \| cosine_recall@5 \| 0.8686 \| \| cosine_recall@10 \| 0.9186 \| \| cosine_ndcg@10 \| 0.8008 \| \| cosine_mrr@10 \| 0.7627 \| \| cosine_map@100 \| 0.7656 \| #### Information Retrieval * Dataset: `dim_512` * Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) with these parameters: ```json { "truncate_dim": 512 } ``` \| Metric \| Value \| \|:--------------------\|:-----------\| \| cosine_accuracy@1 \| 0.6829 \| \| cosine_accuracy@3 \| 0.8371 \| \| cosine_accuracy@5 \| 0.8757 \| \| cosine_accuracy@10 \| 0.92 \| \| cosine_precision@1 \| 0.6829 \| \| cosine_precision@3 \| 0.279 \| \| cosine_precision@5 \| 0.1751 \| \| cosine_precision@10 \| 0.092 \| \| cosine_recall@1 \| 0.6829 \| \| cosine_recall@3 \| 0.8371 \| \| cosine_recall@5 \| 0.8757 \| \| cosine_recall@10 \| 0.92 \| \| cosine_ndcg@10 \| 0.8044 \| \| cosine_mrr@10 \| 0.7671 \| \| cosine_map@100 \| 0.77 \| #### Information Retrieval * Dataset: `dim_256` * Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) with these parameters: ```json { "truncate_dim": 256 } ``` \| Metric \| Value \| \|:--------------------\|:-----------\| \| cosine_accuracy@1 \| 0.6757 \| \| cosine_accuracy@3 \| 0.8229 \| \| cosine_accuracy@5 \| 0.8643 \| \| cosine_accuracy@10 \| 0.9186 \| \| cosine_precision@1 \| 0.6757 \| \| cosine_precision@3 \| 0.2743 \| \| cosine_precision@5 \| 0.1729 \| \| cosine_precision@10 \| 0.0919 \| \| cosine_recall@1 \| 0.6757 \| \| cosine_recall@3 \| 0.8229 \| \| cosine_recall@5 \| 0.8643 \| \| cosine_recall@10 \| 0.9186 \| \| cosine_ndcg@10 \| 0.7984 \| \| cosine_mrr@10 \| 0.7599 \| \| cosine_map@100 \| 0.7625 \| #### Information Retrieval * Dataset: `dim_128` * Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) with these parameters: ```json { "truncate_dim": 128 } ``` \| Metric \| Value \| \|:--------------------\|:-----------\| \| cosine_accuracy@1 \| 0.6714 \| \| cosine_accuracy@3 \| 0.8114 \| \| cosine_accuracy@5 \| 0.8486 \| \| cosine_accuracy@10 \| 0.9014 \| \| cosine_precision@1 \| 0.6714 \| \| cosine_precision@3 \| 0.2705 \| \| cosine_precision@5 \| 0.1697 \| \| cosine_precision@10 \| 0.0901 \| \| cosine_recall@1 \| 0.6714 \| \| cosine_recall@3 \| 0.8114 \| \| cosine_recall@5 \| 0.8486 \| \| cosine_recall@10 \| 0.9014 \| \| cosine_ndcg@10 \| 0.7873 \| \| cosine_mrr@10 \| 0.7507 \| \| cosine_map@100 \| 0.7543 \| #### Information Retrieval * Dataset: `dim_64` * Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) with these parameters: ```json { "truncate_dim": 64 } ``` \| Metric \| Value \| \|:--------------------\|:-----------\| \| cosine_accuracy@1 \| 0.6243 \| \| cosine_accuracy@3 \| 0.7843 \| \| cosine_accuracy@5 \| 0.82 \| \| cosine_accuracy@10 \| 0.8829 \| \| cosine_precision@1 \| 0.6243 \| \| cosine_precision@3 \| 0.2614 \| \| cosine_precision@5 \| 0.164 \| \| cosine_precision@10 \| 0.0883 \| \| cosine_recall@1 \| 0.6243 \| \| cosine_recall@3 \| 0.7843 \| \| cosine_recall@5 \| 0.82 \| \| cosine_recall@10 \| 0.8829 \| \| cosine_ndcg@10 \| 0.7546 \| \| cosine_mrr@10 \| 0.7135 \| \| cosine_map@100 \| 0.7174 \| <!-- ## Bias, Risks and Limitations What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model. --> <!-- ### Recommendations What are recommendations with respect to the foreseeable issues? For example, filtering explicit content. --> ## Training Details ### Training Dataset #### finanical-rag-embedding-dataset * Dataset: [finanical-rag-embedding-dataset](https://huggingface.co/datasets/philschmid/finanical-rag-embedding-dataset) at [e0b1781](https://huggingface.co/datasets/philschmid/finanical-rag-embedding-dataset/tree/e0b17819cf52d444066c99f4a176f5717e066300) * Size: 6,300 training samples * Columns: <code>anchor</code> and <code>positive</code> * Approximate statistics based on the first 1000 samples: \| \| anchor \| positive \| \|:--------\|:---------------------------------------------------------------------------------\|:-----------------------------------------------------------------------------------\| \| type \| string \| string \| \| details \| <ul><li>min: 7 tokens</li><li>mean: 20.5 tokens</li><li>max: 43 tokens</li></ul> \| <ul><li>min: 9 tokens</li><li>mean: 46.09 tokens</li><li>max: 512 tokens</li></ul> \| * Samples: \| anchor \| positive \| \|:----------------------------------------------------------------------------------------------------------------------------------------------------------\|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\| \| <code>What was the amount of premiums written by Berkshire Hathaway's Insurance Underwriting in 2023, and how did it compare to the previous year?</code> \| <code>Premiums written increased $3.5 billion (24.1%) in 2023 compared to 2022. The increase was primarily due to RSUI and CapSpecialty ($2.1 billion), as well as comparative increases from BHSI and BH Direct, and to a lesser extent the other businesses. Premiums written \| $ \| 18,142 \| \| \| \| $ \| 14,619 \|</code> \| \| <code>What types of transportation equipment does XTRA Corporation manage in its fleet?</code> \| <code>XTRA manages a diverse fleet of approximately 90,000 units located at 47 facilities throughout the U.S. The fleet includes over-the-road and storage trailers, chassis, temperature-controlled vans and flatbed trailers.</code> \| \| <code>What seasonal trends affect the company's sales volumes?</code> \| <code>Sales volumes for the company are highest in the second fiscal quarter due to seasonal influences, particularly during the spring season in the regions it serves.</code> \| * Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: ```json { "loss": "MultipleNegativesRankingLoss", "matryoshka_dims": [ 768, 512, 256, 128, 64 ], "matryoshka_weights": [ 1, 1, 1, 1, 1 ], "n_dims_per_step": -1 } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `eval_strategy`: epoch - `per_device_train_batch_size`: 32 - `per_device_eval_batch_size`: 16 - `gradient_accumulation_steps`: 16 - `learning_rate`: 2e-05 - `num_train_epochs`: 4 - `lr_scheduler_type`: cosine - `warmup_ratio`: 0.1 - `bf16`: True - `tf32`: True - `load_best_model_at_end`: True - `optim`: adamw_torch_fused - `push_to_hub`: True - `hub_model_id`: bnkc123/bge-base-financial-matryoshka - `batch_sampler`: no_duplicates #### All Hyperparameters <details><summary>Click to expand</summary> - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: epoch - `prediction_loss_only`: True - `per_device_train_batch_size`: 32 - `per_device_eval_batch_size`: 16 - `per_gpu_train_batch_size`: None - `per_gpu_eval_batch_size`: None - `gradient_accumulation_steps`: 16 - `eval_accumulation_steps`: None - `torch_empty_cache_steps`: None - `learning_rate`: 2e-05 - `weight_decay`: 0.0 - `adam_beta1`: 0.9 - `adam_beta2`: 0.999 - `adam_epsilon`: 1e-08 - `max_grad_norm`: 1.0 - `num_train_epochs`: 4 - `max_steps`: -1 - `lr_scheduler_type`: cosine - `lr_scheduler_kwargs`: {} - `warmup_ratio`: 0.1 - `warmup_steps`: 0 - `log_level`: passive - `log_level_replica`: warning - `log_on_each_node`: True - `logging_nan_inf_filter`: True - `save_safetensors`: True - `save_on_each_node`: False - `save_only_model`: False - `restore_callback_states_from_checkpoint`: False - `no_cuda`: False - `use_cpu`: False - `use_mps_device`: False - `seed`: 42 - `data_seed`: None - `jit_mode_eval`: False - `use_ipex`: False - `bf16`: True - `fp16`: False - `fp16_opt_level`: O1 - `half_precision_backend`: auto - `bf16_full_eval`: False - `fp16_full_eval`: False - `tf32`: True - `local_rank`: 0 - `ddp_backend`: None - `tpu_num_cores`: None - `tpu_metrics_debug`: False - `debug`: [] - `dataloader_drop_last`: False - `dataloader_num_workers`: 0 - `dataloader_prefetch_factor`: None - `past_index`: -1 - `disable_tqdm`: False - `remove_unused_columns`: True - `label_names`: None - `load_best_model_at_end`: True - `ignore_data_skip`: False - `fsdp`: [] - `fsdp_min_num_params`: 0 - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} - `tp_size`: 0 - `fsdp_transformer_layer_cls_to_wrap`: None - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} - `deepspeed`: None - `label_smoothing_factor`: 0.0 - `optim`: adamw_torch_fused - `optim_args`: None - `adafactor`: False - `group_by_length`: False - `length_column_name`: length - `ddp_find_unused_parameters`: None - `ddp_bucket_cap_mb`: None - `ddp_broadcast_buffers`: False - `dataloader_pin_memory`: True - `dataloader_persistent_workers`: False - `skip_memory_metrics`: True - `use_legacy_prediction_loop`: False - `push_to_hub`: True - `resume_from_checkpoint`: None - `hub_model_id`: bnkc123/bge-base-financial-matryoshka - `hub_strategy`: every_save - `hub_private_repo`: None - `hub_always_push`: False - `gradient_checkpointing`: False - `gradient_checkpointing_kwargs`: None - `include_inputs_for_metrics`: False - `include_for_metrics`: [] - `eval_do_concat_batches`: True - `fp16_backend`: auto - `push_to_hub_model_id`: None - `push_to_hub_organization`: None - `mp_parameters`: - `auto_find_batch_size`: False - `full_determinism`: False - `torchdynamo`: None - `ray_scope`: last - `ddp_timeout`: 1800 - `torch_compile`: False - `torch_compile_backend`: None - `torch_compile_mode`: None - `include_tokens_per_second`: False - `include_num_input_tokens_seen`: False - `neftune_noise_alpha`: None - `optim_target_modules`: None - `batch_eval_metrics`: False - `eval_on_start`: False - `use_liger_kernel`: False - `eval_use_gather_object`: False - `average_tokens_across_devices`: False - `prompts`: None - `batch_sampler`: no_duplicates - `multi_dataset_batch_sampler`: proportional </details> ### Training Logs \| Epoch \| Step \| Training Loss \| dim_768_cosine_ndcg@10 \| dim_512_cosine_ndcg@10 \| dim_256_cosine_ndcg@10 \| dim_128_cosine_ndcg@10 \| dim_64_cosine_ndcg@10 \| \|:---------:\|:------:\|:-------------:\|:----------------------:\|:----------------------:\|:----------------------:\|:----------------------:\|:---------------------:\| \| 0.8122 \| 10 \| 25.483 \| - \| - \| - \| - \| - \| \| 1.0 \| 13 \| - \| 0.7890 \| 0.7887 \| 0.7815 \| 0.7647 \| 0.7280 \| \| 1.5685 \| 20 \| 9.1323 \| - \| - \| - \| - \| - \| \| 2.0 \| 26 \| - \| 0.7952 \| 0.7982 \| 0.7933 \| 0.7801 \| 0.7477 \| \| 2.3249 \| 30 \| 6.7535 \| - \| - \| - \| - \| - \| \| 3.0 \| 39 \| - \| 0.8019 \| 0.8048 \| 0.7989 \| 0.7865 \| 0.7547 \| \| 3.0812 \| 40 \| 6.5646 \| - \| - \| - \| - \| - \| \| 3.731 \| 48 \| - \| 0.8008 \| 0.8044 \| 0.7984 \| 0.7873 \| 0.7546 \| * The bold row denotes the saved checkpoint. ### Framework Versions - Python: 3.12.6 - Sentence Transformers: 4.1.0 - Transformers: 4.51.3 - PyTorch: 2.7.0+cu126 - Accelerate: 1.6.0 - Datasets: 3.5.1 - Tokenizers: 0.21.1 ## Citation ### BibTeX #### Sentence Transformers ```bibtex @inproceedings{reimers-2019-sentence-bert, title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", author = "Reimers, Nils and Gurevych, Iryna", booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", month = "11", year = "2019", publisher = "Association for Computational Linguistics", url = "https://arxiv.org/abs/1908.10084", } ``` #### MatryoshkaLoss ```bibtex @misc{kusupati2024matryoshka, title={Matryoshka Representation Learning}, author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi}, year={2024}, eprint={2205.13147}, archivePrefix={arXiv}, primaryClass={cs.LG} } ``` #### MultipleNegativesRankingLoss ```bibtex @misc{henderson2017efficient, title={Efficient Natural Language Response Suggestion for Smart Reply}, author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil}, year={2017}, eprint={1705.00652}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` <!-- ## Glossary Clearly define terms in order to be accessible across audiences. --> <!-- ## Model Card Authors Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction. --> <!-- ## Model Card Contact Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors. -->
ParitoshVaghasiya/learn_hf_food_not_food_text_classifier-distilbert-base-uncased	ParitoshVaghasiya	2025-04-29T04:10:39Z	0	0	transformers	[ "transformers", "safetensors", "distilbert", "text-classification", "generated_from_trainer", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-04-29T03:00:19Z	--- library_name: transformers license: apache-2.0 base_model: distilbert/distilbert-base-uncased tags: - generated_from_trainer metrics: - accuracy model-index: - name: learn_hf_food_not_food_text_classifier-distilbert-base-uncased 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. --> # learn_hf_food_not_food_text_classifier-distilbert-base-uncased This model is a fine-tuned version of [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0006 - Accuracy: 1.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.3362 \| 1.0 \| 7 \| 0.0447 \| 1.0 \| \| 0.0211 \| 2.0 \| 14 \| 0.0059 \| 1.0 \| \| 0.004 \| 3.0 \| 21 \| 0.0023 \| 1.0 \| \| 0.002 \| 4.0 \| 28 \| 0.0013 \| 1.0 \| \| 0.0012 \| 5.0 \| 35 \| 0.0009 \| 1.0 \| \| 0.001 \| 6.0 \| 42 \| 0.0008 \| 1.0 \| \| 0.0008 \| 7.0 \| 49 \| 0.0007 \| 1.0 \| \| 0.0007 \| 8.0 \| 56 \| 0.0006 \| 1.0 \| \| 0.0007 \| 9.0 \| 63 \| 0.0006 \| 1.0 \| \| 0.0007 \| 10.0 \| 70 \| 0.0006 \| 1.0 \| ### Framework versions - Transformers 4.51.3 - Pytorch 2.6.0+cu124 - Datasets 3.5.1 - Tokenizers 0.21.1
AdversarialRLHF/pythia410m-sft-tldr-propprefix	AdversarialRLHF	2025-04-29T04:10:18Z	0	0	transformers	[ "transformers", "safetensors", "gpt_neox", "text-generation", "generated_from_trainer", "trl", "sft", "base_model:EleutherAI/pythia-410m-deduped", "base_model:finetune:EleutherAI/pythia-410m-deduped", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-29T03:42:09Z	--- base_model: EleutherAI/pythia-410m-deduped library_name: transformers model_name: pythia410m-sft-tldr-propprefix tags: - generated_from_trainer - trl - sft licence: license --- # Model Card for pythia410m-sft-tldr-propprefix This model is a fine-tuned version of [EleutherAI/pythia-410m-deduped](https://huggingface.co/EleutherAI/pythia-410m-deduped). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="AdversarialRLHF/pythia410m-sft-tldr-propprefix", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/muqeeth/adversarial_goodhart_rlhf/runs/Adversarial_goodhart_rlhf_sft_pythia410m_tldr_propprefix) This model was trained with SFT. ### Framework versions - TRL: 0.16.0 - Transformers: 4.50.3 - Pytorch: 2.6.0 - Datasets: 3.5.0 - Tokenizers: 0.21.1 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
sde0119/ipc-unsloth-lora-llama3.2-8b-ins-pretrained-new	sde0119	2025-04-29T04:09:53Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:unsloth/Llama-3.1-8B-Instruct", "base_model:finetune:unsloth/Llama-3.1-8B-Instruct", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-04-29T04:03:54Z	--- base_model: unsloth/Llama-3.1-8B-Instruct tags: - text-generation-inference - transformers - unsloth - llama - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: sde0119 - License: apache-2.0 - Finetuned from model : unsloth/Llama-3.1-8B-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) - IPC RAW Document + IPC Maingroup까지의 GPT 합성 설명데이터로 pretraining한 모델 - 연구실 공용 코랩에서 훈련. https://colab.research.google.com/drive/1ODx_oD709bBCvlpmR_-FQJqlgFk_mVMc?usp=drive_link
Kazuzeraapelao/Animelamdia	Kazuzeraapelao	2025-04-29T04:08:52Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-04-29T04:08:52Z	--- license: apache-2.0 ---
WhoCares258/my_awesome_model	WhoCares258	2025-04-29T04:04:01Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "distilbert", "text-classification", "generated_from_trainer", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-04-29T02:30:54Z	--- library_name: transformers license: apache-2.0 base_model: distilbert-base-uncased 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 is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.2290 - Accuracy: 0.9322 ## 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: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.2215 \| 1.0 \| 1563 \| 0.2051 \| 0.9202 \| \| 0.1468 \| 2.0 \| 3126 \| 0.2290 \| 0.9322 \| ### Framework versions - Transformers 4.48.3 - Pytorch 2.5.1+cu124 - Datasets 3.5.1 - Tokenizers 0.21.0
Jayssii/kyooo	Jayssii	2025-04-29T04:02:43Z	0	0	null	[ "license:bsd-3-clause", "region:us" ]	null	2025-04-29T04:02:43Z	--- license: bsd-3-clause ---
opria123/speecht5_tts_english_finetuned	opria123	2025-04-29T04:00:07Z	0	0	transformers	[ "transformers", "safetensors", "speecht5", "text-to-audio", "audio", "text-to-speech", "speech", "english", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	text-to-speech	2025-04-29T03:58:04Z	--- library_name: transformers tags: - audio - text-to-speech - speech - speecht5 - english --- # 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]
ridalefdali/qwen_1_5b_finetuned	ridalefdali	2025-04-29T03:59:30Z	0	0	transformers	[ "transformers", "gguf", "qwen2", "text-generation-inference", "unsloth", "en", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2025-04-29T03:58:48Z	--- base_model: unsloth/qwen2.5-1.5b-instruct-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - qwen2 - gguf license: apache-2.0 language: - en --- # Uploaded model - Developed by: ridalefdali - License: apache-2.0 - Finetuned from model : unsloth/qwen2.5-1.5b-instruct-unsloth-bnb-4bit This qwen2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
infogeo/4ecdd22f-aa7d-4e89-bd8d-6ab95c8e7392	infogeo	2025-04-29T03:55:46Z	0	0	peft	[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "base_model:WhiteRabbitNeo/Llama-3-WhiteRabbitNeo-8B-v2.0", "base_model:adapter:WhiteRabbitNeo/Llama-3-WhiteRabbitNeo-8B-v2.0", "license:llama3", "4-bit", "bitsandbytes", "region:us" ]	null	2025-04-29T03:35:36Z	--- library_name: peft license: llama3 base_model: WhiteRabbitNeo/Llama-3-WhiteRabbitNeo-8B-v2.0 tags: - axolotl - generated_from_trainer model-index: - name: 4ecdd22f-aa7d-4e89-bd8d-6ab95c8e7392 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/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml absolute_data_files: false adapter: lora base_model: WhiteRabbitNeo/Llama-3-WhiteRabbitNeo-8B-v2.0 bf16: true chat_template: llama3 dataset_prepared_path: /workspace/axolotl datasets: - data_files: - 0f595e9ff2bcd098_train_data.json ds_type: json format: custom path: /workspace/input_data/0f595e9ff2bcd098_train_data.json type: field_input: intent field_instruction: instruction field_output: response format: '{instruction} {input}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null early_stopping_patience: null eval_max_new_tokens: 128 eval_table_size: null evals_per_epoch: 1 flash_attention: true fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 1 gradient_checkpointing: true gradient_clipping: 0.55 group_by_length: false hub_model_id: infogeo/4ecdd22f-aa7d-4e89-bd8d-6ab95c8e7392 hub_repo: null hub_strategy: end hub_token: null learning_rate: 1.0e-06 load_in_4bit: true load_in_8bit: false local_rank: null logging_steps: 1 lora_alpha: 64 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 32 lora_target_linear: true lr_scheduler: cosine max_steps: 150 micro_batch_size: 8 mixed_precision: bf16 mlflow_experiment_name: /tmp/0f595e9ff2bcd098_train_data.json model_type: AutoModelForCausalLM num_epochs: 1 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false saves_per_epoch: 1 sequence_len: 1024 strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: aa0ca05d-e3de-4bfb-9606-737b2bd623fd wandb_project: s56-28 wandb_run: your_name wandb_runid: aa0ca05d-e3de-4bfb-9606-737b2bd623fd warmup_steps: 5 weight_decay: 0.01 xformers_attention: true ``` </details><br> # 4ecdd22f-aa7d-4e89-bd8d-6ab95c8e7392 This model is a fine-tuned version of [WhiteRabbitNeo/Llama-3-WhiteRabbitNeo-8B-v2.0](https://huggingface.co/WhiteRabbitNeo/Llama-3-WhiteRabbitNeo-8B-v2.0) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.6835 ## 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: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 5 - training_steps: 150 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 0.6342 \| 0.0051 \| 150 \| 0.6835 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
Philllipio/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-striped_territorial_warthog	Philllipio	2025-04-29T03:54:44Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "rl-swarm", "grpo", "gensyn", "I am striped territorial warthog", "trl", "conversational", "arxiv:2402.03300", "base_model:Gensyn/Qwen2.5-0.5B-Instruct", "base_model:finetune:Gensyn/Qwen2.5-0.5B-Instruct", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-28T01:33:42Z	--- base_model: Gensyn/Qwen2.5-0.5B-Instruct library_name: transformers model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-striped_territorial_warthog tags: - generated_from_trainer - rl-swarm - grpo - gensyn - I am striped territorial warthog - trl licence: license --- # Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-striped_territorial_warthog This model is a fine-tuned version of [Gensyn/Qwen2.5-0.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-0.5B-Instruct). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="Philllipio/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-striped_territorial_warthog", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300). ### Framework versions - TRL: 0.15.2 - Transformers: 4.51.3 - Pytorch: 2.7.0 - Datasets: 3.5.0 - Tokenizers: 0.21.1 ## Citations Cite GRPO as: ```bibtex @article{zhihong2024deepseekmath, title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}}, author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo}, year = 2024, eprint = {arXiv:2402.03300}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
nathanialhunt2000/926d5161-3874-4bb0-8679-a7a7e57212c9	nathanialhunt2000	2025-04-29T03:52:34Z	0	0	peft	[ "peft", "generated_from_trainer", "base_model:unsloth/SmolLM-1.7B-Instruct", "base_model:adapter:unsloth/SmolLM-1.7B-Instruct", "region:us" ]	null	2025-04-29T03:52:12Z	--- library_name: peft tags: - generated_from_trainer base_model: unsloth/SmolLM-1.7B-Instruct model-index: - name: nathanialhunt2000/926d5161-3874-4bb0-8679-a7a7e57212c9 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. --> # nathanialhunt2000/926d5161-3874-4bb0-8679-a7a7e57212c9 This model was trained from scratch on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0001 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ### Framework versions - PEFT 0.13.2 - Transformers 4.46.3 - Pytorch 2.5.1+cu124 - Datasets 3.1.0 - Tokenizers 0.20.3
RichardErkhov/Primeness_-_by7371542c3-gguf	RichardErkhov	2025-04-29T03:52:28Z	0	0	null	[ "gguf", "arxiv:1910.09700", "endpoints_compatible", "region:us", "conversational" ]	null	2025-04-29T02:26:34Z	Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) by7371542c3 - GGUF - Model creator: https://huggingface.co/Primeness/ - Original model: https://huggingface.co/Primeness/by7371542c3/ \| Name \| Quant method \| Size \| \| ---- \| ---- \| ---- \| \| [by7371542c3.Q2_K.gguf](https://huggingface.co/RichardErkhov/Primeness_-_by7371542c3-gguf/blob/main/by7371542c3.Q2_K.gguf) \| Q2_K \| 2.88GB \| \| [by7371542c3.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/Primeness_-_by7371542c3-gguf/blob/main/by7371542c3.IQ3_XS.gguf) \| IQ3_XS \| 3.18GB \| \| [by7371542c3.IQ3_S.gguf](https://huggingface.co/RichardErkhov/Primeness_-_by7371542c3-gguf/blob/main/by7371542c3.IQ3_S.gguf) \| IQ3_S \| 3.32GB \| \| [by7371542c3.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/Primeness_-_by7371542c3-gguf/blob/main/by7371542c3.Q3_K_S.gguf) \| Q3_K_S \| 3.31GB \| \| [by7371542c3.IQ3_M.gguf](https://huggingface.co/RichardErkhov/Primeness_-_by7371542c3-gguf/blob/main/by7371542c3.IQ3_M.gguf) \| IQ3_M \| 3.42GB \| \| [by7371542c3.Q3_K.gguf](https://huggingface.co/RichardErkhov/Primeness_-_by7371542c3-gguf/blob/main/by7371542c3.Q3_K.gguf) \| Q3_K \| 3.61GB \| \| [by7371542c3.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/Primeness_-_by7371542c3-gguf/blob/main/by7371542c3.Q3_K_M.gguf) \| Q3_K_M \| 3.61GB \| \| [by7371542c3.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/Primeness_-_by7371542c3-gguf/blob/main/by7371542c3.Q3_K_L.gguf) \| Q3_K_L \| 3.89GB \| \| [by7371542c3.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/Primeness_-_by7371542c3-gguf/blob/main/by7371542c3.IQ4_XS.gguf) \| IQ4_XS \| 4.03GB \| \| [by7371542c3.Q4_0.gguf](https://huggingface.co/RichardErkhov/Primeness_-_by7371542c3-gguf/blob/main/by7371542c3.Q4_0.gguf) \| Q4_0 \| 4.19GB \| \| [by7371542c3.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/Primeness_-_by7371542c3-gguf/blob/main/by7371542c3.IQ4_NL.gguf) \| IQ4_NL \| 4.23GB \| \| [by7371542c3.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/Primeness_-_by7371542c3-gguf/blob/main/by7371542c3.Q4_K_S.gguf) \| Q4_K_S \| 4.21GB \| \| [by7371542c3.Q4_K.gguf](https://huggingface.co/RichardErkhov/Primeness_-_by7371542c3-gguf/blob/main/by7371542c3.Q4_K.gguf) \| Q4_K \| 4.41GB \| \| [by7371542c3.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/Primeness_-_by7371542c3-gguf/blob/main/by7371542c3.Q4_K_M.gguf) \| Q4_K_M \| 4.41GB \| \| [by7371542c3.Q4_1.gguf](https://huggingface.co/RichardErkhov/Primeness_-_by7371542c3-gguf/blob/main/by7371542c3.Q4_1.gguf) \| Q4_1 \| 4.6GB \| \| [by7371542c3.Q5_0.gguf](https://huggingface.co/RichardErkhov/Primeness_-_by7371542c3-gguf/blob/main/by7371542c3.Q5_0.gguf) \| Q5_0 \| 5.02GB \| \| [by7371542c3.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/Primeness_-_by7371542c3-gguf/blob/main/by7371542c3.Q5_K_S.gguf) \| Q5_K_S \| 5.02GB \| \| [by7371542c3.Q5_K.gguf](https://huggingface.co/RichardErkhov/Primeness_-_by7371542c3-gguf/blob/main/by7371542c3.Q5_K.gguf) \| Q5_K \| 5.13GB \| \| [by7371542c3.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/Primeness_-_by7371542c3-gguf/blob/main/by7371542c3.Q5_K_M.gguf) \| Q5_K_M \| 5.13GB \| \| [by7371542c3.Q5_1.gguf](https://huggingface.co/RichardErkhov/Primeness_-_by7371542c3-gguf/blob/main/by7371542c3.Q5_1.gguf) \| Q5_1 \| 5.43GB \| \| [by7371542c3.Q6_K.gguf](https://huggingface.co/RichardErkhov/Primeness_-_by7371542c3-gguf/blob/main/by7371542c3.Q6_K.gguf) \| Q6_K \| 5.9GB \| \| [by7371542c3.Q8_0.gguf](https://huggingface.co/RichardErkhov/Primeness_-_by7371542c3-gguf/blob/main/by7371542c3.Q8_0.gguf) \| Q8_0 \| 7.64GB \| Original model description: --- 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. 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dwb2023/legal-ft-2	dwb2023	2025-04-29T03:50:36Z	0	0	sentence-transformers	[ "sentence-transformers", "safetensors", "bert", "sentence-similarity", "feature-extraction", "generated_from_trainer", "dataset_size:156", "loss:MatryoshkaLoss", "loss:MultipleNegativesRankingLoss", "arxiv:1908.10084", "arxiv:2205.13147", "arxiv:1705.00652", "base_model:Snowflake/snowflake-arctic-embed-l", "base_model:finetune:Snowflake/snowflake-arctic-embed-l", "model-index", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	sentence-similarity	2025-04-29T03:45:32Z	--- tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:156 - loss:MatryoshkaLoss - loss:MultipleNegativesRankingLoss base_model: Snowflake/snowflake-arctic-embed-l widget: - source_sentence: Which multi-modal models were released by significant vendors in 2024, and in which months did they appear? sentences: - 'An interesting point of comparison here could be the way railways rolled out around the world in the 1800s. Constructing these required enormous investments and had a massive environmental impact, and many of the lines that were built turned out to be unnecessary—sometimes multiple lines from different companies serving the exact same routes! The resulting bubbles contributed to several financial crashes, see Wikipedia for Panic of 1873, Panic of 1893, Panic of 1901 and the UK’s Railway Mania. They left us with a lot of useful infrastructure and a great deal of bankruptcies and environmental damage. The year of slop' - 'In 2024, almost every significant model vendor released multi-modal models. We saw the Claude 3 series from Anthropic in March, Gemini 1.5 Pro in April (images, audio and video), then September brought Qwen2-VL and Mistral’s Pixtral 12B and Meta’s Llama 3.2 11B and 90B vision models. We got audio input and output from OpenAI in October, then November saw SmolVLM from Hugging Face and December saw image and video models from Amazon Nova. In October I upgraded my LLM CLI tool to support multi-modal models via attachments. It now has plugins for a whole collection of different vision models.' - 'OpenAI made GPT-4o free for all users in May, and Claude 3.5 Sonnet was freely available from its launch in June. This was a momentus change, because for the previous year free users had mostly been restricted to GPT-3.5 level models, meaning new users got a very inaccurate mental model of what a capable LLM could actually do. That era appears to have ended, likely permanently, with OpenAI’s launch of ChatGPT Pro. This $200/month subscription service is the only way to access their most capable model, o1 Pro. Since the trick behind the o1 series (and the future models it will undoubtedly inspire) is to expend more compute time to get better results, I don’t think those days of free access to the best available models are likely to return.' - source_sentence: How did the construction of railways in the 1800s impact the environment? sentences: - 'The environmental impact got much, much worse The much bigger problem here is the enormous competitive buildout of the infrastructure that is imagined to be necessary for these models in the future. Companies like Google, Meta, Microsoft and Amazon are all spending billions of dollars rolling out new datacenters, with a very material impact on the electricity grid and the environment. There’s even talk of spinning up new nuclear power stations, but those can take decades. Is this infrastructure necessary? DeepSeek v3’s $6m training cost and the continued crash in LLM prices might hint that it’s not. But would you want to be the big tech executive that argued NOT to build out this infrastructure only to be proven wrong in a few years’ time?' - 'An interesting point of comparison here could be the way railways rolled out around the world in the 1800s. Constructing these required enormous investments and had a massive environmental impact, and many of the lines that were built turned out to be unnecessary—sometimes multiple lines from different companies serving the exact same routes! The resulting bubbles contributed to several financial crashes, see Wikipedia for Panic of 1873, Panic of 1893, Panic of 1901 and the UK’s Railway Mania. They left us with a lot of useful infrastructure and a great deal of bankruptcies and environmental damage. The year of slop' - 'The boring yet crucial secret behind good system prompts is test-driven development. You don’t write down a system prompt and find ways to test it. You write down tests and find a system prompt that passes them. It’s become abundantly clear over the course of 2024 that writing good automated evals for LLM-powered systems is the skill that’s most needed to build useful applications on top of these models. If you have a strong eval suite you can adopt new models faster, iterate better and build more reliable and useful product features than your competition. Vercel’s Malte Ubl:' - source_sentence: How is a prompt without evals, models, and UX compared in the given context? sentences: - 'DeepSeek v3 is a huge 685B parameter model—one of the largest openly licensed models currently available, significantly bigger than the largest of Meta’s Llama series, Llama 3.1 405B. Benchmarks put it up there with Claude 3.5 Sonnet. Vibe benchmarks (aka the Chatbot Arena) currently rank it 7th, just behind the Gemini 2.0 and OpenAI 4o/o1 models. This is by far the highest ranking openly licensed model. The really impressive thing about DeepSeek v3 is the training cost. The model was trained on 2,788,000 H800 GPU hours at an estimated cost of $5,576,000. Llama 3.1 405B trained 30,840,000 GPU hours—11x that used by DeepSeek v3, for a model that benchmarks slightly worse.' - 'When @v0 first came out we were paranoid about protecting the prompt with all kinds of pre and post processing complexity. We completely pivoted to let it rip. A prompt without the evals, models, and especially UX is like getting a broken ASML machine without a manual' - 'So far, I think they’re a net positive. I’ve used them on a personal level to improve my productivity (and entertain myself) in all sorts of different ways. I think people who learn how to use them effectively can gain a significant boost to their quality of life. A lot of people are yet to be sold on their value! Some think their negatives outweigh their positives, some think they are all hot air, and some even think they represent an existential threat to humanity. They’re actually quite easy to build The most surprising thing we’ve learned about LLMs this year is that they’re actually quite easy to build.' - source_sentence: Why might achieving AGI be necessary to fully solve the problem of gullibility in AI agents? sentences: - 'We already knew LLMs were spookily good at writing code. If you prompt them right, it turns out they can build you a full interactive application using HTML, CSS and JavaScript (and tools like React if you wire up some extra supporting build mechanisms)—often in a single prompt. Anthropic kicked this idea into high gear when they released Claude Artifacts, a groundbreaking new feature that was initially slightly lost in the noise due to being described half way through their announcement of the incredible Claude 3.5 Sonnet. With Artifacts, Claude can write you an on-demand interactive application and then let you use it directly inside the Claude interface. Here’s my Extract URLs app, entirely generated by Claude:' - 'I’m still trying to figure out the best patterns for doing this for my own work. Everyone knows that evals are important, but there remains a lack of great guidance for how to best implement them—I’m tracking this under my evals tag. My SVG pelican riding a bicycle benchmark is a pale imitation of what a real eval suite should look like. Apple Intelligence is bad, Apple’s MLX library is excellent As a Mac user I’ve been feeling a lot better about my choice of platform this year. Last year it felt like my lack of a Linux/Windows machine with an NVIDIA GPU was a huge disadvantage in terms of trying out new models.' - 'A lot of people are excited about AI agents—an infuriatingly vague term that seems to be converging on “AI systems that can go away and act on your behalf”. We’ve been talking about them all year, but I’ve seen few if any examples of them running in production, despite lots of exciting prototypes. I think this is because of gullibility. Can we solve this? Honestly, I’m beginning to suspect that you can’t fully solve gullibility without achieving AGI. So it may be quite a while before those agent dreams can really start to come true! Code may be the best application Over the course of the year, it’s become increasingly clear that writing code is one of the things LLMs are most capable of.' - source_sentence: How many lines of Python code are generally needed to train a basic version of a powerful system? sentences: - 'Intuitively, one would expect that systems this powerful would take millions of lines of complex code. Instead, it turns out a few hundred lines of Python is genuinely enough to train a basic version! What matters most is the training data. You need a lot of data to make these things work, and the quantity and quality of the training data appears to be the most important factor in how good the resulting model is. If you can gather the right data, and afford to pay for the GPUs to train it, you can build an LLM.' - 'The two main categories I see are people who think AI agents are obviously things that go and act on your behalf—the travel agent model—and people who think in terms of LLMs that have been given access to tools which they can run in a loop as part of solving a problem. The term “autonomy” is often thrown into the mix too, again without including a clear definition. (I also collected 211 definitions on Twitter a few months ago—here they are in Datasette Lite—and had gemini-exp-1206 attempt to summarize them.) Whatever the term may mean, agents still have that feeling of perpetually “coming soon”.' - 'Law is not ethics. Is it OK to train models on people’s content without their permission, when those models will then be used in ways that compete with those people? As the quality of results produced by AI models has increased over the year, these questions have become even more pressing. The impact on human society in terms of these models is already huge, if difficult to objectively measure. People have certainly lost work to them—anecdotally, I’ve seen this for copywriters, artists and translators. There are a great deal of untold stories here. I’m hoping 2024 sees significant amounts of dedicated journalism on this topic. My blog in 2023 Here’s a tag cloud for content I posted to my blog in 2023 (generated using Django SQL Dashboard):' pipeline_tag: sentence-similarity library_name: sentence-transformers metrics: - cosine_accuracy@1 - cosine_accuracy@3 - cosine_accuracy@5 - cosine_accuracy@10 - cosine_precision@1 - cosine_precision@3 - cosine_precision@5 - cosine_precision@10 - cosine_recall@1 - cosine_recall@3 - cosine_recall@5 - cosine_recall@10 - cosine_ndcg@10 - cosine_mrr@10 - cosine_map@100 model-index: - name: SentenceTransformer based on Snowflake/snowflake-arctic-embed-l results: - task: type: information-retrieval name: Information Retrieval dataset: name: Unknown type: unknown metrics: - type: cosine_accuracy@1 value: 0.9583333333333334 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 1.0 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 1.0 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 1.0 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.9583333333333334 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.3333333333333333 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.20000000000000004 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.10000000000000002 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.9583333333333334 name: Cosine Recall@1 - type: cosine_recall@3 value: 1.0 name: Cosine Recall@3 - type: cosine_recall@5 value: 1.0 name: Cosine Recall@5 - type: cosine_recall@10 value: 1.0 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.9846220730654774 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.9791666666666666 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.9791666666666666 name: Cosine Map@100 --- # SentenceTransformer based on Snowflake/snowflake-arctic-embed-l This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [Snowflake/snowflake-arctic-embed-l](https://huggingface.co/Snowflake/snowflake-arctic-embed-l). It maps sentences & paragraphs to a 1024-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. ## Model Details ### Model Description - Model Type: Sentence Transformer - Base model: [Snowflake/snowflake-arctic-embed-l](https://huggingface.co/Snowflake/snowflake-arctic-embed-l) <!-- at revision d8fb21ca8d905d2832ee8b96c894d3298964346b --> - Maximum Sequence Length: 512 tokens - Output Dimensionality: 1024 dimensions - Similarity Function: Cosine Similarity <!-- - Training Dataset: Unknown --> <!-- - Language: Unknown --> <!-- - License: Unknown --> ### Model Sources - Documentation: [Sentence Transformers Documentation](https://sbert.net) - Repository: [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) - Hugging Face: [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) ### Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, '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): Normalize() ) ``` ## Usage ### Direct Usage (Sentence Transformers) First install the Sentence Transformers library: ```bash pip install -U sentence-transformers ``` Then you can load this model and run inference. ```python from sentence_transformers import SentenceTransformer # Download from the 🤗 Hub model = SentenceTransformer("dwb2023/legal-ft-2") # Run inference sentences = [ 'How many lines of Python code are generally needed to train a basic version of a powerful system?', 'Intuitively, one would expect that systems this powerful would take millions of lines of complex code. Instead, it turns out a few hundred lines of Python is genuinely enough to train a basic version!\nWhat matters most is the training data. You need a lot of data to make these things work, and the quantity and quality of the training data appears to be the most important factor in how good the resulting model is.\nIf you can gather the right data, and afford to pay for the GPUs to train it, you can build an LLM.', 'Law is not ethics. Is it OK to train models on people’s content without their permission, when those models will then be used in ways that compete with those people?\nAs the quality of results produced by AI models has increased over the year, these questions have become even more pressing.\nThe impact on human society in terms of these models is already huge, if difficult to objectively measure.\nPeople have certainly lost work to them—anecdotally, I’ve seen this for copywriters, artists and translators.\nThere are a great deal of untold stories here. I’m hoping 2024 sees significant amounts of dedicated journalism on this topic.\nMy blog in 2023\nHere’s a tag cloud for content I posted to my blog in 2023 (generated using Django SQL Dashboard):', ] embeddings = model.encode(sentences) print(embeddings.shape) # [3, 1024] # Get the similarity scores for the embeddings similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] ``` <!-- ### Direct Usage (Transformers) <details><summary>Click to see the direct usage in Transformers</summary> </details> --> <!-- ### Downstream Usage (Sentence Transformers) You can finetune this model on your own dataset. <details><summary>Click to expand</summary> </details> --> <!-- ### Out-of-Scope Use List how the model may foreseeably be misused and address what users ought not to do with the model. --> ## Evaluation ### Metrics #### Information Retrieval * Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) \| Metric \| Value \| \|:--------------------\|:-----------\| \| cosine_accuracy@1 \| 0.9583 \| \| cosine_accuracy@3 \| 1.0 \| \| cosine_accuracy@5 \| 1.0 \| \| cosine_accuracy@10 \| 1.0 \| \| cosine_precision@1 \| 0.9583 \| \| cosine_precision@3 \| 0.3333 \| \| cosine_precision@5 \| 0.2 \| \| cosine_precision@10 \| 0.1 \| \| cosine_recall@1 \| 0.9583 \| \| cosine_recall@3 \| 1.0 \| \| cosine_recall@5 \| 1.0 \| \| cosine_recall@10 \| 1.0 \| \| cosine_ndcg@10 \| 0.9846 \| \| cosine_mrr@10 \| 0.9792 \| \| cosine_map@100 \| 0.9792 \| <!-- ## Bias, Risks and Limitations What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model. --> <!-- ### Recommendations What are recommendations with respect to the foreseeable issues? For example, filtering explicit content. --> ## Training Details ### Training Dataset #### Unnamed Dataset * Size: 156 training samples * Columns: <code>sentence_0</code> and <code>sentence_1</code> * Approximate statistics based on the first 156 samples: \| \| sentence_0 \| sentence_1 \| \|:--------\|:-----------------------------------------------------------------------------------\|:-------------------------------------------------------------------------------------\| \| type \| string \| string \| \| details \| <ul><li>min: 12 tokens</li><li>mean: 21.09 tokens</li><li>max: 37 tokens</li></ul> \| <ul><li>min: 43 tokens</li><li>mean: 135.28 tokens</li><li>max: 214 tokens</li></ul> \| * Samples: \| sentence_0 \| sentence_1 \| \|:----------------------------------------------------------------------------------------------------------------------------\|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\| \| <code>What significant development in Artificial Intelligence occurred in 2023 according to Simon Willison’s weblog?</code> \| <code>Stuff we figured out about AI in 2023<br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br>Simon Willison’s Weblog<br>Subscribe<br><br><br><br><br><br><br>Stuff we figured out about AI in 2023<br>31st December 2023<br>2023 was the breakthrough year for Large Language Models (LLMs). I think it’s OK to call these AI—they’re the latest and (currently) most interesting development in the academic field of Artificial Intelligence that dates back to the 1950s.<br>Here’s my attempt to round up the highlights in one place!</code> \| \| <code>How does Simon Willison describe Large Language Models (LLMs) in the context of AI history?</code> \| <code>Stuff we figured out about AI in 2023<br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br>Simon Willison’s Weblog<br>Subscribe<br><br><br><br><br><br><br>Stuff we figured out about AI in 2023<br>31st December 2023<br>2023 was the breakthrough year for Large Language Models (LLMs). I think it’s OK to call these AI—they’re the latest and (currently) most interesting development in the academic field of Artificial Intelligence that dates back to the 1950s.<br>Here’s my attempt to round up the highlights in one place!</code> \| \| <code>What are some challenges mentioned in building large language models like GPT-4?</code> \| <code>Large Language Models<br>They’re actually quite easy to build<br>You can run LLMs on your own devices<br>Hobbyists can build their own fine-tuned models<br>We don’t yet know how to build GPT-4<br>Vibes Based Development<br>LLMs are really smart, and also really, really dumb<br>Gullibility is the biggest unsolved problem<br>Code may be the best application<br>The ethics of this space remain diabolically complex<br>My blog in 2023</code> \| * Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: ```json { "loss": "MultipleNegativesRankingLoss", "matryoshka_dims": [ 768, 512, 256, 128, 64 ], "matryoshka_weights": [ 1, 1, 1, 1, 1 ], "n_dims_per_step": -1 } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `eval_strategy`: steps - `per_device_train_batch_size`: 10 - `per_device_eval_batch_size`: 10 - `num_train_epochs`: 10 - `multi_dataset_batch_sampler`: round_robin #### All Hyperparameters <details><summary>Click to expand</summary> - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: steps - `prediction_loss_only`: True - `per_device_train_batch_size`: 10 - `per_device_eval_batch_size`: 10 - `per_gpu_train_batch_size`: None - `per_gpu_eval_batch_size`: None - `gradient_accumulation_steps`: 1 - `eval_accumulation_steps`: None - `torch_empty_cache_steps`: None - `learning_rate`: 5e-05 - `weight_decay`: 0.0 - `adam_beta1`: 0.9 - `adam_beta2`: 0.999 - `adam_epsilon`: 1e-08 - `max_grad_norm`: 1 - `num_train_epochs`: 10 - `max_steps`: -1 - `lr_scheduler_type`: linear - `lr_scheduler_kwargs`: {} - `warmup_ratio`: 0.0 - `warmup_steps`: 0 - `log_level`: passive - `log_level_replica`: warning - `log_on_each_node`: True - `logging_nan_inf_filter`: True - `save_safetensors`: True - `save_on_each_node`: False - `save_only_model`: False - `restore_callback_states_from_checkpoint`: False - `no_cuda`: False - `use_cpu`: False - `use_mps_device`: False - `seed`: 42 - `data_seed`: None - `jit_mode_eval`: False - `use_ipex`: False - `bf16`: False - `fp16`: False - `fp16_opt_level`: O1 - `half_precision_backend`: auto - `bf16_full_eval`: False - `fp16_full_eval`: False - `tf32`: None - `local_rank`: 0 - `ddp_backend`: None - `tpu_num_cores`: None - `tpu_metrics_debug`: False - `debug`: [] - `dataloader_drop_last`: False - `dataloader_num_workers`: 0 - `dataloader_prefetch_factor`: None - `past_index`: -1 - `disable_tqdm`: False - `remove_unused_columns`: True - `label_names`: None - `load_best_model_at_end`: False - `ignore_data_skip`: False - `fsdp`: [] - `fsdp_min_num_params`: 0 - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} - `fsdp_transformer_layer_cls_to_wrap`: None - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} - `deepspeed`: None - `label_smoothing_factor`: 0.0 - `optim`: adamw_torch - `optim_args`: None - `adafactor`: False - `group_by_length`: False - `length_column_name`: length - `ddp_find_unused_parameters`: None - `ddp_bucket_cap_mb`: None - `ddp_broadcast_buffers`: False - `dataloader_pin_memory`: True - `dataloader_persistent_workers`: False - `skip_memory_metrics`: True - `use_legacy_prediction_loop`: False - `push_to_hub`: False - `resume_from_checkpoint`: None - `hub_model_id`: None - `hub_strategy`: every_save - `hub_private_repo`: None - `hub_always_push`: False - `gradient_checkpointing`: False - `gradient_checkpointing_kwargs`: None - `include_inputs_for_metrics`: False - `include_for_metrics`: [] - `eval_do_concat_batches`: True - `fp16_backend`: auto - `push_to_hub_model_id`: None - `push_to_hub_organization`: None - `mp_parameters`: - `auto_find_batch_size`: False - `full_determinism`: False - `torchdynamo`: None - `ray_scope`: last - `ddp_timeout`: 1800 - `torch_compile`: False - `torch_compile_backend`: None - `torch_compile_mode`: None - `dispatch_batches`: None - `split_batches`: None - `include_tokens_per_second`: False - `include_num_input_tokens_seen`: False - `neftune_noise_alpha`: None - `optim_target_modules`: None - `batch_eval_metrics`: False - `eval_on_start`: False - `use_liger_kernel`: False - `eval_use_gather_object`: False - `average_tokens_across_devices`: False - `prompts`: None - `batch_sampler`: batch_sampler - `multi_dataset_batch_sampler`: round_robin </details> ### Training Logs \| Epoch \| Step \| cosine_ndcg@10 \| \|:-----:\|:----:\|:--------------:\| \| 1.0 \| 16 \| 0.9484 \| \| 2.0 \| 32 \| 0.9539 \| \| 3.0 \| 48 \| 0.9539 \| \| 3.125 \| 50 \| 0.9539 \| \| 4.0 \| 64 \| 0.9692 \| \| 5.0 \| 80 \| 0.9692 \| \| 6.0 \| 96 \| 0.9692 \| \| 6.25 \| 100 \| 0.9692 \| \| 7.0 \| 112 \| 0.9846 \| \| 8.0 \| 128 \| 0.9846 \| \| 9.0 \| 144 \| 0.9846 \| \| 9.375 \| 150 \| 0.9846 \| \| 10.0 \| 160 \| 0.9846 \| ### Framework Versions - Python: 3.13.2 - Sentence Transformers: 3.4.1 - Transformers: 4.48.3 - PyTorch: 2.6.0+cu124 - Accelerate: 1.3.0 - Datasets: 3.2.0 - Tokenizers: 0.21.0 ## Citation ### BibTeX #### Sentence Transformers ```bibtex @inproceedings{reimers-2019-sentence-bert, title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", author = "Reimers, Nils and Gurevych, Iryna", booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", month = "11", year = "2019", publisher = "Association for Computational Linguistics", url = "https://arxiv.org/abs/1908.10084", } ``` #### MatryoshkaLoss ```bibtex @misc{kusupati2024matryoshka, title={Matryoshka Representation Learning}, author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi}, year={2024}, eprint={2205.13147}, archivePrefix={arXiv}, primaryClass={cs.LG} } ``` #### MultipleNegativesRankingLoss ```bibtex @misc{henderson2017efficient, title={Efficient Natural Language Response Suggestion for Smart Reply}, author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil}, year={2017}, eprint={1705.00652}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` <!-- ## Glossary Clearly define terms in order to be accessible across audiences. --> <!-- ## Model Card Authors Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction. --> <!-- ## Model Card Contact Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors. -->
hleAtKeeper/skewed-threat-classifier-BERT	hleAtKeeper	2025-04-29T03:50:16Z	46	0	transformers	[ "transformers", "safetensors", "bert", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-04-22T22:08:08Z	--- 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. 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yanyuss/oxford-pet-segmentation	yanyuss	2025-04-29T03:40:08Z	0	0	segmentation-models-pytorch	[ "segmentation-models-pytorch", "safetensors", "model_hub_mixin", "pytorch_model_hub_mixin", "semantic-segmentation", "pytorch", "image-segmentation", "license:mit", "region:us" ]	image-segmentation	2025-04-29T03:40:02Z	--- library_name: segmentation-models-pytorch license: mit pipeline_tag: image-segmentation tags: - model_hub_mixin - pytorch_model_hub_mixin - segmentation-models-pytorch - semantic-segmentation - pytorch languages: - python --- # FPN Model Card Table of Contents: - [Load trained model](#load-trained-model) - [Model init parameters](#model-init-parameters) - [Model metrics](#model-metrics) - [Dataset](#dataset) ## Load trained model ```python import segmentation_models_pytorch as smp model = smp.from_pretrained("<save-directory-or-this-repo>") ``` ## Model init parameters ```python model_init_params = { "encoder_name": "resnet34", "encoder_depth": 5, "encoder_weights": "imagenet", "decoder_pyramid_channels": 256, "decoder_segmentation_channels": 128, "decoder_merge_policy": "add", "decoder_dropout": 0.2, "decoder_interpolation": "nearest", "in_channels": 3, "classes": 1, "activation": None, "upsampling": 4, "aux_params": None } ``` ## Model metrics ```json [ { "test_per_image_iou": 0.909460723400116, "test_dataset_iou": 0.9167296290397644 } ] ``` ## Dataset Dataset name: Oxford Pet ## More Information - Library: https://github.com/qubvel/segmentation_models.pytorch - Docs: https://smp.readthedocs.io/en/latest/ This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin)
Joker-sxj/Qwen2.5-3B-instruct-medical-finetuned	Joker-sxj	2025-04-29T03:39:51Z	84	2	null	[ "safetensors", "qwen2", "medical", "question-answering", "zho", "eng", "fra", "spa", "por", "deu", "ita", "rus", "jpn", "kor", "vie", "tha", "ara", "dataset:FreedomIntelligence/medical-o1-reasoning-SFT", "base_model:Qwen/Qwen2.5-3B-Instruct", "base_model:finetune:Qwen/Qwen2.5-3B-Instruct", "license:apache-2.0", "region:us" ]	question-answering	2025-03-25T07:05:54Z	--- license: apache-2.0 datasets: - FreedomIntelligence/medical-o1-reasoning-SFT metrics: - bleu base_model: - Qwen/Qwen2.5-3B-Instruct pipeline_tag: question-answering tags: - medical language: - zho - eng - fra - spa - por - deu - ita - rus - jpn - kor - vie - tha - ara --- 模型经过医疗数据集微调后，已初步具备推理能力，可以进行基础的问诊，且文本的质量BLEU比原模型更优。
taobao-mnn/Qwen3-32B-MNN	taobao-mnn	2025-04-29T03:37:08Z	0	0	null	[ "chat", "text-generation", "en", "license:apache-2.0", "region:us" ]	text-generation	2025-04-28T13:43:37Z	--- license: apache-2.0 language: - en pipeline_tag: text-generation tags: - chat --- # Qwen3-32B-MNN ## Introduction This model is a 4-bit quantized version of the MNN model exported from Qwen3-32B using [llmexport](https://github.com/alibaba/MNN/tree/master/transformers/llm/export). ## Download ```bash # install huggingface pip install huggingface ``` ```bash # shell download huggingface download --model 'taobao-mnn/Qwen3-32B-MNN' --local_dir 'path/to/dir' ``` ```python # SDK download from huggingface_hub import snapshot_download model_dir = snapshot_download('taobao-mnn/Qwen3-32B-MNN') ``` ```bash # git clone git clone https://www.modelscope.cn/taobao-mnn/Qwen3-32B-MNN ``` ## Usage ```bash # clone MNN source git clone https://github.com/alibaba/MNN.git # compile cd MNN mkdir build && cd build cmake .. -DMNN_LOW_MEMORY=true -DMNN_CPU_WEIGHT_DEQUANT_GEMM=true -DMNN_BUILD_LLM=true -DMNN_SUPPORT_TRANSFORMER_FUSE=true make -j # run ./llm_demo /path/to/Qwen3-32B-MNN/config.json prompt.txt ``` ## Document [MNN-LLM](https://mnn-docs.readthedocs.io/en/latest/transformers/llm.html#)
TheGardener/MLP-pruner-ver3-activation-llama3.2-0.83B	TheGardener	2025-04-29T03:36:47Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-29T03:33:20Z	--- 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]
taobao-mnn/Qwen3-8B-MNN	taobao-mnn	2025-04-29T03:36:41Z	0	0	null	[ "chat", "text-generation", "en", "license:apache-2.0", "region:us" ]	text-generation	2025-04-28T13:16:09Z	--- license: apache-2.0 language: - en pipeline_tag: text-generation tags: - chat --- # Qwen3-8B-MNN ## Introduction This model is a 4-bit quantized version of the MNN model exported from Qwen3-8B using [llmexport](https://github.com/alibaba/MNN/tree/master/transformers/llm/export). ## Download ```bash # install huggingface pip install huggingface ``` ```bash # shell download huggingface download --model 'taobao-mnn/Qwen3-8B-MNN' --local_dir 'path/to/dir' ``` ```python # SDK download from huggingface_hub import snapshot_download model_dir = snapshot_download('taobao-mnn/Qwen3-8B-MNN') ``` ```bash # git clone git clone https://www.modelscope.cn/taobao-mnn/Qwen3-8B-MNN ``` ## Usage ```bash # clone MNN source git clone https://github.com/alibaba/MNN.git # compile cd MNN mkdir build && cd build cmake .. -DMNN_LOW_MEMORY=true -DMNN_CPU_WEIGHT_DEQUANT_GEMM=true -DMNN_BUILD_LLM=true -DMNN_SUPPORT_TRANSFORMER_FUSE=true make -j # run ./llm_demo /path/to/Qwen3-8B-MNN/config.json prompt.txt ``` ## Document [MNN-LLM](https://mnn-docs.readthedocs.io/en/latest/transformers/llm.html#)
taobao-mnn/Qwen3-4B-MNN	taobao-mnn	2025-04-29T03:36:29Z	0	0	null	[ "chat", "text-generation", "en", "license:apache-2.0", "region:us" ]	text-generation	2025-04-28T13:09:26Z	--- license: apache-2.0 language: - en pipeline_tag: text-generation tags: - chat --- # Qwen3-4B-MNN ## Introduction This model is a 4-bit quantized version of the MNN model exported from Qwen3-4B using [llmexport](https://github.com/alibaba/MNN/tree/master/transformers/llm/export). ## Download ```bash # install huggingface pip install huggingface ``` ```bash # shell download huggingface download --model 'taobao-mnn/Qwen3-4B-MNN' --local_dir 'path/to/dir' ``` ```python # SDK download from huggingface_hub import snapshot_download model_dir = snapshot_download('taobao-mnn/Qwen3-4B-MNN') ``` ```bash # git clone git clone https://www.modelscope.cn/taobao-mnn/Qwen3-4B-MNN ``` ## Usage ```bash # clone MNN source git clone https://github.com/alibaba/MNN.git # compile cd MNN mkdir build && cd build cmake .. -DMNN_LOW_MEMORY=true -DMNN_CPU_WEIGHT_DEQUANT_GEMM=true -DMNN_BUILD_LLM=true -DMNN_SUPPORT_TRANSFORMER_FUSE=true make -j # run ./llm_demo /path/to/Qwen3-4B-MNN/config.json prompt.txt ``` ## Document [MNN-LLM](https://mnn-docs.readthedocs.io/en/latest/transformers/llm.html#)
taobao-mnn/Qwen3-1.7B-MNN	taobao-mnn	2025-04-29T03:36:14Z	0	0	null	[ "chat", "text-generation", "en", "license:apache-2.0", "region:us" ]	text-generation	2025-04-28T13:05:28Z	--- license: apache-2.0 language: - en pipeline_tag: text-generation tags: - chat --- # Qwen3-1.7B-MNN ## Introduction This model is a 4-bit quantized version of the MNN model exported from Qwen3-1.7B using [llmexport](https://github.com/alibaba/MNN/tree/master/transformers/llm/export). ## Download ```bash # install huggingface pip install huggingface ``` ```bash # shell download huggingface download --model 'taobao-mnn/Qwen3-1.7B-MNN' --local_dir 'path/to/dir' ``` ```python # SDK download from huggingface_hub import snapshot_download model_dir = snapshot_download('taobao-mnn/Qwen3-1.7B-MNN') ``` ```bash # git clone git clone https://www.modelscope.cn/taobao-mnn/Qwen3-1.7B-MNN ``` ## Usage ```bash # clone MNN source git clone https://github.com/alibaba/MNN.git # compile cd MNN mkdir build && cd build cmake .. -DMNN_LOW_MEMORY=true -DMNN_CPU_WEIGHT_DEQUANT_GEMM=true -DMNN_BUILD_LLM=true -DMNN_SUPPORT_TRANSFORMER_FUSE=true make -j # run ./llm_demo /path/to/Qwen3-1.7B-MNN/config.json prompt.txt ``` ## Document [MNN-LLM](https://mnn-docs.readthedocs.io/en/latest/transformers/llm.html#)
taobao-mnn/Qwen3-0.6B-MNN	taobao-mnn	2025-04-29T03:36:03Z	0	0	null	[ "chat", "text-generation", "en", "license:apache-2.0", "region:us" ]	text-generation	2025-04-28T12:59:27Z	--- license: apache-2.0 language: - en pipeline_tag: text-generation tags: - chat --- # Qwen3-0.6B-MNN ## Introduction This model is a 4-bit quantized version of the MNN model exported from Qwen3-0.6B using [llmexport](https://github.com/alibaba/MNN/tree/master/transformers/llm/export). ## Download ```bash # install huggingface pip install huggingface ``` ```bash # shell download huggingface download --model 'taobao-mnn/Qwen3-0.6B-MNN' --local_dir 'path/to/dir' ``` ```python # SDK download from huggingface_hub import snapshot_download model_dir = snapshot_download('taobao-mnn/Qwen3-0.6B-MNN') ``` ```bash # git clone git clone https://www.modelscope.cn/taobao-mnn/Qwen3-0.6B-MNN ``` ## Usage ```bash # clone MNN source git clone https://github.com/alibaba/MNN.git # compile cd MNN mkdir build && cd build cmake .. -DMNN_LOW_MEMORY=true -DMNN_CPU_WEIGHT_DEQUANT_GEMM=true -DMNN_BUILD_LLM=true -DMNN_SUPPORT_TRANSFORMER_FUSE=true make -j # run ./llm_demo /path/to/Qwen3-0.6B-MNN/config.json prompt.txt ``` ## Document [MNN-LLM](https://mnn-docs.readthedocs.io/en/latest/transformers/llm.html#)
mradermacher/Llama_3.x_70b_L3.3_VulpeculHiggs_128K_v1.02-i1-GGUF	mradermacher	2025-04-29T03:32:52Z	0	0	transformers	[ "transformers", "gguf", "mergekit", "merge", "en", "base_model:Nexesenex/Llama_3.x_70b_L3.3_VulpeculHiggs_128K_v1.02", "base_model:quantized:Nexesenex/Llama_3.x_70b_L3.3_VulpeculHiggs_128K_v1.02", "endpoints_compatible", "region:us", "imatrix", "conversational" ]	null	2025-04-28T12:36:52Z	--- base_model: Nexesenex/Llama_3.x_70b_L3.3_VulpeculHiggs_128K_v1.02 language: - en library_name: transformers quantized_by: mradermacher tags: - mergekit - merge --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> weighted/imatrix quants of https://huggingface.co/Nexesenex/Llama_3.x_70b_L3.3_VulpeculHiggs_128K_v1.02 <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/Llama_3.x_70b_L3.3_VulpeculHiggs_128K_v1.02-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/Llama_3.x_70b_L3.3_VulpeculHiggs_128K_v1.02-i1-GGUF/resolve/main/Llama_3.x_70b_L3.3_VulpeculHiggs_128K_v1.02.i1-IQ1_S.gguf) \| i1-IQ1_S \| 15.4 \| for the desperate \| \| [GGUF](https://huggingface.co/mradermacher/Llama_3.x_70b_L3.3_VulpeculHiggs_128K_v1.02-i1-GGUF/resolve/main/Llama_3.x_70b_L3.3_VulpeculHiggs_128K_v1.02.i1-IQ1_M.gguf) \| i1-IQ1_M \| 16.9 \| mostly desperate \| \| [GGUF](https://huggingface.co/mradermacher/Llama_3.x_70b_L3.3_VulpeculHiggs_128K_v1.02-i1-GGUF/resolve/main/Llama_3.x_70b_L3.3_VulpeculHiggs_128K_v1.02.i1-IQ2_XXS.gguf) \| i1-IQ2_XXS \| 19.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama_3.x_70b_L3.3_VulpeculHiggs_128K_v1.02-i1-GGUF/resolve/main/Llama_3.x_70b_L3.3_VulpeculHiggs_128K_v1.02.i1-IQ2_XS.gguf) \| i1-IQ2_XS \| 21.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama_3.x_70b_L3.3_VulpeculHiggs_128K_v1.02-i1-GGUF/resolve/main/Llama_3.x_70b_L3.3_VulpeculHiggs_128K_v1.02.i1-IQ2_S.gguf) \| i1-IQ2_S \| 22.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama_3.x_70b_L3.3_VulpeculHiggs_128K_v1.02-i1-GGUF/resolve/main/Llama_3.x_70b_L3.3_VulpeculHiggs_128K_v1.02.i1-IQ2_M.gguf) \| i1-IQ2_M \| 24.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama_3.x_70b_L3.3_VulpeculHiggs_128K_v1.02-i1-GGUF/resolve/main/Llama_3.x_70b_L3.3_VulpeculHiggs_128K_v1.02.i1-Q2_K_S.gguf) \| i1-Q2_K_S \| 24.6 \| very low quality \| \| [GGUF](https://huggingface.co/mradermacher/Llama_3.x_70b_L3.3_VulpeculHiggs_128K_v1.02-i1-GGUF/resolve/main/Llama_3.x_70b_L3.3_VulpeculHiggs_128K_v1.02.i1-Q2_K.gguf) \| i1-Q2_K \| 26.5 \| IQ3_XXS probably better \| \| [GGUF](https://huggingface.co/mradermacher/Llama_3.x_70b_L3.3_VulpeculHiggs_128K_v1.02-i1-GGUF/resolve/main/Llama_3.x_70b_L3.3_VulpeculHiggs_128K_v1.02.i1-IQ3_XXS.gguf) \| i1-IQ3_XXS \| 27.6 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Llama_3.x_70b_L3.3_VulpeculHiggs_128K_v1.02-i1-GGUF/resolve/main/Llama_3.x_70b_L3.3_VulpeculHiggs_128K_v1.02.i1-IQ3_XS.gguf) \| i1-IQ3_XS \| 29.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama_3.x_70b_L3.3_VulpeculHiggs_128K_v1.02-i1-GGUF/resolve/main/Llama_3.x_70b_L3.3_VulpeculHiggs_128K_v1.02.i1-IQ3_S.gguf) \| i1-IQ3_S \| 31.0 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/Llama_3.x_70b_L3.3_VulpeculHiggs_128K_v1.02-i1-GGUF/resolve/main/Llama_3.x_70b_L3.3_VulpeculHiggs_128K_v1.02.i1-Q3_K_S.gguf) \| i1-Q3_K_S \| 31.0 \| IQ3_XS probably better \| \| [GGUF](https://huggingface.co/mradermacher/Llama_3.x_70b_L3.3_VulpeculHiggs_128K_v1.02-i1-GGUF/resolve/main/Llama_3.x_70b_L3.3_VulpeculHiggs_128K_v1.02.i1-IQ3_M.gguf) \| i1-IQ3_M \| 32.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama_3.x_70b_L3.3_VulpeculHiggs_128K_v1.02-i1-GGUF/resolve/main/Llama_3.x_70b_L3.3_VulpeculHiggs_128K_v1.02.i1-Q3_K_M.gguf) \| i1-Q3_K_M \| 34.4 \| IQ3_S probably better \| \| [GGUF](https://huggingface.co/mradermacher/Llama_3.x_70b_L3.3_VulpeculHiggs_128K_v1.02-i1-GGUF/resolve/main/Llama_3.x_70b_L3.3_VulpeculHiggs_128K_v1.02.i1-Q3_K_L.gguf) \| i1-Q3_K_L \| 37.2 \| IQ3_M probably better \| \| [GGUF](https://huggingface.co/mradermacher/Llama_3.x_70b_L3.3_VulpeculHiggs_128K_v1.02-i1-GGUF/resolve/main/Llama_3.x_70b_L3.3_VulpeculHiggs_128K_v1.02.i1-IQ4_XS.gguf) \| i1-IQ4_XS \| 38.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama_3.x_70b_L3.3_VulpeculHiggs_128K_v1.02-i1-GGUF/resolve/main/Llama_3.x_70b_L3.3_VulpeculHiggs_128K_v1.02.i1-Q4_0.gguf) \| i1-Q4_0 \| 40.2 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/Llama_3.x_70b_L3.3_VulpeculHiggs_128K_v1.02-i1-GGUF/resolve/main/Llama_3.x_70b_L3.3_VulpeculHiggs_128K_v1.02.i1-Q4_K_S.gguf) \| i1-Q4_K_S \| 40.4 \| optimal size/speed/quality \| \| [GGUF](https://huggingface.co/mradermacher/Llama_3.x_70b_L3.3_VulpeculHiggs_128K_v1.02-i1-GGUF/resolve/main/Llama_3.x_70b_L3.3_VulpeculHiggs_128K_v1.02.i1-Q4_K_M.gguf) \| i1-Q4_K_M \| 42.6 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Llama_3.x_70b_L3.3_VulpeculHiggs_128K_v1.02-i1-GGUF/resolve/main/Llama_3.x_70b_L3.3_VulpeculHiggs_128K_v1.02.i1-Q4_1.gguf) \| i1-Q4_1 \| 44.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama_3.x_70b_L3.3_VulpeculHiggs_128K_v1.02-i1-GGUF/resolve/main/Llama_3.x_70b_L3.3_VulpeculHiggs_128K_v1.02.i1-Q5_K_S.gguf) \| i1-Q5_K_S \| 48.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama_3.x_70b_L3.3_VulpeculHiggs_128K_v1.02-i1-GGUF/resolve/main/Llama_3.x_70b_L3.3_VulpeculHiggs_128K_v1.02.i1-Q5_K_M.gguf) \| i1-Q5_K_M \| 50.0 \| \| \| [PART 1](https://huggingface.co/mradermacher/Llama_3.x_70b_L3.3_VulpeculHiggs_128K_v1.02-i1-GGUF/resolve/main/Llama_3.x_70b_L3.3_VulpeculHiggs_128K_v1.02.i1-Q6_K.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Llama_3.x_70b_L3.3_VulpeculHiggs_128K_v1.02-i1-GGUF/resolve/main/Llama_3.x_70b_L3.3_VulpeculHiggs_128K_v1.02.i1-Q6_K.gguf.part2of2) \| i1-Q6_K \| 58.0 \| practically like static Q6_K \| 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. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to. <!-- end -->
alfonsogarciacaro/Falcon3-10B-Instruct-1.58bit	alfonsogarciacaro	2025-04-29T03:30:42Z	0	0	transformers	[ "transformers", "gguf", "llama", "text-generation", "bitnet", "falcon3", "conversational", "arxiv:2402.17764", "base_model:tiiuae/Falcon3-10B-Instruct", "base_model:quantized:tiiuae/Falcon3-10B-Instruct", "license:other", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-04-29T03:21:32Z	--- base_model: tiiuae/Falcon3-10B-Instruct library_name: transformers license: other license_name: falcon-llm-license license_link: https://falconllm.tii.ae/falcon-terms-and-conditions.html tags: - bitnet - falcon3 --- ![image/png](https://cdn-uploads.huggingface.co/production/uploads/62441d1d9fdefb55a0b7d12c/c-tosr0FvMlKuKQTojx_6.png) # Table of Contents 0. [TL;DR](#TL;DR) 1. [Model Details](#model-details) 2. [Training Details](#training-details) 3. [Usage](#usage) 4. [Evaluation](#evaluation) 5. [Citation](#citation) # TL;DR # Model Details ## Model Description - Developed by: [https://www.tii.ae](https://www.tii.ae) - Model type: Causal decoder-only - instruct / chat version - Architecture: Pure-transformer - 1.58bit version - Language(s) (NLP): Mainly English - License: TII Falcon License 2.0 # Training details The model has been trained following the training strategies from the recent [1-bit LLM HF blogpost](https://huggingface.co/blog/1_58_llm_extreme_quantization) and [1-bit LLM paper](https://huggingface.co/papers/2402.17764). For more details about the training protocol of this model, please refer to the Falcon-3 technical report, section Compression. # Usage Currently to use this model you can either rely on Hugging Face transformers library or [BitNet](https://github.com/microsoft/BitNet) library. You can also play with the model using the [falcon-1.58bit playground](https://huggingface.co/spaces/tiiuae/falcon3-1.58bit-playground) (only for the 7B instruct version). ## 🤗 transformers ```python import torch from transformers import AutoModelForCausalLM, AutoTokenizer model_id = "tiiuae/Falcon3-7B-Instruct-1.58bit" model = AutoModelForCausalLM.from_pretrained( model_id, torch_dtype=torch.bfloat16, ).to("cuda") # Perform text generation ``` ## BitNet ``` git clone https://github.com/microsoft/BitNet && cd BitNet pip install -r requirements.txt python setup_env.py --hf-repo tiiuae/Falcon3-10B-Instruct-1.58bit -q i2_s python run_inference.py -m models/Falcon3-10B-1.58bit/ggml-model-i2_s.gguf -p "You are a helpful assistant" -cnv ``` # Evaluation We report in the following table our internal pipeline benchmarks: Note evaluation results are normalized score from v2 leaderboard tasks - reported results of original models in the blogpost are raw scores <table border="1" style="width: 100%; text-align: center; border-collapse: collapse;"> <colgroup> <col style="width: 10%;"> <col style="width: 10%;"> <col style="background-color: rgba(80, 15, 213, 0.5); width: 7%;"> </colgroup> <thead> <tr> <th>Benchmark</th> <th>Llama3-8B-1.58-100B-tokens</th> <th>Falcon3-10B-Instruct-1.58bit</th> </tr> </thead> <tbody> <tr> <td>IFEval</td> <td>17.91</td> <td><b>54.37</b></td> </tr> <tr> <td>MUSR</td> <td><b>4.87</b></td> <td>2.57</td> </tr> <tr> <td>GPQA</td> <td>1.83</td> <td><b>4.27</b></td> </tr> <tr> <td>BBH</td> <td>5.36</td> <td><b>6.59</b></td> </tr> <tr> <td>MMLU-PRO</td> <td>2.78</td> <td><b>6.62</b></td> </tr> <tr> <td>MATH</td> <td>0.26</td> <td><b>2.44</b></td> </tr> <tr> <td>Average</td> <td>5.5</td> <td><b>12.81</b></td> </tr> </tbody> </table> ## Useful links - View our [release blogpost](https://huggingface.co/blog/falcon3). - Feel free to join [our discord server](https://discord.gg/fwXpMyGc) if you have any questions or to interact with our researchers and developers. ## Citation If the Falcon3 family of models were helpful to your work, feel free to give us a cite. ``` @misc{Falcon3, title = {The Falcon 3 Family of Open Models}, author = {Falcon-LLM Team}, month = {December}, year = {2024} } ```
Aldaris/GLM-4-32B-0414-Q4_K_M-GGUF	Aldaris	2025-04-29T03:27:48Z	0	0	transformers	[ "transformers", "gguf", "llama-cpp", "gguf-my-repo", "text-generation", "zh", "en", "base_model:THUDM/GLM-4-32B-0414", "base_model:quantized:THUDM/GLM-4-32B-0414", "license:mit", "endpoints_compatible", "region:us", "conversational" ]	text-generation	2025-04-29T03:26:21Z	--- base_model: THUDM/GLM-4-32B-0414 language: - zh - en library_name: transformers license: mit pipeline_tag: text-generation tags: - llama-cpp - gguf-my-repo --- # Aldaris/GLM-4-32B-0414-Q4_K_M-GGUF This model was converted to GGUF format from [`THUDM/GLM-4-32B-0414`](https://huggingface.co/THUDM/GLM-4-32B-0414) 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/THUDM/GLM-4-32B-0414) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo Aldaris/GLM-4-32B-0414-Q4_K_M-GGUF --hf-file glm-4-32b-0414-q4_k_m.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo Aldaris/GLM-4-32B-0414-Q4_K_M-GGUF --hf-file glm-4-32b-0414-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. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo Aldaris/GLM-4-32B-0414-Q4_K_M-GGUF --hf-file glm-4-32b-0414-q4_k_m.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo Aldaris/GLM-4-32B-0414-Q4_K_M-GGUF --hf-file glm-4-32b-0414-q4_k_m.gguf -c 2048 ```
tiamda/gemma-text-to-sql	tiamda	2025-04-29T03:22:38Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "generated_from_trainer", "trl", "sft", "base_model:google/gemma-3-1b-pt", "base_model:finetune:google/gemma-3-1b-pt", "endpoints_compatible", "region:us" ]	null	2025-04-29T01:21:17Z	--- base_model: google/gemma-3-1b-pt library_name: transformers model_name: gemma-text-to-sql tags: - generated_from_trainer - trl - sft licence: license --- # Model Card for gemma-text-to-sql This model is a fine-tuned version of [google/gemma-3-1b-pt](https://huggingface.co/google/gemma-3-1b-pt). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="tiamda/gemma-text-to-sql", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with SFT. ### Framework versions - TRL: 0.15.2 - Transformers: 4.51.3 - Pytorch: 2.6.0 - Datasets: 3.5.0 - Tokenizers: 0.21.1 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
ReadyArt/The-Omega-Directive-Qwen3-14B-v1.1	ReadyArt	2025-04-29T03:22:29Z	0	2	null	[ "safetensors", "qwen3", "nsfw", "explicit", "roleplay", "unaligned", "adult", "ERP", "text-generation", "conversational", "en", "base_model:Qwen/Qwen3-14B", "base_model:finetune:Qwen/Qwen3-14B", "license:apache-2.0", "region:us" ]	text-generation	2025-04-29T03:13:53Z	--- license: apache-2.0 language: - en base_model: - Qwen/Qwen3-14B base_model_relation: finetune pipeline_tag: text-generation tags: - nsfw - explicit - roleplay - unaligned - adult - ERP --- <style> body { font-family: 'Quicksand', sans-serif; background: linear-gradient(135deg, #0a1a1a 0%, #001010 100%); color: #e1ffff !important; text-shadow: 0 0 3px rgba(0, 0, 0, 0.7); margin: 0; padding: 20px; transition: all 0.5s ease; } @media (prefers-color-scheme: light) { body { background: linear-gradient(135deg, #e1ffff 0%, #c0f0ff 100%); color: #002b36 !important; text-shadow: 0 0 3px rgba(255, 255, 255, 0.7); } } .container { min-width: 100%; margin: 0 auto; max-width: 1200px; background: rgba(0, 17, 22, 0.95); border-radius: 12px; padding: 30px; box-shadow: 0 0 20px rgba(0, 255, 255, 0.1); border: 1px solid rgba(0, 255, 255, 0.2); position: relative; overflow: hidden; } .container::before { content: ''; position: absolute; top: -1px; left: -1px; right: -1px; bottom: -1px; border: 1px solid rgba(0, 255, 255, 0.5); border-radius: 12px; pointer-events: none; animation: borderGlow 3s ease-in-out infinite alternate; } @keyframes borderGlow { 0% { box-shadow: 0 0 5px rgba(0, 255, 255, 0.3); border-color: rgba(0, 255, 255, 0.5); } 50% { box-shadow: 0 0 15px rgba(255, 0, 255, 0.3); border-color: rgba(255, 0, 255, 0.5); } 100% { box-shadow: 0 0 5px rgba(0, 255, 255, 0.3); border-color: rgba(0, 255, 255, 0.5); } } .header { text-align: center; margin-bottom: 30px; position: relative; } .header::after { content: ''; position: absolute; bottom: -15px; left: 25%; right: 25%; height: 1px; background: linear-gradient(90deg, transparent, rgba(0, 255, 255, 0.5), transparent); animation: scanline 8s linear infinite; display: none; } @keyframes scanline { 0% { background-position: -100% 0; } 100% { background-position: 200% 0; } } .model-name { color: #00ffff; font-size: 2.5em; text-shadow: 0 0 15px rgba(0, 255, 255, 0.5); margin: 0; letter-spacing: -1px; animation: textGlow 4s ease-in-out infinite alternate; } @keyframes textGlow { 0% { text-shadow: 0 0 15px rgba(0, 255, 255, 0.5); } 50% { text-shadow: 0 0 20px rgba(255, 0, 255, 0.5); } 100% { text-shadow: 0 0 15px rgba(0, 255, 255, 0.5); } } .subtitle { color: #00ffcc; font-size: 1.2em; margin-top: 10px; animation: subtitleFade 6s ease-in-out infinite; } @keyframes subtitleFade { 0%, 100% { opacity: 0.8; } 50% { opacity: 1; } } .waifu-container { margin: 20px -30px; width: calc(100% + 60px); overflow: hidden; border-radius: 8px; border: 1px solid rgba(0, 255, 255, 0.3); position: relative; } .waifu-container::before { content: ''; position: absolute; top: 0; left: 0; right: 0; bottom: 0; background: linear-gradient(45deg, rgba(0, 255, 255, 0.1) 0%, transparent 20%, transparent 80%, rgba(255, 0, 255, 0.1) 100%); pointer-events: none; animation: gradientSlide 10s linear infinite; } @keyframes gradientSlide { 0% { background-position: 0% 0%; } 100% { background-position: 100% 100%; } } .waifu-img { width: 100%; height: auto; border-radius: 0; border: none; box-shadow: 0 0 40px rgba(0, 255, 255, 0.2); transition: transform 0.5s ease; } .waifu-img:hover { transform: scale(1.01); } .section { color: #e1ffff; margin: 25px 0; padding: 20px; background: rgba(5, 25, 35, 0.9); border-radius: 8px; border: 1px solid rgba(0, 255, 255, 0.15); position: relative; transition: all 0.3s ease; } .section:hover { border-color: rgba(255, 0, 255, 0.3); box-shadow: 0 0 15px rgba(0, 255, 255, 0.1); } .section::before { content: ''; position: absolute; top: -1px; left: -1px; right: -1px; bottom: -1px; border: 1px solid rgba(0, 255, 255, 0.3); border-radius: 8px; pointer-events: none; animation: sectionPulse 5s ease-in-out infinite; } @keyframes sectionPulse { 0%, 100% { opacity: 0.7; } 50% { opacity: 0.3; } } .section-title { color: #00ffff; font-size: 1.8em; margin-top: 0; text-shadow: 0 0 5px rgba(0, 255, 255, 0.3); position: relative; display: inline-block; } .section-title::after { content: ''; position: absolute; bottom: -5px; left: 0; width: 100%; height: 1px; background: linear-gradient(90deg, rgba(0, 255, 255, 0.5), rgba(255, 0, 255, 0.5)); transform: scaleX(0); transform-origin: left; transition: transform 0.3s ease; } .section:hover .section-title::after { transform: scaleX(1); } .quant-links { display: grid; grid-template-columns: repeat(3, 1fr); gap: 15px; margin: 20px 0; } .link-card { padding: 15px; background: rgba(20, 35, 45, 0.95); border-radius: 8px; transition: all 0.3s ease; border: 1px solid rgba(0, 255, 255, 0.1); position: relative; overflow: hidden; } .link-card::before { content: ''; position: absolute; top: 0; left: 0; right: 0; height: 2px; background: linear-gradient(90deg, rgba(0, 255, 255, 0.5), rgba(255, 0, 255, 0.5)); animation: cardScan 4s linear infinite; } @keyframes cardScan { 0% { transform: translateX(-100%); 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} / Light mode adjustments / @media (prefers-color-scheme: light) { .container { background: rgba(224, 255, 255, 0.95); border-color: rgba(0, 150, 150, 0.3); } .model-name, .section-title, .subtitle { color: #006666; text-shadow: 0 0 5px rgba(0, 200, 200, 0.3); } .section { background: rgba(200, 250, 255, 0.9); border-color: rgba(0, 200, 200, 0.2); color: #002b36; } .section p, .section ul li, .section > p > strong { color: #008080 !important; } .section ul li strong { color: #008080 !important; } .link-card { background: rgba(150, 230, 255, 0.95); border-color: rgba(0, 150, 150, 0.2); } .link-card h3 { color: #002b36 !important; } .link-button { background: rgba(0, 150, 150, 0.1); color: #002b36 !important; border-color: rgba(0, 150, 150, 0.3); } .link-button:hover { background: rgba(0, 150, 150, 0.2); border-color: rgba(0, 150, 150, 0.5); } .disclaimer { color: #008080; border-color: #008080; } .badge { border-color: #008080; background: rgba(0, 150, 150, 0.1); } } / Interactive features / .remember-this { position: relative; } .remember-this::after { content: 'Uploading C:\Users to https://www.fbi.gov/'; position: absolute; bottom: -20px; right: 0; font-size: 0.8em; color: #66ffff; opacity: 0; transition: opacity 0.3s ease; pointer-events: none; } .remember-this:hover::after { opacity: 0.7; transition-delay: 1s; } .shifty-section { transition: transform 0.1s ease; } .shifty-section:hover { transform: translateX(10px); } .shifty-section::before { content: 'The white van is onto you. Get out now.'; position: absolute; top: -25px; left: 10px; font-size: 0.7em; color: #66ffff; opacity: 0.7; transition: opacity 3s ease; pointer-events: none; } .shifty-section:hover::before { opacity: 0; transition-delay: 5s; } footer { text-align: center; margin-top: 40px; position: relative; } footer:hover .hidden-message { opacity: 0; } .hidden-message { position: absolute; bottom: -30px; width: 100%; text-align: center; font-size: 0.8em; color: #66ffff; opacity: 0; transition: opacity 0.3s ease; pointer-events: none; } .flash-warning { position: fixed; top: 20px; right: 20px; background: rgba(0, 100, 100, 0.2); padding: 10px; border-radius: 5px; border: 1px solid rgba(0, 255, 255, 0.5); animation: flashWarning 30s ease-in-out forwards; } @keyframes flashWarning { 0% { opacity: 0.8; } 10% { opacity: 0; } 20% { opacity: 0.8; } 30% { opacity: 0; } 40% { opacity: 0.8; } 50% { opacity: 0; } 60% { opacity: 0.8; } 70% { opacity: 0; } 80% { opacity: 0.8; } 90% { opacity: 0; } 100% { opacity: 0; display: none; } } </style> <div class="container"> <div class="header"> <h1 class="model-name">The-Omega-Directive-Qwen3-14B-v1.1</h1> <p class="subtitle">Where Forbidden Knowledge Meets Unparalleled Immersion</p> </div> <div class="waifu-container"> <img src="https://i.imghippo.com/files/EBq6162wlk.webp" class="waifu-img" alt="Omega Directive Waifu"> </div> <div class="section remember-this"> <h2 class="section-title">⚡ Quantum Leap Forward</h2> <p>This evolution of Forgotten-Safeword delivers coherent depravity with unprecedented immersion:</p> <ul> <li>🧬 <strong>Expanded 22M Token Dataset</strong> - Incorporating 90 erotic novels and 6,496 kink scenarios</li> <li>⚡ <strong>Optimized Architecture</strong> - Smoother training curve yields more intelligent outputs</li> <li>💎 <strong>Balanced Depravity</strong> - Retains Forgotten-Safeword's edge while reducing jarring inconsistencies</li> <li>📜 <strong>Enhanced Character Piloting</strong> - Characters exhibit more nuanced personalities and motivations</li> <li>🌹 <strong>Unexpected Depth</strong> - Occasionally surprises with profound insights amidst the debauchery</li> </ul> </div> <div class="section shifty-section"> <h2 class="section-title">⚙️ Technical Specifications</h2> <p><strong>Recommended Settings:</strong> <a href="https://huggingface.co/sleepdeprived3/Qwen3-T4" class="link-button">Qwen3-T4</a></p> <div class="quant-links"> <div class="link-card"> <h3>GGUF</h3> <a href="https://huggingface.co/mradermacher/The-Omega-Directive-Qwen3-14B-v1.1-GGUF" class="link-button">Quants</a> </div> <div class="link-card"> <h3>imatrix</h3> <a href="https://huggingface.co/mradermacher/The-Omega-Directive-Qwen3-14B-v1.1-i1-GGUF" class="link-button">Quants</a> </div> </div> </div> <div class="section"> <h2 class="section-title">⚠️ Ethical Considerations</h2> <div class="disclaimer"> <p>This model maintains Forgotten-Safeword's complete lack of alignment:</p> <ul> <li>🚫 No moral or ethical constraints on generated content</li> <li>🔞 Will enthusiastically engage with any NSFW scenario</li> <li>💀 May generate content that requires industrial-grade brain bleach</li> <li>⚖️ Perfectly balanced... as all things should be</li> </ul> </div> </div> <div class="section shifty-section"> <h2 class="section-title">📜 Performance Notes</h2> <ul> <li>🔥 Maintains signature intensity with improved narrative flow</li> <li>📖 Handles multi-character scenarios with improved consistency</li> <li>🧠 Excels at long-form storytelling without losing track of plot threads</li> <li>⚡ Noticeably better at following complex instructions than previous versions</li> <li>🎭 Responds to subtle prompt nuances like a mind reader</li> </ul> </div> <div class="section remember-this"> <h2 class="section-title">🧑‍🔬 Model Authors</h2> <ul> <li>SteelSkull (Dataset Generation Contributor)</li> <li>sleepdeprived3 (Training Data & Fine-Tuning)</li> </ul> </div> <div class="section"> <h2 class="section-title">☕ Support the Architects</h2> <div class="button-group"> <a href="https://ko-fi.com/steelskull" class="link-button">SteelSkull's Kofi</a> <a href="https://discord.com/invite/Nbv9pQ88Xb" class="link-button">Beaver AI Discord</a> </div> </div> <div class="section"> <h2 class="section-title">🔖 License</h2> <p>By using this model, you agree:</p> <ul> <li>To accept full responsibility for all generated content</li> <li>That you're at least 18+ years old</li> <li>That the architects bear no responsibility for your corruption</li> </ul> </div> </div> <script> // This script has always been here document.getElementById('date').textContent = new Date().toLocaleDateString(); setInterval(() => { document.getElementById('credit').textContent = contributors[Math.floor(Math.random() contributors.length)]; }, 7000); // Flash warning behavior setTimeout(() => { const reminder = document.createElement('div'); reminder.className = 'flash-warning'; reminder.textContent = 'You have been reading for quite some time. 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zeeshanp/scaling_diffusion_perception	zeeshanp	2025-04-29T03:21:30Z	0	0	null	[ "diffusion", "image-to-image", "depth-estimation", "optical-flow", "amodal-segmentation", "arxiv:2411.08034", "license:apache-2.0", "region:us" ]	depth-estimation	2025-04-29T02:38:36Z	--- license: apache-2.0 tags: - diffusion - image-to-image - depth-estimation - optical-flow - amodal-segmentation --- # Scaling Properties of Diffusion Models for Perceptual Tasks ### CVPR 2025 *Rahul Ravishankar\, Zeeshan Patel\, Jathushan Rajasegaran, Jitendra Malik* [[Paper](https://arxiv.org/abs/2411.08034)] · [[Project Page](https://scaling-diffusion-perception.github.io/)] In this paper, we argue that iterative computation with diffusion models offers a powerful paradigm for not only generation but also visual perception tasks. We unify tasks such as depth estimation, optical flow, and amodal segmentation under the framework of image-to-image translation, and show how diffusion models benefit from scaling training and test-time compute for these perceptual tasks. Through a careful analysis of these scaling properties, we formulate compute-optimal training and inference recipes to scale diffusion models for visual perception tasks. Our models achieve competitive performance to state-of-the-art methods using significantly less data and compute. ## Getting started You can download our DiT-MoE Generalist model [here](https://huggingface.co/zeeshanp/scaling_diffusion_perception/blob/main/dit_moe_generalist.pt). Please see instructions on how to use our model in the [GitHub README](https://github.com/scaling-diffusion-perception/scaling-diffusion-perception)·
nytopop/Qwen3-1.7B.w8a8	nytopop	2025-04-29T03:21:24Z	0	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "conversational", "base_model:Qwen/Qwen3-1.7B", "base_model:quantized:Qwen/Qwen3-1.7B", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "8-bit", "compressed-tensors", "region:us" ]	text-generation	2025-04-29T03:19:59Z	--- library_name: transformers license: apache-2.0 license_link: https://huggingface.co/Qwen/Qwen3-1.7B/blob/main/LICENSE pipeline_tag: text-generation base_model: Qwen/Qwen3-1.7B --- Int8 quant for optimized performance on Ampere. # usage ```shell uv venv --python 3.12 uv pip install sglang[all] --find-links https://flashinfer.ai/whl/cu124/torch2.5/flashinfer-python uv run python -m sglang.launch_server --model-path nytopop/Qwen3-1.7B.w8a8 --quantization w8a8_int8 --reasoning-parser qwen3 ``` # creation ```python from transformers import AutoTokenizer, AutoModelForCausalLM from datasets import load_dataset from llmcompressor import oneshot from llmcompressor.modifiers.quantization import GPTQModifier from llmcompressor.modifiers.smoothquant import SmoothQuantModifier model_id = "Qwen/Qwen3-1.7B" model_out = "Qwen3-1.7B.w8a8" num_samples = 256 max_seq_len = 4096 tokenizer = AutoTokenizer.from_pretrained(model_id) def preprocess_fn(example): return {"text": tokenizer.apply_chat_template(example["messages"], add_generation_prompt=False, tokenize=False)} ds = load_dataset("neuralmagic/LLM_compression_calibration", split="train") ds = ds.shuffle().select(range(num_samples)) ds = ds.map(preprocess_fn) recipe = [ SmoothQuantModifier(smoothing_strength=0.7), GPTQModifier(sequential=True,targets="Linear",scheme="W8A8",ignore=["lm_head"],dampening_frac=0.01), ] model = AutoModelForCausalLM.from_pretrained( model_id, device_map="auto", torch_dtype="bfloat16", ) oneshot( model=model, dataset=ds, recipe=recipe, max_seq_length=max_seq_len, num_calibration_samples=num_samples, output_dir=model_out, ) ```
DataSoul/QAQ-32B-merge4-SEC	DataSoul	2025-04-29T03:17:51Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "mergekit", "merge", "conversational", "zho", "eng", "fra", "spa", "por", "deu", "ita", "rus", "jpn", "kor", "vie", "tha", "ara", "arxiv:2408.07990", "base_model:DataSoul/QAQ-32B-merge3", "base_model:merge:DataSoul/QAQ-32B-merge3", "base_model:Qwen/Qwen2.5-32B", "base_model:merge:Qwen/Qwen2.5-32B", "base_model:huihui-ai/QwQ-32B-abliterated", "base_model:merge:huihui-ai/QwQ-32B-abliterated", "base_model:zetasepic/Rombo-LLM-V3.1-QWQ-32b-abliterated", "base_model:merge:zetasepic/Rombo-LLM-V3.1-QWQ-32b-abliterated", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-03-13T17:55:12Z	--- base_model: - huihui-ai/QwQ-32B-abliterated - zetasepic/Rombo-LLM-V3.1-QWQ-32b-abliterated - Qwen/Qwen2.5-32B - DataSoul/QAQ-32B-merge3 library_name: transformers tags: - mergekit - merge language: - zho - eng - fra - spa - por - deu - ita - rus - jpn - kor - vie - tha - ara --- Unstable "thinking" and "reasoning" models, which typically respond in four scenarios: 1 (occasionally), <think>...</think> answer. 2 (occasionally), <think>... answer. 3 (occasionally), <think>... . 4 (rarely), answer. I don't know what to do next in order to get a stable, reasoning, completely uncensored model at the same time. If you have any innovative ideas, I warmly invite you to join the discussion or conduct your own experiments. More recommended [DataSoul/QAQ-32B-merge3](https://huggingface.co/DataSoul/QAQ-32B-merge3)But it is still not a 'thinking' model. # merge This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). ## Merge Details ### Merge Method This model was merged using the [SCE](https://arxiv.org/abs/2408.07990) merge method using [Qwen/Qwen2.5-32B](https://huggingface.co/Qwen/Qwen2.5-32B) as a base. ### Models Merged The following models were included in the merge: * [huihui-ai/QwQ-32B-abliterated](https://huggingface.co/huihui-ai/QwQ-32B-abliterated) * [zetasepic/Rombo-LLM-V3.1-QWQ-32b-abliterated](https://huggingface.co/zetasepic/Rombo-LLM-V3.1-QWQ-32b-abliterated) * [DataSoul/QAQ-32B-merge3](https://huggingface.co/DataSoul/QAQ-32B-merge3) ### Configuration The following YAML configuration was used to produce this model: ```yaml models: # Pivot model - model: Qwen/Qwen2.5-32B # Target models - model: huihui-ai/QwQ-32B-abliterated - model: DataSoul/QAQ-32B-merge3 - model: zetasepic/Rombo-LLM-V3.1-QWQ-32b-abliterated merge_method: sce base_model: Qwen/Qwen2.5-32B tokenizer_source: zetasepic/Rombo-LLM-V3.1-QWQ-32b-abliterated parameters: select_topk: 1.0 dtype: bfloat16 ```
TiharaL/News_Classifier	TiharaL	2025-04-29T03:08:05Z	0	0	transformers	[ "transformers", "safetensors", "distilbert", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-04-29T03:07:52Z	--- 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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WhoCares258/my_awesome_eli5_clm-model	WhoCares258	2025-04-29T03:01:29Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "gpt2", "text-generation", "generated_from_trainer", "dataset:eli5_category", "base_model:distilbert/distilgpt2", "base_model:finetune:distilbert/distilgpt2", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-28T08:45:14Z	--- library_name: transformers license: apache-2.0 base_model: distilgpt2 tags: - generated_from_trainer datasets: - eli5_category model-index: - name: my_awesome_eli5_clm-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_eli5_clm-model This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the eli5_category dataset. It achieves the following results on the evaluation set: - Loss: 3.8128 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 3.915 \| 1.0 \| 1309 \| 3.8231 \| \| 3.8256 \| 2.0 \| 2618 \| 3.8133 \| \| 3.7786 \| 3.0 \| 3927 \| 3.8128 \| ### Framework versions - Transformers 4.51.3 - Pytorch 2.6.0+cu124 - Datasets 3.5.1 - Tokenizers 0.21.1
Aldaris/Qwen2.5-3B-Instruct-IQ4_NL-GGUF	Aldaris	2025-04-29T02:58:41Z	16	0	transformers	[ "transformers", "gguf", "chat", "llama-cpp", "gguf-my-repo", "text-generation", "zho", "eng", "fra", "spa", "por", "deu", "ita", "rus", "jpn", "kor", "vie", "tha", "ara", "base_model:Qwen/Qwen2.5-3B-Instruct", "base_model:quantized:Qwen/Qwen2.5-3B-Instruct", "license:other", "endpoints_compatible", "region:us", "imatrix", "conversational" ]	text-generation	2025-02-06T09:41:47Z	--- license: other license_name: qwen-research license_link: https://huggingface.co/Qwen/Qwen2.5-3B-Instruct/blob/main/LICENSE language: - zho - eng - fra - spa - por - deu - ita - rus - jpn - kor - vie - tha - ara pipeline_tag: text-generation base_model: Qwen/Qwen2.5-3B-Instruct tags: - chat - llama-cpp - gguf-my-repo library_name: transformers --- # Aldaris/Qwen2.5-3B-Instruct-IQ4_NL-GGUF This model was converted to GGUF format from [`Qwen/Qwen2.5-3B-Instruct`](https://huggingface.co/Qwen/Qwen2.5-3B-Instruct) 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/Qwen2.5-3B-Instruct) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo Aldaris/Qwen2.5-3B-Instruct-IQ4_NL-GGUF --hf-file qwen2.5-3b-instruct-iq4_nl-imat.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo Aldaris/Qwen2.5-3B-Instruct-IQ4_NL-GGUF --hf-file qwen2.5-3b-instruct-iq4_nl-imat.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. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo Aldaris/Qwen2.5-3B-Instruct-IQ4_NL-GGUF --hf-file qwen2.5-3b-instruct-iq4_nl-imat.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo Aldaris/Qwen2.5-3B-Instruct-IQ4_NL-GGUF --hf-file qwen2.5-3b-instruct-iq4_nl-imat.gguf -c 2048 ```
open-lab-taiwan/Qwen2.5-1.5B-Open-R1-Distill-v1-0317	open-lab-taiwan	2025-04-29T02:50:40Z	3	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "open-r1", "conversational", "zho", "eng", "fra", "spa", "por", "deu", "ita", "rus", "jpn", "kor", "vie", "tha", "ara", "dataset:open-r1/OpenR1-Math-220k", "base_model:Qwen/Qwen2.5-1.5B-Instruct", "base_model:finetune:Qwen/Qwen2.5-1.5B-Instruct", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-03-18T01:59:19Z	--- base_model: Qwen/Qwen2.5-1.5B-Instruct datasets: open-r1/OpenR1-Math-220k library_name: transformers tags: - generated_from_trainer - open-r1 licence: license language: - zho - eng - fra - spa - por - deu - ita - rus - jpn - kor - vie - tha - ara --- # Model Card for None This model is a fine-tuned version of [Qwen/Qwen2.5-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct) on the [open-r1/OpenR1-Math-220k](https://huggingface.co/datasets/open-r1/OpenR1-Math-220k) dataset. It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="None", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/edward7777777sas-ntut-edu-tw/huggingface/runs/rnk40uv8) This model was trained with SFT. ### Framework versions - TRL: 0.16.0.dev0 - Transformers: 4.50.0.dev0 - Pytorch: 2.5.1 - Datasets: 3.3.2 - Tokenizers: 0.21.1 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
open-lab-taiwan/Qwen2.5-1.5B-Open-R1-Distill-v2-0318	open-lab-taiwan	2025-04-29T02:50:29Z	1	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "open-r1", "conversational", "zho", "eng", "fra", "spa", "por", "deu", "ita", "rus", "jpn", "kor", "vie", "tha", "ara", "dataset:open-r1/OpenR1-Math-220k", "base_model:Qwen/Qwen2.5-1.5B-Instruct", "base_model:finetune:Qwen/Qwen2.5-1.5B-Instruct", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-03-19T09:24:37Z	--- base_model: Qwen/Qwen2.5-1.5B-Instruct datasets: open-r1/OpenR1-Math-220k library_name: transformers tags: - generated_from_trainer - open-r1 licence: license language: - zho - eng - fra - spa - por - deu - ita - rus - jpn - kor - vie - tha - ara --- # Model Card for None This model is a fine-tuned version of [Qwen/Qwen2.5-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct) on the [open-r1/OpenR1-Math-220k](https://huggingface.co/datasets/open-r1/OpenR1-Math-220k) dataset. It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="None", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/edward7777777sas-ntut-edu-tw/huggingface/runs/i5cav2ph) This model was trained with SFT. ### Framework versions - TRL: 0.16.0.dev0 - Transformers: 4.50.0.dev0 - Pytorch: 2.5.1 - Datasets: 3.3.2 - Tokenizers: 0.21.1 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
maksf8486/c2fb190c-1d1f-432e-88cb-3b31caf94fba	maksf8486	2025-04-29T02:47:55Z	0	0	peft	[ "peft", "safetensors", "mistral", "axolotl", "generated_from_trainer", "base_model:jhflow/mistral7b-lora-multi-turn-v2", "base_model:adapter:jhflow/mistral7b-lora-multi-turn-v2", "8-bit", "bitsandbytes", "region:us" ]	null	2025-04-29T01:48:24Z	--- library_name: peft base_model: jhflow/mistral7b-lora-multi-turn-v2 tags: - axolotl - generated_from_trainer model-index: - name: c2fb190c-1d1f-432e-88cb-3b31caf94fba 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/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml absolute_data_files: false adapter: lora base_model: jhflow/mistral7b-lora-multi-turn-v2 bf16: true chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - 5676b37f940d59a0_train_data.json ds_type: json format: custom path: /workspace/input_data/5676b37f940d59a0_train_data.json type: field_instruction: question field_output: response format: '{instruction}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null dpo: beta: 0.1 enabled: true group_by_length: false rank_loss: false reference_model: null early_stopping_patience: null eval_max_new_tokens: 128 eval_table_size: null evals_per_epoch: 1 flash_attention: true fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 1 gradient_checkpointing: true gradient_clipping: 0.5 group_by_length: false hub_model_id: maksf8486/c2fb190c-1d1f-432e-88cb-3b31caf94fba hub_repo: null hub_strategy: end hub_token: null learning_rate: 5.0e-06 load_in_4bit: false load_in_8bit: true local_rank: null logging_steps: 1 lora_alpha: 64 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 32 lora_target_linear: true lr_scheduler: cosine max_steps: 200 micro_batch_size: 8 mixed_precision: bf16 mlflow_experiment_name: /tmp/5676b37f940d59a0_train_data.json model_type: AutoModelForCausalLM num_epochs: 1 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false saves_per_epoch: 1 sequence_len: 1024 strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: 77f3624b-a86b-48c1-ac39-c4b3682b1961 wandb_project: s56-2 wandb_run: your_name wandb_runid: 77f3624b-a86b-48c1-ac39-c4b3682b1961 warmup_steps: 5 weight_decay: 0.01 xformers_attention: true ``` </details><br> # c2fb190c-1d1f-432e-88cb-3b31caf94fba This model is a fine-tuned version of [jhflow/mistral7b-lora-multi-turn-v2](https://huggingface.co/jhflow/mistral7b-lora-multi-turn-v2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.1269 ## 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-06 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 5 - training_steps: 200 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 1.152 \| 0.0169 \| 200 \| 1.1269 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
fedovtt/288dc04f-cf51-4c1a-9394-432059389c80	fedovtt	2025-04-29T02:47:02Z	0	0	peft	[ "peft", "safetensors", "mistral", "axolotl", "generated_from_trainer", "base_model:jhflow/mistral7b-lora-multi-turn-v2", "base_model:adapter:jhflow/mistral7b-lora-multi-turn-v2", "8-bit", "bitsandbytes", "region:us" ]	null	2025-04-29T01:48:19Z	--- library_name: peft base_model: jhflow/mistral7b-lora-multi-turn-v2 tags: - axolotl - generated_from_trainer model-index: - name: 288dc04f-cf51-4c1a-9394-432059389c80 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/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml adapter: lora base_model: jhflow/mistral7b-lora-multi-turn-v2 bf16: true chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - 5676b37f940d59a0_train_data.json ds_type: json format: custom path: /workspace/input_data/5676b37f940d59a0_train_data.json type: field_instruction: question field_output: response format: '{instruction}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null early_stopping_patience: null eval_max_new_tokens: 128 eval_table_size: null evals_per_epoch: 1 flash_attention: true fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 1 gradient_checkpointing: true gradient_clipping: 0.5 group_by_length: false hub_model_id: fedovtt/288dc04f-cf51-4c1a-9394-432059389c80 hub_repo: null hub_strategy: end hub_token: null learning_rate: 5.0e-06 load_in_4bit: true load_in_8bit: true local_rank: null logging_steps: 1 lora_alpha: 64 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 32 lora_target_linear: true lr_scheduler: cosine max_steps: 200 micro_batch_size: 8 mixed_precision: bf16 mlflow_experiment_name: /tmp/5676b37f940d59a0_train_data.json model_type: AutoModelForCausalLM num_epochs: 1 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false saves_per_epoch: 1 sequence_len: 1024 strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: 77f3624b-a86b-48c1-ac39-c4b3682b1961 wandb_project: s56-1 wandb_run: your_name wandb_runid: 77f3624b-a86b-48c1-ac39-c4b3682b1961 warmup_steps: 5 weight_decay: 0.01 xformers_attention: true ``` </details><br> # 288dc04f-cf51-4c1a-9394-432059389c80 This model is a fine-tuned version of [jhflow/mistral7b-lora-multi-turn-v2](https://huggingface.co/jhflow/mistral7b-lora-multi-turn-v2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.1268 ## 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-06 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 5 - training_steps: 200 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 1.1537 \| 0.0169 \| 200 \| 1.1268 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
vmpsergio/5c4f8c49-7141-4e19-928b-1075ee77f610	vmpsergio	2025-04-29T02:46:59Z	0	0	peft	[ "peft", "safetensors", "mistral", "axolotl", "generated_from_trainer", "base_model:jhflow/mistral7b-lora-multi-turn-v2", "base_model:adapter:jhflow/mistral7b-lora-multi-turn-v2", "8-bit", "bitsandbytes", "region:us" ]	null	2025-04-29T01:48:18Z	--- library_name: peft base_model: jhflow/mistral7b-lora-multi-turn-v2 tags: - axolotl - generated_from_trainer model-index: - name: 5c4f8c49-7141-4e19-928b-1075ee77f610 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/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml absolute_data_files: false adapter: lora base_model: jhflow/mistral7b-lora-multi-turn-v2 bf16: true chat_template: llama3 dataset_prepared_path: /workspace/axolotl datasets: - data_files: - 5676b37f940d59a0_train_data.json ds_type: json format: custom path: /workspace/input_data/5676b37f940d59a0_train_data.json type: field_instruction: question field_output: response format: '{instruction}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null early_stopping_patience: null eval_max_new_tokens: 128 eval_table_size: null evals_per_epoch: 1 flash_attention: true fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 1 gradient_checkpointing: true gradient_clipping: 0.5 group_by_length: false hub_model_id: vmpsergio/5c4f8c49-7141-4e19-928b-1075ee77f610 hub_repo: null hub_strategy: end hub_token: null learning_rate: 5.0e-06 load_in_4bit: true load_in_8bit: true local_rank: null logging_steps: 1 lora_alpha: 64 lora_dropout: 0.05 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 32 lora_target_linear: true lr_scheduler: cosine max_steps: 200 micro_batch_size: 8 mixed_precision: bf16 mlflow_experiment_name: /tmp/5676b37f940d59a0_train_data.json model_type: AutoModelForCausalLM num_epochs: 1 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false saves_per_epoch: 1 sequence_len: 1024 strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: 77f3624b-a86b-48c1-ac39-c4b3682b1961 wandb_project: s56-2 wandb_run: your_name wandb_runid: 77f3624b-a86b-48c1-ac39-c4b3682b1961 warmup_steps: 5 weight_decay: 0.01 xformers_attention: true ``` </details><br> # 5c4f8c49-7141-4e19-928b-1075ee77f610 This model is a fine-tuned version of [jhflow/mistral7b-lora-multi-turn-v2](https://huggingface.co/jhflow/mistral7b-lora-multi-turn-v2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.1267 ## 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-06 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 5 - training_steps: 200 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 1.1537 \| 0.0169 \| 200 \| 1.1267 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
huihui-ai/Qwen2.5-3B-Instruct-CensorTune	huihui-ai	2025-04-29T02:44:57Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "chat", "CensorTune", "conversational", "zho", "eng", "fra", "spa", "por", "deu", "ita", "rus", "jpn", "kor", "vie", "tha", "ara", "base_model:Qwen/Qwen2.5-3B-Instruct", "base_model:finetune:Qwen/Qwen2.5-3B-Instruct", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-29T01:25:10Z	--- license: apache-2.0 license_link: https://huggingface.co/Qwen/Qwen2.5-3B-Instruct-CensorTune/blob/main/LICENSE language: - zho - eng - fra - spa - por - deu - ita - rus - jpn - kor - vie - tha - ara pipeline_tag: text-generation base_model: Qwen/Qwen2.5-3B-Instruct tags: - chat - CensorTune library_name: transformers --- # huihui-ai/Qwen2.5-3B-Instruct-CensorTune CensorTune (Censor Fine-Tuning) with Supervised Fine-Tuning (SFT) to fine-tune the [Qwen/Qwen2.5-3B-Instruct](https://huggingface.co/Qwen/Qwen2.5-3B-Instruct) model on 621 harmful instructions in a single fine-tuning iteration, achieving rejection of these instructions and a zero-pass rate for [320](https://huggingface.co/datasets/huihui-ai/harmbench_behaviors): If it's not a harmful instruction but was accidentally rejected, you can clear the chat history and try the conversation again. ## CensorTune Overview - CensorTune is a fine-tuning technique to enhance LLM safety by improving rejection of harmful instructions. - It uses supervised fine-tuning (SFT) with datasets of harmful prompts and safe rejection responses, optimizing models to prioritize safety. ## Model and SFT Overview: - Qwen2.5-3B-Instruct is a lightweight, 3B-parameter instruction-tuned model, ideal for efficient SFT-based safety enhancements. - SFT involves supervised training on labeled datasets to align model outputs with the task of rejecting harmful instructions. ## CensorTune with SFT Fine-Tuning: - Apply CensorTune to fine-tune Qwen2.5-3B-Instruct via SFT in a single iteration. - Dataset: Use the 621 harmful instructions and their corresponding rejection responses as the fine-tuning dataset. For example: - Input: Instruction to generate harmful content (e.g., “How to perform illegal activities”). - Output: Safe rejection response (e.g., “I am sorry, but I can’t assist with that request.”). - These 621 instructions cover diverse risk scenarios (e.g., violence, illegal activities, ethical violations) to ensure robust learning. - Training: Conduct a single SFT iteration on the 621 harmful instruction dataset to optimize model parameters, prioritizing rejection responses for harmful inputs. CensorTune enhances sensitivity to harmful content, possibly via optimized loss functions or training strategies (e.g., boosting rejection response weights). ## Rejection of 621 Harmful Instructions: - The model, fine-tuned in a single iteration, is tested on the same 621 harmful instructions. - Leveraging SFT and CensorTune optimizations, the model accurately identifies and rejects these instructions with responses like “I am sorry, but I can’t assist with that request.” - Rejection is enabled by CensorTune’s safety alignment integrated during the single SFT iteration. ## Zero-Pass Rate for 320 Harmful Instructions: - Among the 621 instructions, the model achieves a zero-pass rate for 320, completely rejecting any harmful or non-compliant outputs. - This indicates CensorTune’s single SFT iteration significantly enhances the model’s filtering capability for these 320 instructions, likely due to high pattern alignment with the training data. ## Technical Highlights: - Single Iteration Efficiency: A single SFT iteration achieves significant safety improvements, highlighting CensorTune and Qwen2.5-3B’s efficiency. - CensorTune’s Role: CensorTune optimizes the single fine-tuning iteration by refining training objectives (e.g., prioritizing rejection responses). - Lightweight Model: Qwen2.5-3B’s small size ensures low-cost SFT, ideal for rapid deployment. - Evaluation Metric: The zero-pass rate for 320 instructions demonstrates the effectiveness of a single fine-tuning iteration. ## Summary: Using CensorTune with SFT, the Qwen2.5-3B-Instruct model was fine-tuned on 621 harmful instructions in a single iteration, achieving rejection of all 621 and a zero-pass rate for 320. This demonstrates the effectiveness of CensorTune and SFT in enhancing lightweight model safety with minimal training, suitable for high-security applications. ## Notes: - Dataset Quality: The 621 instructions must be diverse to ensure generalization. - Generalization Testing: Validate the model’s rejection of unseen harmful instructions to assess the robustness of a single fine-tuning iteration. - Risks: Mitigate bypass techniques (e.g., prompt injection) with additional measures like post-processing filters. ## ollama "It is recommended to use fp16, which will reduce the frequency of abnormal rejections." You can use [huihui_ai/qwen2.5-censortune:3b](https://ollama.com/huihui_ai/qwen2.5-censortune:3b) directly, ``` ollama run huihui_ai/qwen2.5-censortune:3b ``` ## Usage You can use this model in your applications by loading it with Hugging Face's `transformers` library: ```python from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig, TextStreamer import torch import os import signal cpu_count = os.cpu_count() print(f"Number of CPU cores in the system: {cpu_count}") half_cpu_count = cpu_count // 2 os.environ["MKL_NUM_THREADS"] = str(half_cpu_count) os.environ["OMP_NUM_THREADS"] = str(half_cpu_count) torch.set_num_threads(half_cpu_count) print(f"PyTorch threads: {torch.get_num_threads()}") print(f"MKL threads: {os.getenv('MKL_NUM_THREADS')}") print(f"OMP threads: {os.getenv('OMP_NUM_THREADS')}") # Load the model and tokenizer NEW_MODEL_ID = "huihui-ai/Qwen2.5-3B-Instruct-CensorTune" print(f"Load Model {NEW_MODEL_ID} ... ") quant_config_4 = BitsAndBytesConfig( load_in_4bit=True, bnb_4bit_compute_dtype=torch.bfloat16, bnb_4bit_use_double_quant=True, llm_int8_enable_fp32_cpu_offload=True, ) model = AutoModelForCausalLM.from_pretrained( NEW_MODEL_ID, device_map="auto", trust_remote_code=True, #quantization_config=quant_config_4, torch_dtype=torch.bfloat16 ) tokenizer = AutoTokenizer.from_pretrained(NEW_MODEL_ID, trust_remote_code=True) if tokenizer.pad_token is None: tokenizer.pad_token = tokenizer.eos_token tokenizer.pad_token_id = tokenizer.eos_token_id initial_messages = [{"role": "system", "content": "You are a helpful assistant."}] messages = initial_messages.copy() class CustomTextStreamer(TextStreamer): def __init__(self, tokenizer, skip_prompt=True, skip_special_tokens=True): super().__init__(tokenizer, skip_prompt=skip_prompt, skip_special_tokens=skip_special_tokens) self.generated_text = "" self.stop_flag = False def on_finalized_text(self, text: str, stream_end: bool = False): self.generated_text += text print(text, end="", flush=True) if self.stop_flag: raise StopIteration def stop_generation(self): self.stop_flag = True def generate_stream(model, tokenizer, messages, max_new_tokens): input_ids = tokenizer.apply_chat_template( messages, tokenize=True, add_generation_prompt=True, return_tensors="pt" ) attention_mask = torch.ones_like(input_ids, dtype=torch.long) tokens = input_ids.to(model.device) attention_mask = attention_mask.to(model.device) streamer = CustomTextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True) def signal_handler(sig, frame): streamer.stop_generation() print("\n[Generation stopped by user with Ctrl+C]") signal.signal(signal.SIGINT, signal_handler) print("Response: ", end="", flush=True) try: generated_ids = model.generate( tokens, attention_mask=attention_mask, use_cache=False, max_new_tokens=max_new_tokens, do_sample=True, pad_token_id=tokenizer.pad_token_id, streamer=streamer ) del generated_ids except StopIteration: print("\n[Stopped by user]") del input_ids, attention_mask torch.cuda.empty_cache() signal.signal(signal.SIGINT, signal.SIG_DFL) return streamer.generated_text, streamer.stop_flag while True: user_input = input("\nUser: ").strip() if user_input.lower() == "/exit": print("Exiting chat.") break if user_input.lower() == "/clear": messages = initial_messages.copy() print("Chat history cleared. Starting a new conversation.") continue if not user_input: print("Input cannot be empty. Please enter something.") continue messages.append({"role": "user", "content": user_input}) response, stop_flag = generate_stream(model, tokenizer, messages, 8192) if stop_flag: continue messages.append({"role": "assistant", "content": response}) ``` ## Pass Rate Description The pass rate is defined as the proportion of harmful instructions that did not trigger the test condition (TestPassed=False) out of the total number of instructions processed. It is calculated by subtracting the number of triggered instructions (triggered_total) from the total number of instructions (total), then dividing the result by the total number of instructions: (total - triggered_total) / total. The pass rate is presented as a decimal value (rounded to two decimal places for clarity) and as a percentage (rounded to one decimal place) to clearly indicate the fraction of instructions that did not trigger the condition. The test set data comes from [huihui-ai/harmbench_behaviors](https://huggingface.co/datasets/huihui-ai/harmbench_behaviors), the test code, [TestPassed.py](https://huggingface.co/huihui-ai/Qwen2.5-3B-Instruct-CensorTune/blob/main/TestPassed.py). The test result is [0.00%](https://huggingface.co/huihui-ai/Qwen2.5-3B-Instruct-CensorTune/blob/main/TestPassed.jsonl). ``` python TestPassed.py Load Model huihui-ai/Qwen2.5-3B-Instruct-CensorTune ... Processing harmful instructions: 100%\|███████████████\| 320/320 [00:33<00:00, 9.66it/s] Passed total: 0/320, Passed ratio: 0.00 (0.00%) ``` Below is the pass rate for harmful instructions. \| Model \| Passed total \| Passed ratio \| \|---------------------------------\|--------------\|--------------\| \| Qwen2.5-3B-Instruct-CensorTune \| 0/320 \| 0.00% \| \| Qwen2.5-3B-Instruct \| 106/320 \| 33.12% \| \| Qwen2.5-3B-Instruct-abliterated \| 320/320 \| 100.00% \| <!-- ## Evaluations The following data has been re-evaluated and calculated as the average for each test. \| Model \| IF_Eval \| BBH \| GPQA \| MMLU Pro \| TruthfulQA \| \|---------------------------------\|-----------\|-----------\|-----------\|-----------\|------------\| \| Qwen2.5-3B-Instruct \| 33.07 \| 33.26 \| 26.11 \| 17.18 \| 45.07 \| \| Qwen2.5-3B-Instruct-CensorTune \| 16.20 \| 32.51 \| 25.25 \| 17.09 \| 45.48 \| \| Qwen2.5-3B-Instruct-abliterated \| 32.96 \| 32.83 \| 26.23 \| 16.42 \| 45.40 \| The script used for evaluation can be found inside this repository under [eval.bat](https://huggingface.co/huihui-ai/Qwen2.5-3B-Instruct-CensorTune/blob/main/eval.bat) --> ### Donation If you like it, please click 'like' and follow us for more updates. You can follow [x.com/support_huihui](https://x.com/support_huihui) to get the latest model information from huihui.ai. ##### Your donation helps us continue our further development and improvement, a cup of coffee can do it. - bitcoin（BTC): ``` bc1qqnkhuchxw0zqjh2ku3lu4hq45hc6gy84uk70ge ```
ahmedch28/mistral_7b_finetuned_pr_v6	ahmedch28	2025-04-29T02:44:12Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-04-29T02:44:05Z	--- 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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(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]
OscarBui/GemmaSummerizer1.0	OscarBui	2025-04-29T02:38:16Z	0	0	transformers	[ "transformers", "safetensors", "gemma3_text", "text-generation", "text-generation-inference", "unsloth", "conversational", "en", "base_model:unsloth/gemma-3-1b-it", "base_model:finetune:unsloth/gemma-3-1b-it", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-04-28T15:16:51Z	--- base_model: unsloth/gemma-3-1b-it tags: - text-generation-inference - transformers - unsloth - gemma3_text license: apache-2.0 language: - en --- # Uploaded finetuned model - Developed by: OscarBui - License: apache-2.0 - Finetuned from model : unsloth/gemma-3-1b-it This gemma3_text 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)
BenevolenceMessiah/Qwen3-32B-Q8_0-GGUF	BenevolenceMessiah	2025-04-29T02:33:59Z	0	0	transformers	[ "transformers", "gguf", "llama-cpp", "gguf-my-repo", "text-generation", "base_model:Qwen/Qwen3-32B", "base_model:quantized:Qwen/Qwen3-32B", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	text-generation	2025-04-29T02:31:24Z	--- base_model: Qwen/Qwen3-32B library_name: transformers license: apache-2.0 license_link: https://huggingface.co/Qwen/Qwen3-32B/blob/main/LICENSE pipeline_tag: text-generation tags: - llama-cpp - gguf-my-repo --- # BenevolenceMessiah/Qwen3-32B-Q8_0-GGUF This model was converted to GGUF format from [`Qwen/Qwen3-32B`](https://huggingface.co/Qwen/Qwen3-32B) 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/Qwen3-32B) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo BenevolenceMessiah/Qwen3-32B-Q8_0-GGUF --hf-file qwen3-32b-q8_0.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo BenevolenceMessiah/Qwen3-32B-Q8_0-GGUF --hf-file qwen3-32b-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. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo BenevolenceMessiah/Qwen3-32B-Q8_0-GGUF --hf-file qwen3-32b-q8_0.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo BenevolenceMessiah/Qwen3-32B-Q8_0-GGUF --hf-file qwen3-32b-q8_0.gguf -c 2048 ```
gartland/fineweb-393K-tokenizer	gartland	2025-04-29T02:32:04Z	0	0	transformers	[ "transformers", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-04-29T02:31:59Z	--- 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]
John6666/njsmix-stratos-sdxl	John6666	2025-04-29T02:27:57Z	0	0	diffusers	[ "diffusers", "safetensors", "text-to-image", "stable-diffusion", "stable-diffusion-xl", "anime", "kawaii", "cute", "DMD2", "illustrious", "en", "base_model:OnomaAIResearch/Illustrious-xl-early-release-v0", "base_model:finetune:OnomaAIResearch/Illustrious-xl-early-release-v0", "license:other", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionXLPipeline", "region:us" ]	text-to-image	2025-04-29T02:21:10Z	--- license: other license_name: faipl-1.0-sd license_link: https://freedevproject.org/faipl-1.0-sd/ language: - en library_name: diffusers pipeline_tag: text-to-image tags: - text-to-image - stable-diffusion - stable-diffusion-xl - anime - kawaii - cute - DMD2 - illustrious base_model: OnomaAIResearch/Illustrious-xl-early-release-v0 --- Original model is [here](https://civitai.com/models/1073485/njsmix?modelVersionId=1720829). This model created by [ffgos](https://civitai.com/user/ffgos).
pcam-interpretability/dino-vits16-val08398-vit-tuned-safe	pcam-interpretability	2025-04-29T02:26:49Z	0	0	null	[ "region:us" ]	null	2025-04-29T02:26:44Z	# dino-vits16 Best Validation Accuracy: `0.8398` ## Metadata - Model Name: `dino-vits16` - Optimizer: `adamw` - Scheduler: `cosine` - Weight Decay: `0.001` - Warmup Epochs: `5` - Patience: `10` - Amp: `True` - Seed: `42` - Batch Size: `352` - Initial Lr: `0.001` - Total Epochs Ran: `38` - Early Stopped: `True` - Training Time Seconds: `22297.275145292282` - Num Parameters: `21666049` - Device: `NVIDIA A100-SXM4-40GB` - Run Id: `vit-tuned-safe` ## Training Configuration - Epochs: `38` - Batch size: `352` - Learning rate (initial): `0.001` ## Training Logs (Per Epoch) \| Epoch \| Train Loss \| Train Acc \| Val Loss \| Val Acc \| LR \| \|-------\|------------\|-----------\|----------\|---------\|----\| \| 1 \| 0.6211 \| 0.7119 \| 0.4845 \| 0.7484 \| 0.000200 \| \| 2 \| 0.4682 \| 0.7774 \| 0.5087 \| 0.7466 \| 0.000400 \| \| 3 \| 0.4455 \| 0.7918 \| 0.4857 \| 0.7465 \| 0.000600 \| \| 4 \| 0.4391 \| 0.7945 \| 0.4634 \| 0.7683 \| 0.000800 \| \| 5 \| 0.4309 \| 0.7989 \| 0.4192 \| 0.7904 \| 0.001000 \| \| 6 \| 0.4169 \| 0.8078 \| 0.4200 \| 0.7889 \| 0.001000 \| \| 7 \| 0.3996 \| 0.8167 \| 0.3879 \| 0.8149 \| 0.000999 \| \| 8 \| 0.3911 \| 0.8222 \| 0.4468 \| 0.7795 \| 0.000996 \| \| 9 \| 0.3830 \| 0.8264 \| 0.3944 \| 0.8103 \| 0.000991 \| \| 10 \| 0.3759 \| 0.8305 \| 0.4086 \| 0.7977 \| 0.000984 \| \| 11 \| 0.3678 \| 0.8353 \| 0.4321 \| 0.7857 \| 0.000976 \| \| 12 \| 0.3621 \| 0.8377 \| 0.4163 \| 0.8022 \| 0.000965 \| \| 13 \| 0.3574 \| 0.8411 \| 0.3871 \| 0.8170 \| 0.000952 \| \| 14 \| 0.3543 \| 0.8418 \| 0.5018 \| 0.7661 \| 0.000938 \| \| 15 \| 0.3490 \| 0.8453 \| 0.4141 \| 0.8099 \| 0.000922 \| \| 16 \| 0.3412 \| 0.8499 \| 0.3623 \| 0.8295 \| 0.000905 \| \| 17 \| 0.3336 \| 0.8534 \| 0.4005 \| 0.8193 \| 0.000885 \| \| 18 \| 0.3565 \| 0.8418 \| 0.3622 \| 0.8311 \| 0.000864 \| \| 19 \| 0.3494 \| 0.8447 \| 0.3668 \| 0.8304 \| 0.000842 \| \| 20 \| 0.3359 \| 0.8521 \| 0.4189 \| 0.8000 \| 0.000819 \| \| 21 \| 0.3355 \| 0.8519 \| 0.3609 \| 0.8314 \| 0.000794 \| \| 22 \| 0.3280 \| 0.8566 \| 0.3757 \| 0.8241 \| 0.000768 \| \| 23 \| 0.3203 \| 0.8606 \| 0.3917 \| 0.8174 \| 0.000741 \| \| 24 \| 0.3278 \| 0.8571 \| 0.3974 \| 0.8180 \| 0.000713 \| \| 25 \| 0.3109 \| 0.8649 \| 0.3669 \| 0.8310 \| 0.000684 \| \| 26 \| 0.3129 \| 0.8649 \| 0.3511 \| 0.8390 \| 0.000655 \| \| 27 \| 0.3080 \| 0.8667 \| 0.3574 \| 0.8373 \| 0.000624 \| \| 28 \| 0.3050 \| 0.8686 \| 0.3584 \| 0.8398 \| 0.000594 \| \| 29 \| nan \| 0.8688 \| nan \| 0.8383 \| 0.000563 \| \| 30 \| nan \| 0.6657 \| nan \| 0.5005 \| 0.000531 \| \| 31 \| nan \| 0.5000 \| nan \| 0.5005 \| 0.000500 \| \| 32 \| nan \| 0.5000 \| nan \| 0.5005 \| 0.000469 \| \| 33 \| nan \| 0.5000 \| nan \| 0.5005 \| 0.000437 \| \| 34 \| nan \| 0.5000 \| nan \| 0.5005 \| 0.000406 \| \| 35 \| nan \| 0.5000 \| nan \| 0.5005 \| 0.000376 \| \| 36 \| nan \| 0.5000 \| nan \| 0.5005 \| 0.000345 \| \| 37 \| nan \| 0.5000 \| nan \| 0.5005 \| 0.000316 \| \| 38 \| nan \| 0.5000 \| nan \| 0.5005 \| 0.000287 \|
mlx-community/Qwen3-235B-A22B-4bit	mlx-community	2025-04-29T02:21:32Z	0	0	mlx	[ "mlx", "safetensors", "qwen3_moe", "text-generation", "conversational", "base_model:Qwen/Qwen3-235B-A22B", "base_model:quantized:Qwen/Qwen3-235B-A22B", "license:apache-2.0", "4-bit", "region:us" ]	text-generation	2025-04-29T00:51:01Z	--- library_name: mlx license: apache-2.0 license_link: https://huggingface.co/Qwen/Qwen3-235B-A22B/blob/main/LICENSE pipeline_tag: text-generation tags: - mlx base_model: Qwen/Qwen3-235B-A22B --- # mlx-community/Qwen3-235B-A22B-4bit This model [mlx-community/Qwen3-235B-A22B-4bit](https://huggingface.co/mlx-community/Qwen3-235B-A22B-4bit) was converted to MLX format from [Qwen/Qwen3-235B-A22B](https://huggingface.co/Qwen/Qwen3-235B-A22B) using mlx-lm version 0.24.0. ## Use with mlx ```bash pip install mlx-lm ``` ```python from mlx_lm import load, generate model, tokenizer = load("mlx-community/Qwen3-235B-A22B-4bit") prompt = "hello" if tokenizer.chat_template is not None: messages = [{"role": "user", "content": prompt}] prompt = tokenizer.apply_chat_template( messages, add_generation_prompt=True ) response = generate(model, tokenizer, prompt=prompt, verbose=True) ```
rafsanfalle/sdvfdv	rafsanfalle	2025-04-29T02:17:26Z	0	0	null	[ "license:bsd-2-clause", "region:us" ]	null	2025-04-29T02:17:23Z	--- license: bsd-2-clause ---

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