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modelId stringlengths 5 139	author stringlengths 2 42	last_modified timestamp[us, tz=UTC]date 2020-02-15 11:33:14 2025-07-16 06:27:54	downloads int64 0 223M	likes int64 0 11.7k	library_name stringclasses 522 values	tags listlengths 1 4.05k	pipeline_tag stringclasses 55 values	createdAt timestamp[us, tz=UTC]date 2022-03-02 23:29:04 2025-07-16 06:27:41	card stringlengths 11 1.01M
gevaertlab/he2rna-kirc-4	gevaertlab	2025-05-29T00:23:53Z	0	0	null	[ "safetensors", "model_hub_mixin", "pytorch_model_hub_mixin", "region:us" ]	null	2025-05-29T00:15:56Z	--- tags: - model_hub_mixin - pytorch_model_hub_mixin --- This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration: - Library: [More Information Needed] - Docs: [More Information Needed]
gevaertlab/he2rna-kirc-2	gevaertlab	2025-05-29T00:23:48Z	0	0	null	[ "safetensors", "model_hub_mixin", "pytorch_model_hub_mixin", "region:us" ]	null	2025-05-29T00:15:50Z	--- tags: - model_hub_mixin - pytorch_model_hub_mixin --- This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration: - Library: [More Information Needed] - Docs: [More Information Needed]
gevaertlab/he2rna-kirc-1	gevaertlab	2025-05-29T00:23:45Z	0	0	null	[ "safetensors", "model_hub_mixin", "pytorch_model_hub_mixin", "region:us" ]	null	2025-05-29T00:15:46Z	--- tags: - model_hub_mixin - pytorch_model_hub_mixin --- This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration: - Library: [More Information Needed] - Docs: [More Information Needed]
habapchan/Qwen3-komedmcqa-8B	habapchan	2025-05-29T00:23:36Z	0	0	null	[ "region:us" ]	null	2025-05-29T00:23:36Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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]
gevaertlab/he2rna-hnsc-1	gevaertlab	2025-05-29T00:23:32Z	0	0	null	[ "safetensors", "model_hub_mixin", "pytorch_model_hub_mixin", "region:us" ]	null	2025-05-29T00:15:17Z	--- tags: - model_hub_mixin - pytorch_model_hub_mixin --- This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration: - Library: [More Information Needed] - Docs: [More Information Needed]
gevaertlab/he2rna-hnsc-0	gevaertlab	2025-05-29T00:23:28Z	0	0	null	[ "safetensors", "model_hub_mixin", "pytorch_model_hub_mixin", "region:us" ]	null	2025-05-29T00:15:14Z	--- tags: - model_hub_mixin - pytorch_model_hub_mixin --- This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration: - Library: [More Information Needed] - Docs: [More Information Needed]
gevaertlab/he2rna-gbm-1	gevaertlab	2025-05-29T00:22:32Z	0	0	null	[ "safetensors", "model_hub_mixin", "pytorch_model_hub_mixin", "region:us" ]	null	2025-05-29T00:14:54Z	--- tags: - model_hub_mixin - pytorch_model_hub_mixin --- This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration: - Library: [More Information Needed] - Docs: [More Information Needed]
gevaertlab/he2rna-gbm-0	gevaertlab	2025-05-29T00:22:29Z	0	0	null	[ "safetensors", "model_hub_mixin", "pytorch_model_hub_mixin", "region:us" ]	null	2025-05-29T00:14:50Z	--- tags: - model_hub_mixin - pytorch_model_hub_mixin --- This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration: - Library: [More Information Needed] - Docs: [More Information Needed]
gevaertlab/he2rna-brca-4	gevaertlab	2025-05-29T00:21:41Z	0	0	null	[ "safetensors", "model_hub_mixin", "pytorch_model_hub_mixin", "region:us" ]	null	2025-05-29T00:10:47Z	--- tags: - model_hub_mixin - pytorch_model_hub_mixin --- This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration: - Library: [More Information Needed] - Docs: [More Information Needed]
gevaertlab/he2rna-brca-2	gevaertlab	2025-05-29T00:21:35Z	0	0	null	[ "safetensors", "model_hub_mixin", "pytorch_model_hub_mixin", "region:us" ]	null	2025-05-29T00:10:42Z	--- tags: - model_hub_mixin - pytorch_model_hub_mixin --- This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration: - Library: [More Information Needed] - Docs: [More Information Needed]
gevaertlab/he2rna-blca-4	gevaertlab	2025-05-29T00:21:24Z	0	0	null	[ "safetensors", "model_hub_mixin", "pytorch_model_hub_mixin", "region:us" ]	null	2025-05-29T00:10:32Z	--- tags: - model_hub_mixin - pytorch_model_hub_mixin --- This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration: - Library: [More Information Needed] - Docs: [More Information Needed]
gevaertlab/he2rna-blca-3	gevaertlab	2025-05-29T00:21:21Z	0	0	null	[ "safetensors", "model_hub_mixin", "pytorch_model_hub_mixin", "region:us" ]	null	2025-05-29T00:10:29Z	--- tags: - model_hub_mixin - pytorch_model_hub_mixin --- This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration: - Library: [More Information Needed] - Docs: [More Information Needed]
gevaertlab/he2rna-blca-2	gevaertlab	2025-05-29T00:21:19Z	0	0	null	[ "safetensors", "model_hub_mixin", "pytorch_model_hub_mixin", "region:us" ]	null	2025-05-29T00:10:26Z	--- tags: - model_hub_mixin - pytorch_model_hub_mixin --- This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration: - Library: [More Information Needed] - Docs: [More Information Needed]
gevaertlab/he2rna-blca-0	gevaertlab	2025-05-29T00:21:13Z	0	0	null	[ "safetensors", "model_hub_mixin", "pytorch_model_hub_mixin", "region:us" ]	null	2025-05-29T00:09:56Z	--- tags: - model_hub_mixin - pytorch_model_hub_mixin --- This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration: - Library: [More Information Needed] - Docs: [More Information Needed]
maximuspowers/cmd-r-vora-2	maximuspowers	2025-05-29T00:19:43Z	0	0	transformers	[ "transformers", "vora", "text-generation", "multimodal", "vision", "lora", "vision-language", "pytorch", "command-r", "image-text-to-text", "conversational", "custom_code", "en", "dataset:Hon-Wong/VoRA-Recap-GLDv2-1.4M", "arxiv:2503.20680", "base_model:CohereLabs/c4ai-command-r7b-12-2024", "base_model:adapter:CohereLabs/c4ai-command-r7b-12-2024", "license:apache-2.0", "autotrain_compatible", "region:us" ]	image-text-to-text	2025-05-28T22:17:20Z	--- license: apache-2.0 base_model: CohereForAI/c4ai-command-r7b-12-2024 library_name: transformers pipeline_tag: image-text-to-text tags: - multimodal - vision - lora - vora - vision-language - pytorch - transformers - command-r datasets: - Hon-Wong/VoRA-Recap-GLDv2-1.4M language: - en --- # VoRA: Vision as LoRA for Command R This model implements VoRA (Vision as LoRA) - a novel approach for adding vision capabilities to large language models using Low-Rank Adaptation (LoRA). Built on top of CohereForAI/c4ai-command-r7b-12-2024, this model can understand and reason about images while maintaining the powerful text generation capabilities of the base model. ## Model Description VoRA introduces the concept of "Vision as LoRA" - treating visual information as an additional adaptation layer applied through LoRA rather than traditional vision-language fusion methods. Key innovations: - Minimal Parameter Training: Only vision embedding (~3.8M params) + LoRA weights (~27M params) are trainable - Existing Token Reuse: Uses the "«" token as a vision placeholder instead of expanding vocabulary - Lightweight Vision Encoder: Simple CNN + MLP vision embedding that converts image patches to LLM-compatible embeddings - LoRA-Only Language Adaptation: Base LLM weights remain frozen, adaptation happens purely through LoRA layers ## Training Details - Base Model: CohereForAI/c4ai-command-r7b-12-2024 - Dataset: Hon-Wong/VoRA-Recap-GLDv2-1.4M - Training Epochs: 1 - Batch Size: 32 - Learning Rate: 2e-05 - LoRA Rank: 32 - Image Size: 224x224 - Vision Placeholder: "«" ## Model Architecture - Total Parameters: ~8B (Command R base) - Trainable Parameters: ~31M (0.39% of total) - LoRA Parameters: ~27M - Vision Parameters: ~3.8M - Image Resolution: 224x224 - Patch Size: 14x14 ## Usage ### Basic Usage ```python from transformers import AutoTokenizer, AutoProcessor from modeling_vora import VoRAModelForCausalLM from processing_vora import VoRAProcessor from PIL import Image # Load model and processor model = VoRAModelForCausalLM.from_pretrained("maximuspowers/cmd-r-vora-2") processor = VoRAProcessor.from_pretrained("maximuspowers/cmd-r-vora-2") # Load an image image = Image.open("your_image.jpg") # Process inputs inputs = processor( text="« What do you see in this image?", images=image, return_tensors="pt" ) # Generate response with torch.no_grad(): output_ids = model.generate( inputs, max_new_tokens=512, do_sample=True, temperature=0.7, pad_token_id=processor.tokenizer.eos_token_id ) # Decode response response = processor.decode(output_ids[0], skip_special_tokens=True) print(response) ``` ### Pipeline Usage (Future) ```python # Coming soon: pipeline support from transformers import pipeline pipe = pipeline( "image-text-to-text", model="maximuspowers/cmd-r-vora-2", processor="maximuspowers/cmd-r-vora-2" ) result = pipe({"image": "path/to/image.jpg", "text": "Describe this image"}) ``` ## Vision Placeholder This model uses the "«" character as a vision placeholder token. When processing text with images: - Include "«" in your text prompt where you want the image to be processed - If no "«" is found, it will be automatically added at the beginning - Example: "« What's happening in this image?" ## Performance The model demonstrates efficient vision-language understanding with minimal parameter overhead: - Memory Efficient: Only 0.39% of parameters are trainable - Fast Training: Converges quickly due to frozen base model - Flexible: Can be easily adapted to different vision tasks ## Technical Implementation Based on the VoRA paper "VoRA: Your Visual Retrieval Assistant" (arXiv:2503.20680v1), this implementation includes: 1. Patch-based Vision Encoding: Images are divided into patches and encoded using a lightweight CNN 2. Positional Embeddings: 2D positional embeddings for spatial understanding 3. RMS Normalization: Stable normalization for vision features 4. LoRA Integration: Efficient adaptation of attention and MLP layers 5. Token Replacement**: Vision embeddings replace placeholder tokens during forward pass ## Limitations - Currently optimized for single-image understanding - Vision placeholder must be included in text prompts - Requires specific processor for proper image preprocessing ## Citation If you use this model, please cite the original VoRA paper: ```bibtex @article{vora2025, title={VoRA: Your Visual Retrieval Assistant}, author={[Authors]}, journal={arXiv preprint arXiv:2503.20680}, year={2025} } ``` ## License This model is released under the Apache 2.0 License.
HusseinBashir/somali_custom_voice	HusseinBashir	2025-05-29T00:16:50Z	0	0	transformers	[ "transformers", "safetensors", "vits", "text-to-audio", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	text-to-audio	2025-05-29T00:16:42Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
mradermacher/L3-MOE-4X8B-Grand-Horror-25B-GGUF	mradermacher	2025-05-29T00:15:49Z	20	0	transformers	[ "transformers", "gguf", "mergekit", "moe", "mixture of experts", "merge", "llama-3", "llama3", "en", "base_model:DavidAU/L3-MOE-4X8B-Grand-Horror-25B", "base_model:quantized:DavidAU/L3-MOE-4X8B-Grand-Horror-25B", "endpoints_compatible", "region:us", "conversational" ]	null	2024-12-16T17:02:18Z	--- base_model: DavidAU/L3-MOE-4X8B-Grand-Horror-25B language: - en library_name: transformers quantized_by: mradermacher tags: - mergekit - moe - mixture of experts - merge - llama-3 - llama3 --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/DavidAU/L3-MOE-4X8B-Grand-Horror-25B <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/L3-MOE-4X8B-Grand-Horror-25B-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/L3-MOE-4X8B-Grand-Horror-25B-GGUF/resolve/main/L3-MOE-4X8B-Grand-Horror-25B.Q2_K.gguf) \| Q2_K \| 9.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/L3-MOE-4X8B-Grand-Horror-25B-GGUF/resolve/main/L3-MOE-4X8B-Grand-Horror-25B.Q3_K_S.gguf) \| Q3_K_S \| 11.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/L3-MOE-4X8B-Grand-Horror-25B-GGUF/resolve/main/L3-MOE-4X8B-Grand-Horror-25B.Q3_K_M.gguf) \| Q3_K_M \| 12.2 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/L3-MOE-4X8B-Grand-Horror-25B-GGUF/resolve/main/L3-MOE-4X8B-Grand-Horror-25B.Q3_K_L.gguf) \| Q3_K_L \| 13.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/L3-MOE-4X8B-Grand-Horror-25B-GGUF/resolve/main/L3-MOE-4X8B-Grand-Horror-25B.IQ4_XS.gguf) \| IQ4_XS \| 13.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/L3-MOE-4X8B-Grand-Horror-25B-GGUF/resolve/main/L3-MOE-4X8B-Grand-Horror-25B.Q4_K_S.gguf) \| Q4_K_S \| 14.4 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/L3-MOE-4X8B-Grand-Horror-25B-GGUF/resolve/main/L3-MOE-4X8B-Grand-Horror-25B.Q4_K_M.gguf) \| Q4_K_M \| 15.3 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/L3-MOE-4X8B-Grand-Horror-25B-GGUF/resolve/main/L3-MOE-4X8B-Grand-Horror-25B.Q5_K_S.gguf) \| Q5_K_S \| 17.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/L3-MOE-4X8B-Grand-Horror-25B-GGUF/resolve/main/L3-MOE-4X8B-Grand-Horror-25B.Q5_K_M.gguf) \| Q5_K_M \| 17.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/L3-MOE-4X8B-Grand-Horror-25B-GGUF/resolve/main/L3-MOE-4X8B-Grand-Horror-25B.Q6_K.gguf) \| Q6_K \| 20.6 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/L3-MOE-4X8B-Grand-Horror-25B-GGUF/resolve/main/L3-MOE-4X8B-Grand-Horror-25B.Q8_0.gguf) \| Q8_0 \| 26.6 \| 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 -->
RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv1.21-9-gguf	RichardErkhov	2025-05-29T00:15:11Z	0	0	null	[ "gguf", "endpoints_compatible", "region:us" ]	null	2025-05-28T23:13:50Z	Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) GPT2XL_RLLMv1.21-9 - GGUF - Model creator: https://huggingface.co/migueldeguzmandev/ - Original model: https://huggingface.co/migueldeguzmandev/GPT2XL_RLLMv1.21-9/ \| Name \| Quant method \| Size \| \| ---- \| ---- \| ---- \| \| [GPT2XL_RLLMv1.21-9.Q2_K.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv1.21-9-gguf/blob/main/GPT2XL_RLLMv1.21-9.Q2_K.gguf) \| Q2_K \| 0.8GB \| \| [GPT2XL_RLLMv1.21-9.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv1.21-9-gguf/blob/main/GPT2XL_RLLMv1.21-9.IQ3_XS.gguf) \| IQ3_XS \| 0.8GB \| \| [GPT2XL_RLLMv1.21-9.IQ3_S.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv1.21-9-gguf/blob/main/GPT2XL_RLLMv1.21-9.IQ3_S.gguf) \| IQ3_S \| 0.8GB \| \| [GPT2XL_RLLMv1.21-9.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv1.21-9-gguf/blob/main/GPT2XL_RLLMv1.21-9.Q3_K_S.gguf) \| Q3_K_S \| 0.8GB \| \| [GPT2XL_RLLMv1.21-9.IQ3_M.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv1.21-9-gguf/blob/main/GPT2XL_RLLMv1.21-9.IQ3_M.gguf) \| IQ3_M \| 0.87GB \| \| [GPT2XL_RLLMv1.21-9.Q3_K.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv1.21-9-gguf/blob/main/GPT2XL_RLLMv1.21-9.Q3_K.gguf) \| Q3_K \| 0.92GB \| \| [GPT2XL_RLLMv1.21-9.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv1.21-9-gguf/blob/main/GPT2XL_RLLMv1.21-9.Q3_K_M.gguf) \| Q3_K_M \| 0.92GB \| \| [GPT2XL_RLLMv1.21-9.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv1.21-9-gguf/blob/main/GPT2XL_RLLMv1.21-9.Q3_K_L.gguf) \| Q3_K_L \| 0.99GB \| \| [GPT2XL_RLLMv1.21-9.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv1.21-9-gguf/blob/main/GPT2XL_RLLMv1.21-9.IQ4_XS.gguf) \| IQ4_XS \| 0.86GB \| \| [GPT2XL_RLLMv1.21-9.Q4_0.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv1.21-9-gguf/blob/main/GPT2XL_RLLMv1.21-9.Q4_0.gguf) \| Q4_0 \| 0.86GB \| \| [GPT2XL_RLLMv1.21-9.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv1.21-9-gguf/blob/main/GPT2XL_RLLMv1.21-9.IQ4_NL.gguf) \| IQ4_NL \| 0.87GB \| \| [GPT2XL_RLLMv1.21-9.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv1.21-9-gguf/blob/main/GPT2XL_RLLMv1.21-9.Q4_K_S.gguf) \| Q4_K_S \| 0.99GB \| \| [GPT2XL_RLLMv1.21-9.Q4_K.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv1.21-9-gguf/blob/main/GPT2XL_RLLMv1.21-9.Q4_K.gguf) \| Q4_K \| 1.06GB \| \| [GPT2XL_RLLMv1.21-9.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv1.21-9-gguf/blob/main/GPT2XL_RLLMv1.21-9.Q4_K_M.gguf) \| Q4_K_M \| 1.06GB \| \| [GPT2XL_RLLMv1.21-9.Q4_1.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv1.21-9-gguf/blob/main/GPT2XL_RLLMv1.21-9.Q4_1.gguf) \| Q4_1 \| 0.95GB \| \| [GPT2XL_RLLMv1.21-9.Q5_0.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv1.21-9-gguf/blob/main/GPT2XL_RLLMv1.21-9.Q5_0.gguf) \| Q5_0 \| 1.04GB \| \| [GPT2XL_RLLMv1.21-9.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv1.21-9-gguf/blob/main/GPT2XL_RLLMv1.21-9.Q5_K_S.gguf) \| Q5_K_S \| 1.09GB \| \| [GPT2XL_RLLMv1.21-9.Q5_K.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv1.21-9-gguf/blob/main/GPT2XL_RLLMv1.21-9.Q5_K.gguf) \| Q5_K \| 1.23GB \| \| [GPT2XL_RLLMv1.21-9.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv1.21-9-gguf/blob/main/GPT2XL_RLLMv1.21-9.Q5_K_M.gguf) \| Q5_K_M \| 1.23GB \| \| [GPT2XL_RLLMv1.21-9.Q5_1.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv1.21-9-gguf/blob/main/GPT2XL_RLLMv1.21-9.Q5_1.gguf) \| Q5_1 \| 1.12GB \| \| [GPT2XL_RLLMv1.21-9.Q6_K.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv1.21-9-gguf/blob/main/GPT2XL_RLLMv1.21-9.Q6_K.gguf) \| Q6_K \| 1.44GB \| \| [GPT2XL_RLLMv1.21-9.Q8_0.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv1.21-9-gguf/blob/main/GPT2XL_RLLMv1.21-9.Q8_0.gguf) \| Q8_0 \| 1.55GB \| Original model description: --- license: mit ---
Saef/fl_new-checkpoint-99	Saef	2025-05-29T00:14:53Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:mistralai/Mistral-7B-v0.1", "base_model:adapter:mistralai/Mistral-7B-v0.1", "region:us" ]	null	2025-05-29T00:14:33Z	--- base_model: mistralai/Mistral-7B-v0.1 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. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.13.2
drisspg/fp8-opt-125m	drisspg	2025-05-29T00:14:38Z	0	0	transformers	[ "transformers", "pytorch", "opt", "text-generation", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "torchao", "region:us" ]	text-generation	2025-05-29T00:08:30Z	--- library_name: transformers tags: [] --- # Model Card for Model ID ## Comand `python ao/prep_model.py --quant_type fp8 --push_to_hub True ` ### Script to generate ```py #!/usr/bin/env python3 # SPDX-License-Identifier: Apache-2.0 """ Script for quantizing LLM models with TorchAO. Supports various quantization configurations and model types. """ import random import numpy as np import torch import time from pathlib import Path from typing import Optional, Literal from transformers import TorchAoConfig, AutoModelForCausalLM, AutoTokenizer from transformer_nuggets.utils.benchmark import benchmark_cuda_function_in_microseconds from torchao.quantization.quant_api import ( Float8DynamicActivationFloat8WeightConfig, Int4WeightOnlyConfig, Int8WeightOnlyConfig, Int8DynamicActivationInt8WeightConfig, PerRow, PerTensor, GemliteUIntXWeightOnlyConfig, Int4DynamicActivationInt4WeightConfig, Int8DynamicActivationInt4WeightConfig, CutlassInt4PackedLayout, ) from torchao.prototype.mx_formats.mx_subclass import MXFPInferenceConfig from torchao.prototype.mx_formats import MXGemmKernelChoice from jsonargparse import CLI, Namespace from rich import print # Set seeds for reproducibility def set_seed(seed): random.seed(seed) np.random.seed(seed) torch.manual_seed(seed) torch.cuda.manual_seed_all(seed) def get_quantization_config(args): """Create TorchAo quantization config based on provided args.""" granularity_mapping = { "per_row": PerRow(), "per_tensor": PerTensor(), } gran = granularity_mapping[args.granularity] match args.quant_type: case "autoquant": return TorchAoConfig("autoquant", min_sqnr=args.min_sqnr) case "fp8": return TorchAoConfig( Float8DynamicActivationFloat8WeightConfig(granularity=gran) ) case "int4_weight_only": return TorchAoConfig(Int4WeightOnlyConfig(group_size=128)) case "int8_weight_only": return TorchAoConfig(Int8WeightOnlyConfig()) case "int8_dynamic_act_int8_weight": return TorchAoConfig(Int8DynamicActivationInt8WeightConfig()) case "gemlite": return TorchAoConfig(GemliteUIntXWeightOnlyConfig()) case "A4W4": return TorchAoConfig(Int4DynamicActivationInt4WeightConfig()) case "A8W4": return TorchAoConfig( Int8DynamicActivationInt4WeightConfig(layout=CutlassInt4PackedLayout()) ) case "mxfp8": return TorchAoConfig(MXFPInferenceConfig()) case "mxfp4": return TorchAoConfig( MXFPInferenceConfig( activation_dtype=torch.float4_e2m1fn_x2, weight_dtype=torch.float4_e2m1fn_x2, block_size=32, gemm_kernel_choice=MXGemmKernelChoice.CUTLASS, ) ) case _: raise ValueError(f"Unsupported quantization type: {args.quant_type}") def benchmark_model(model, input_ids, max_new_tokens, name=""): """Benchmark model generation speed.""" try: time_ms = benchmark_cuda_function_in_microseconds( model.generate, input_ids, max_new_tokens=max_new_tokens, cache_implementation="static", ) tokens_per_second = max_new_tokens / (time_ms / 1000) print( f"{name} model: {time_ms:.2f}ms for {max_new_tokens} tokens ({tokens_per_second:.2f} tokens/sec)" ) return time_ms except ImportError: # Fallback to simple timing if inductor utils not available print("torch._inductor.utils not available, using simple timing") start = time.time() model.generate( input_ids, max_new_tokens=max_new_tokens, cache_implementation="static" ) elapsed = (time.time() - start) * 1000 # ms tokens_per_second = max_new_tokens / (elapsed / 1000) print( f"{name} model: {elapsed:.2f}ms for {max_new_tokens} tokens ({tokens_per_second:.2f} tokens/sec)" ) return elapsed def main( model_name: str = "facebook/opt-125m", output_dir: Optional[str] = None, push_to_hub: bool = False, quant_type: Literal[ "fp8", "int4_weight_only", "int8_weight_only", "int8_dynamic_act_int8_weight", "autoquant", "gemlite", "A4W4", "A8W4", "fp8", "mxfp4", ] = "fp8", granularity: Literal["per_row", "per_tensor"] = "per_row", min_sqnr: Optional[float] = None, max_new_tokens: int = 64, benchmark: bool = False, bench_tokens: int = 100, device_map: str = "cuda", ): """ Quantize a model with TorchAO and test its performance. Args: model_name: Model to quantize (e.g., meta-llama/Meta-Llama-3-8B, facebook/opt-125m) output_dir: Directory to save the quantized model push_to_hub: HF Hub repo name to push the model (e.g., 'your-username/model-name') quant_type: Quantization type to use granularity: Quantization granularity min_sqnr: Minimum SQNR for autoquant max_new_tokens: Max tokens to generate for testing benchmark: Run benchmarking comparison bench_tokens: Number of tokens to generate for benchmarking device_map: Device mapping strategy """ # Set seed before creating the model set_seed(42) # Set default output directory based on model base name if not provided if output_dir is None: model_base_name = model_name.split("/")[-1] output_dir = f"data/{quant_type}-{model_base_name}" # Convert to args-like object for compatibility with the rest of the code args = Namespace( model_name=model_name, output_dir=output_dir, push_to_hub=push_to_hub, quant_type=quant_type, granularity=granularity, min_sqnr=min_sqnr, max_new_tokens=max_new_tokens, benchmark=benchmark, bench_tokens=bench_tokens, device_map=device_map, ) print(f"Using Model name: {args.model_name}") print(f"Quantization type: {args.quant_type}") # Create output directory output_dir = Path(args.output_dir) output_dir.mkdir(parents=True, exist_ok=True) # Get quantization config quantization_config = get_quantization_config(args) # Load and quantize model print("Loading and quantizing model...") quantized_model = AutoModelForCausalLM.from_pretrained( args.model_name, torch_dtype="bfloat16", device_map=args.device_map, quantization_config=quantization_config, ) # Load tokenizer tokenizer = AutoTokenizer.from_pretrained(args.model_name) # Test prompts prompts = [ "Why is Pytorch 2.0 the best machine learning compiler?", "Hello, my name is", "The president of the United States is", "The capital of France is", "The future of AI is", ] # Test generation print("\nTesting quantized model generation...") input_ids = tokenizer(prompts, return_tensors="pt", padding=True).to( quantized_model.device ) outputs = quantized_model.generate(input_ids, max_new_tokens=args.max_new_tokens) for i, (prompt, output) in enumerate(zip(prompts, outputs)): generated_text = tokenizer.decode(output, skip_special_tokens=True) print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}") # Save quantized model print(f"\n📁Saving quantized model to: {output_dir}") quantized_model.save_pretrained(output_dir, safe_serialization=False) tokenizer.save_pretrained(output_dir) # Push to HuggingFace hub if requested if args.push_to_hub: # Get model name from output_dir model_name = output_dir.name hub_path = f"drisspg/ao_models/{model_name}" print(f"Pushing model to HuggingFace Hub: {hub_path}") quantized_model.push_to_hub(model_name, safe_serialization=False) tokenizer.push_to_hub(model_name) # Load saved model to verify print("\nLoading saved quantized model to verify...") loaded_model = AutoModelForCausalLM.from_pretrained( output_dir, device_map=args.device_map, torch_dtype="auto" ) # Test loaded model with first prompt test_prompt = prompts[0] input_ids = tokenizer(test_prompt, return_tensors="pt").to(loaded_model.device) output = loaded_model.generate(input_ids, max_new_tokens=args.max_new_tokens) generated_text = tokenizer.decode(output[0], skip_special_tokens=True) print(f"Verification - Prompt: {test_prompt!r}, Generated text: {generated_text!r}") # Benchmark if requested if args.benchmark: print("\nBenchmarking models...") # Benchmark quantized model print("Benchmarking quantized model:") quant_time = benchmark_model( loaded_model, input_ids, args.bench_tokens, f"Quantized ({args.quant_type})" ) # Load and benchmark original model in BF16 print("\nLoading original model in BF16 for comparison...") bf16_model = AutoModelForCausalLM.from_pretrained( args.model_name, device_map=args.device_map, torch_dtype=torch.bfloat16 ) # Benchmark original model print("Benchmarking original BF16 model:") bf16_time = benchmark_model(bf16_model, input_ids, args.bench_tokens, "BF16") # Calculate speedup speedup = bf16_time / quant_time if quant_time > 0 else 0 print(f"\nSpeedup: {speedup:.2f}x") print("\nQuantization process completed successfully.") if __name__ == "__main__": CLI(main) ```
dimasik2987/bbc50b03-745f-46eb-b9db-4cf12c1dcdb9	dimasik2987	2025-05-29T00:14:14Z	0	0	peft	[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "base_model:codellama/CodeLlama-7b-Instruct-hf", "base_model:adapter:codellama/CodeLlama-7b-Instruct-hf", "license:llama2", "4-bit", "bitsandbytes", "region:us" ]	null	2025-05-28T22:21:06Z	--- library_name: peft license: llama2 base_model: codellama/CodeLlama-7b-Instruct-hf tags: - axolotl - generated_from_trainer model-index: - name: bbc50b03-745f-46eb-b9db-4cf12c1dcdb9 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: codellama/CodeLlama-7b-Instruct-hf bf16: true chat_template: llama3 dataset_prepared_path: /workspace/axolotl datasets: - data_files: - cf9e35bda9ac1e44_train_data.json ds_type: json format: custom path: /workspace/input_data/ type: field_input: input field_instruction: instruct field_output: output format: '{instruction} {input}' 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: true 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: 2 gradient_checkpointing: true gradient_clipping: 0.85 group_by_length: false hub_model_id: dimasik2987/bbc50b03-745f-46eb-b9db-4cf12c1dcdb9 hub_repo: null hub_strategy: end hub_token: null learning_rate: 5.0e-06 load_in_4bit: true load_in_8bit: false local_rank: null logging_steps: 1 lora_alpha: 128 lora_dropout: 0.1 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 64 lora_target_linear: true lr_scheduler: cosine max_steps: 500 micro_batch_size: 12 mixed_precision: bf16 mlflow_experiment_name: /tmp/cf9e35bda9ac1e44_train_data.json model_type: AutoModelForCausalLM num_epochs: 2 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 special_tokens: pad_token: </s> 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: d444ccbf-1904-491d-9e28-e4e4f984e6ad wandb_project: s56-7 wandb_run: your_name wandb_runid: d444ccbf-1904-491d-9e28-e4e4f984e6ad warmup_steps: 50 weight_decay: 0.02 xformers_attention: true ``` </details><br> # bbc50b03-745f-46eb-b9db-4cf12c1dcdb9 This model is a fine-tuned version of [codellama/CodeLlama-7b-Instruct-hf](https://huggingface.co/codellama/CodeLlama-7b-Instruct-hf) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.7485 ## 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: 12 - eval_batch_size: 12 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 24 - 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: 50 - training_steps: 500 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 1.1227 \| 0.0001 \| 1 \| 1.0201 \| \| 0.8283 \| 0.0128 \| 250 \| 0.7735 \| \| 0.6213 \| 0.0255 \| 500 \| 0.7485 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
BootesVoid/cmb8kvm230nt2lexpb8vfkkh6_cmb8lgo1y0ny8lexpl6p292mz	BootesVoid	2025-05-29T00:13:42Z	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	2025-05-29T00:13:40Z	--- 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: NOODLE --- # Cmb8Kvm230Nt2Lexpb8Vfkkh6_Cmb8Lgo1Y0Ny8Lexpl6P292Mz <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 `NOODLE` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "NOODLE", "lora_weights": "https://huggingface.co/BootesVoid/cmb8kvm230nt2lexpb8vfkkh6_cmb8lgo1y0ny8lexpl6p292mz/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('BootesVoid/cmb8kvm230nt2lexpb8vfkkh6_cmb8lgo1y0ny8lexpl6p292mz', weight_name='lora.safetensors') image = pipeline('NOODLE').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/BootesVoid/cmb8kvm230nt2lexpb8vfkkh6_cmb8lgo1y0ny8lexpl6p292mz/discussions) to add images that show off what you’ve made with this LoRA.
RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv12-layer-2-gguf	RichardErkhov	2025-05-29T00:13:12Z	0	0	null	[ "gguf", "endpoints_compatible", "region:us" ]	null	2025-05-28T22:44:48Z	Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) GPT2XL_RLLMv12-layer-2 - GGUF - Model creator: https://huggingface.co/migueldeguzmandev/ - Original model: https://huggingface.co/migueldeguzmandev/GPT2XL_RLLMv12-layer-2/ \| Name \| Quant method \| Size \| \| ---- \| ---- \| ---- \| \| [GPT2XL_RLLMv12-layer-2.Q2_K.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv12-layer-2-gguf/blob/main/GPT2XL_RLLMv12-layer-2.Q2_K.gguf) \| Q2_K \| 0.8GB \| \| [GPT2XL_RLLMv12-layer-2.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv12-layer-2-gguf/blob/main/GPT2XL_RLLMv12-layer-2.IQ3_XS.gguf) \| IQ3_XS \| 0.8GB \| \| [GPT2XL_RLLMv12-layer-2.IQ3_S.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv12-layer-2-gguf/blob/main/GPT2XL_RLLMv12-layer-2.IQ3_S.gguf) \| IQ3_S \| 0.8GB \| \| [GPT2XL_RLLMv12-layer-2.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv12-layer-2-gguf/blob/main/GPT2XL_RLLMv12-layer-2.Q3_K_S.gguf) \| Q3_K_S \| 0.8GB \| \| [GPT2XL_RLLMv12-layer-2.IQ3_M.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv12-layer-2-gguf/blob/main/GPT2XL_RLLMv12-layer-2.IQ3_M.gguf) \| IQ3_M \| 0.87GB \| \| [GPT2XL_RLLMv12-layer-2.Q3_K.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv12-layer-2-gguf/blob/main/GPT2XL_RLLMv12-layer-2.Q3_K.gguf) \| Q3_K \| 0.92GB \| \| [GPT2XL_RLLMv12-layer-2.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv12-layer-2-gguf/blob/main/GPT2XL_RLLMv12-layer-2.Q3_K_M.gguf) \| Q3_K_M \| 0.92GB \| \| [GPT2XL_RLLMv12-layer-2.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv12-layer-2-gguf/blob/main/GPT2XL_RLLMv12-layer-2.Q3_K_L.gguf) \| Q3_K_L \| 0.99GB \| \| [GPT2XL_RLLMv12-layer-2.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv12-layer-2-gguf/blob/main/GPT2XL_RLLMv12-layer-2.IQ4_XS.gguf) \| IQ4_XS \| 0.86GB \| \| [GPT2XL_RLLMv12-layer-2.Q4_0.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv12-layer-2-gguf/blob/main/GPT2XL_RLLMv12-layer-2.Q4_0.gguf) \| Q4_0 \| 0.86GB \| \| [GPT2XL_RLLMv12-layer-2.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv12-layer-2-gguf/blob/main/GPT2XL_RLLMv12-layer-2.IQ4_NL.gguf) \| IQ4_NL \| 0.87GB \| \| [GPT2XL_RLLMv12-layer-2.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv12-layer-2-gguf/blob/main/GPT2XL_RLLMv12-layer-2.Q4_K_S.gguf) \| Q4_K_S \| 0.99GB \| \| [GPT2XL_RLLMv12-layer-2.Q4_K.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv12-layer-2-gguf/blob/main/GPT2XL_RLLMv12-layer-2.Q4_K.gguf) \| Q4_K \| 1.06GB \| \| [GPT2XL_RLLMv12-layer-2.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv12-layer-2-gguf/blob/main/GPT2XL_RLLMv12-layer-2.Q4_K_M.gguf) \| Q4_K_M \| 1.06GB \| \| [GPT2XL_RLLMv12-layer-2.Q4_1.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv12-layer-2-gguf/blob/main/GPT2XL_RLLMv12-layer-2.Q4_1.gguf) \| Q4_1 \| 0.95GB \| \| [GPT2XL_RLLMv12-layer-2.Q5_0.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv12-layer-2-gguf/blob/main/GPT2XL_RLLMv12-layer-2.Q5_0.gguf) \| Q5_0 \| 1.04GB \| \| [GPT2XL_RLLMv12-layer-2.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv12-layer-2-gguf/blob/main/GPT2XL_RLLMv12-layer-2.Q5_K_S.gguf) \| Q5_K_S \| 1.09GB \| \| [GPT2XL_RLLMv12-layer-2.Q5_K.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv12-layer-2-gguf/blob/main/GPT2XL_RLLMv12-layer-2.Q5_K.gguf) \| Q5_K \| 1.23GB \| \| [GPT2XL_RLLMv12-layer-2.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv12-layer-2-gguf/blob/main/GPT2XL_RLLMv12-layer-2.Q5_K_M.gguf) \| Q5_K_M \| 1.23GB \| \| [GPT2XL_RLLMv12-layer-2.Q5_1.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv12-layer-2-gguf/blob/main/GPT2XL_RLLMv12-layer-2.Q5_1.gguf) \| Q5_1 \| 1.12GB \| \| [GPT2XL_RLLMv12-layer-2.Q6_K.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv12-layer-2-gguf/blob/main/GPT2XL_RLLMv12-layer-2.Q6_K.gguf) \| Q6_K \| 1.44GB \| \| [GPT2XL_RLLMv12-layer-2.Q8_0.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv12-layer-2-gguf/blob/main/GPT2XL_RLLMv12-layer-2.Q8_0.gguf) \| Q8_0 \| 1.55GB \| Original model description: --- license: mit --- [More info? see RLLM virtual map!](https://whimsical.com/rllm-visual-map-QQvFHNr6aVDdXRUnyb5NCu)
dimasik87/1a40b829-33ab-4a0b-84ad-00c41d2766e2	dimasik87	2025-05-29T00:11:45Z	0	0	peft	[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "base_model:codellama/CodeLlama-7b-Instruct-hf", "base_model:adapter:codellama/CodeLlama-7b-Instruct-hf", "license:llama2", "4-bit", "bitsandbytes", "region:us" ]	null	2025-05-28T22:20:22Z	--- library_name: peft license: llama2 base_model: codellama/CodeLlama-7b-Instruct-hf tags: - axolotl - generated_from_trainer model-index: - name: 1a40b829-33ab-4a0b-84ad-00c41d2766e2 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: codellama/CodeLlama-7b-Instruct-hf bf16: true chat_template: llama3 dataset_prepared_path: /workspace/axolotl datasets: - data_files: - cf9e35bda9ac1e44_train_data.json ds_type: json format: custom path: /workspace/input_data/ type: field_input: input field_instruction: instruct field_output: output format: '{instruction} {input}' 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: true 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: 4 gradient_checkpointing: true gradient_clipping: 1.0 group_by_length: false hub_model_id: dimasik87/1a40b829-33ab-4a0b-84ad-00c41d2766e2 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: 0.1 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 32 lora_target_linear: true lr_scheduler: cosine max_steps: 500 micro_batch_size: 6 mixed_precision: bf16 mlflow_experiment_name: /tmp/cf9e35bda9ac1e44_train_data.json model_type: AutoModelForCausalLM num_epochs: 2 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 special_tokens: pad_token: </s> 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: d444ccbf-1904-491d-9e28-e4e4f984e6ad wandb_project: s56-7 wandb_run: your_name wandb_runid: d444ccbf-1904-491d-9e28-e4e4f984e6ad warmup_steps: 50 weight_decay: 0.05 xformers_attention: true ``` </details><br> # 1a40b829-33ab-4a0b-84ad-00c41d2766e2 This model is a fine-tuned version of [codellama/CodeLlama-7b-Instruct-hf](https://huggingface.co/codellama/CodeLlama-7b-Instruct-hf) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.0434 ## 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: 6 - eval_batch_size: 6 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 24 - 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: 50 - training_steps: 500 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 1.1228 \| 0.0001 \| 1 \| 1.0867 \| \| 1.1274 \| 0.0128 \| 250 \| 1.0593 \| \| 0.8505 \| 0.0255 \| 500 \| 1.0434 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv13-layer-8-gguf	RichardErkhov	2025-05-29T00:10:46Z	0	0	null	[ "gguf", "endpoints_compatible", "region:us" ]	null	2025-05-28T22:42:22Z	Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) GPT2XL_RLLMv13-layer-8 - GGUF - Model creator: https://huggingface.co/migueldeguzmandev/ - Original model: https://huggingface.co/migueldeguzmandev/GPT2XL_RLLMv13-layer-8/ \| Name \| Quant method \| Size \| \| ---- \| ---- \| ---- \| \| [GPT2XL_RLLMv13-layer-8.Q2_K.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv13-layer-8-gguf/blob/main/GPT2XL_RLLMv13-layer-8.Q2_K.gguf) \| Q2_K \| 0.8GB \| \| [GPT2XL_RLLMv13-layer-8.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv13-layer-8-gguf/blob/main/GPT2XL_RLLMv13-layer-8.IQ3_XS.gguf) \| IQ3_XS \| 0.8GB \| \| [GPT2XL_RLLMv13-layer-8.IQ3_S.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv13-layer-8-gguf/blob/main/GPT2XL_RLLMv13-layer-8.IQ3_S.gguf) \| IQ3_S \| 0.8GB \| \| [GPT2XL_RLLMv13-layer-8.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv13-layer-8-gguf/blob/main/GPT2XL_RLLMv13-layer-8.Q3_K_S.gguf) \| Q3_K_S \| 0.8GB \| \| [GPT2XL_RLLMv13-layer-8.IQ3_M.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv13-layer-8-gguf/blob/main/GPT2XL_RLLMv13-layer-8.IQ3_M.gguf) \| IQ3_M \| 0.87GB \| \| [GPT2XL_RLLMv13-layer-8.Q3_K.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv13-layer-8-gguf/blob/main/GPT2XL_RLLMv13-layer-8.Q3_K.gguf) \| Q3_K \| 0.92GB \| \| [GPT2XL_RLLMv13-layer-8.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv13-layer-8-gguf/blob/main/GPT2XL_RLLMv13-layer-8.Q3_K_M.gguf) \| Q3_K_M \| 0.92GB \| \| [GPT2XL_RLLMv13-layer-8.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv13-layer-8-gguf/blob/main/GPT2XL_RLLMv13-layer-8.Q3_K_L.gguf) \| Q3_K_L \| 0.99GB \| \| [GPT2XL_RLLMv13-layer-8.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv13-layer-8-gguf/blob/main/GPT2XL_RLLMv13-layer-8.IQ4_XS.gguf) \| IQ4_XS \| 0.86GB \| \| [GPT2XL_RLLMv13-layer-8.Q4_0.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv13-layer-8-gguf/blob/main/GPT2XL_RLLMv13-layer-8.Q4_0.gguf) \| Q4_0 \| 0.86GB \| \| [GPT2XL_RLLMv13-layer-8.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv13-layer-8-gguf/blob/main/GPT2XL_RLLMv13-layer-8.IQ4_NL.gguf) \| IQ4_NL \| 0.87GB \| \| [GPT2XL_RLLMv13-layer-8.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv13-layer-8-gguf/blob/main/GPT2XL_RLLMv13-layer-8.Q4_K_S.gguf) \| Q4_K_S \| 0.99GB \| \| [GPT2XL_RLLMv13-layer-8.Q4_K.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv13-layer-8-gguf/blob/main/GPT2XL_RLLMv13-layer-8.Q4_K.gguf) \| Q4_K \| 1.06GB \| \| [GPT2XL_RLLMv13-layer-8.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv13-layer-8-gguf/blob/main/GPT2XL_RLLMv13-layer-8.Q4_K_M.gguf) \| Q4_K_M \| 1.06GB \| \| [GPT2XL_RLLMv13-layer-8.Q4_1.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv13-layer-8-gguf/blob/main/GPT2XL_RLLMv13-layer-8.Q4_1.gguf) \| Q4_1 \| 0.95GB \| \| [GPT2XL_RLLMv13-layer-8.Q5_0.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv13-layer-8-gguf/blob/main/GPT2XL_RLLMv13-layer-8.Q5_0.gguf) \| Q5_0 \| 1.04GB \| \| [GPT2XL_RLLMv13-layer-8.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv13-layer-8-gguf/blob/main/GPT2XL_RLLMv13-layer-8.Q5_K_S.gguf) \| Q5_K_S \| 1.09GB \| \| [GPT2XL_RLLMv13-layer-8.Q5_K.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv13-layer-8-gguf/blob/main/GPT2XL_RLLMv13-layer-8.Q5_K.gguf) \| Q5_K \| 1.23GB \| \| [GPT2XL_RLLMv13-layer-8.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv13-layer-8-gguf/blob/main/GPT2XL_RLLMv13-layer-8.Q5_K_M.gguf) \| Q5_K_M \| 1.23GB \| \| [GPT2XL_RLLMv13-layer-8.Q5_1.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv13-layer-8-gguf/blob/main/GPT2XL_RLLMv13-layer-8.Q5_1.gguf) \| Q5_1 \| 1.12GB \| \| [GPT2XL_RLLMv13-layer-8.Q6_K.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv13-layer-8-gguf/blob/main/GPT2XL_RLLMv13-layer-8.Q6_K.gguf) \| Q6_K \| 1.44GB \| \| [GPT2XL_RLLMv13-layer-8.Q8_0.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv13-layer-8-gguf/blob/main/GPT2XL_RLLMv13-layer-8.Q8_0.gguf) \| Q8_0 \| 1.55GB \| Original model description: --- license: mit --- [More info? see RLLM virtual map!](https://whimsical.com/rllm-visual-map-QQvFHNr6aVDdXRUnyb5NCu)
vermoney/0d5f22dd-45a7-4bcd-87f1-6db7afae05aa	vermoney	2025-05-29T00:09:47Z	0	0	peft	[ "peft", "safetensors", "gpt_neox", "axolotl", "generated_from_trainer", "base_model:EleutherAI/pythia-1b", "base_model:adapter:EleutherAI/pythia-1b", "license:apache-2.0", "4-bit", "bitsandbytes", "region:us" ]	null	2025-05-29T00:03:38Z	--- library_name: peft license: apache-2.0 base_model: EleutherAI/pythia-1b tags: - axolotl - generated_from_trainer model-index: - name: 0d5f22dd-45a7-4bcd-87f1-6db7afae05aa 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: EleutherAI/pythia-1b bf16: true chat_template: llama3 dataset_prepared_path: null datasets: - data_files: - 97434882b7b4c318_train_data.json ds_type: json format: custom path: /workspace/input_data/ type: field_instruction: instruct field_output: output 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: true reference_model: null early_stopping_patience: null eval_max_new_tokens: 128 eval_table_size: null evals_per_epoch: 1 flash_attention: true fp16: false fsdp: null fsdp_config: null gradient_accumulation_steps: 3 gradient_checkpointing: true gradient_clipping: 1.0 group_by_length: false hub_model_id: vermoney/0d5f22dd-45a7-4bcd-87f1-6db7afae05aa hub_repo: null hub_strategy: end hub_token: null learning_rate: 2.0e-06 load_in_4bit: true load_in_8bit: false local_rank: null logging_steps: 1 lora_alpha: 96 lora_dropout: 0.1 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 48 lora_target_linear: true lr_scheduler: cosine max_steps: 280 micro_batch_size: 6 mixed_precision: bf16 mlflow_experiment_name: /tmp/97434882b7b4c318_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 special_tokens: pad_token: <\|endoftext\|> 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: ac76ea07-90a3-4e37-8b05-8f28762ac1a8 wandb_project: s56-9 wandb_run: your_name wandb_runid: ac76ea07-90a3-4e37-8b05-8f28762ac1a8 warmup_steps: 40 weight_decay: 0.02 xformers_attention: true ``` </details><br> # 0d5f22dd-45a7-4bcd-87f1-6db7afae05aa This model is a fine-tuned version of [EleutherAI/pythia-1b](https://huggingface.co/EleutherAI/pythia-1b) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.7670 ## 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-06 - train_batch_size: 6 - eval_batch_size: 6 - seed: 42 - gradient_accumulation_steps: 3 - total_train_batch_size: 18 - 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: 40 - training_steps: 280 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 5.2327 \| 0.0574 \| 280 \| 1.7670 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1
mradermacher/L3-MOE-2X16.5B-DARKEST-Planet-Song-of-Fire-29B-GGUF	mradermacher	2025-05-29T00:08:42Z	0	0	transformers	[ "transformers", "gguf", "mergekit", "moe", "mixture of experts", "merge", "llama-3", "llama3", "en", "base_model:DavidAU/L3-MOE-2X16.5B-DARKEST-Planet-Song-of-Fire-29B", "base_model:quantized:DavidAU/L3-MOE-2X16.5B-DARKEST-Planet-Song-of-Fire-29B", "endpoints_compatible", "region:us", "conversational" ]	null	2024-12-17T18:16:25Z	--- base_model: DavidAU/L3-MOE-2X16.5B-DARKEST-Planet-Song-of-Fire-29B language: - en library_name: transformers quantized_by: mradermacher tags: - mergekit - moe - mixture of experts - merge - llama-3 - llama3 --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/DavidAU/L3-MOE-2X16.5B-DARKEST-Planet-Song-of-Fire-29B <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/L3-MOE-2X16.5B-DARKEST-Planet-Song-of-Fire-29B-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/L3-MOE-2X16.5B-DARKEST-Planet-Song-of-Fire-29B-GGUF/resolve/main/L3-MOE-2X16.5B-DARKEST-Planet-Song-of-Fire-29B.Q2_K.gguf) \| Q2_K \| 10.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/L3-MOE-2X16.5B-DARKEST-Planet-Song-of-Fire-29B-GGUF/resolve/main/L3-MOE-2X16.5B-DARKEST-Planet-Song-of-Fire-29B.Q3_K_S.gguf) \| Q3_K_S \| 12.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/L3-MOE-2X16.5B-DARKEST-Planet-Song-of-Fire-29B-GGUF/resolve/main/L3-MOE-2X16.5B-DARKEST-Planet-Song-of-Fire-29B.Q3_K_M.gguf) \| Q3_K_M \| 14.2 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/L3-MOE-2X16.5B-DARKEST-Planet-Song-of-Fire-29B-GGUF/resolve/main/L3-MOE-2X16.5B-DARKEST-Planet-Song-of-Fire-29B.Q3_K_L.gguf) \| Q3_K_L \| 15.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/L3-MOE-2X16.5B-DARKEST-Planet-Song-of-Fire-29B-GGUF/resolve/main/L3-MOE-2X16.5B-DARKEST-Planet-Song-of-Fire-29B.IQ4_XS.gguf) \| IQ4_XS \| 15.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/L3-MOE-2X16.5B-DARKEST-Planet-Song-of-Fire-29B-GGUF/resolve/main/L3-MOE-2X16.5B-DARKEST-Planet-Song-of-Fire-29B.Q4_K_S.gguf) \| Q4_K_S \| 16.7 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/L3-MOE-2X16.5B-DARKEST-Planet-Song-of-Fire-29B-GGUF/resolve/main/L3-MOE-2X16.5B-DARKEST-Planet-Song-of-Fire-29B.Q4_K_M.gguf) \| Q4_K_M \| 17.7 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/L3-MOE-2X16.5B-DARKEST-Planet-Song-of-Fire-29B-GGUF/resolve/main/L3-MOE-2X16.5B-DARKEST-Planet-Song-of-Fire-29B.Q5_K_S.gguf) \| Q5_K_S \| 20.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/L3-MOE-2X16.5B-DARKEST-Planet-Song-of-Fire-29B-GGUF/resolve/main/L3-MOE-2X16.5B-DARKEST-Planet-Song-of-Fire-29B.Q5_K_M.gguf) \| Q5_K_M \| 20.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/L3-MOE-2X16.5B-DARKEST-Planet-Song-of-Fire-29B-GGUF/resolve/main/L3-MOE-2X16.5B-DARKEST-Planet-Song-of-Fire-29B.Q6_K.gguf) \| Q6_K \| 23.9 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/L3-MOE-2X16.5B-DARKEST-Planet-Song-of-Fire-29B-GGUF/resolve/main/L3-MOE-2X16.5B-DARKEST-Planet-Song-of-Fire-29B.Q8_0.gguf) \| Q8_0 \| 31.0 \| 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 -->
winnieyangwannan/Llama-3.1-8B-Instruct_mlp-down_positive-negative-addition_last_layer_6_2_song_ratio_3_epoch_39	winnieyangwannan	2025-05-29T00:06:23Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-28T21:09:38Z	--- 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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mradermacher/L3.1-MOE-8X8B-Dark-Planet-8D-Mirrored-Chaos-Uncensored-47B-GGUF	mradermacher	2025-05-29T00:05:52Z	21	0	transformers	[ "transformers", "gguf", "mergekit", "moe", "mixture of experts", "merge", "llama 3.1", "llama-3", "llama3", "llama-3.1", "en", "base_model:DavidAU/L3.1-MOE-8X8B-Dark-Planet-8D-Mirrored-Chaos-Uncensored-47B", "base_model:quantized:DavidAU/L3.1-MOE-8X8B-Dark-Planet-8D-Mirrored-Chaos-Uncensored-47B", "endpoints_compatible", "region:us", "conversational" ]	null	2024-12-18T09:10:57Z	--- base_model: DavidAU/L3.1-MOE-8X8B-Dark-Planet-8D-Mirrored-Chaos-Uncensored-47B language: - en library_name: transformers quantized_by: mradermacher tags: - mergekit - moe - mixture of experts - merge - llama 3.1 - llama-3 - llama3 - llama-3.1 --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/DavidAU/L3.1-MOE-8X8B-Dark-Planet-8D-Mirrored-Chaos-Uncensored-47B <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/L3.1-MOE-8X8B-Dark-Planet-8D-Mirrored-Chaos-Uncensored-47B-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/L3.1-MOE-8X8B-Dark-Planet-8D-Mirrored-Chaos-Uncensored-47B-GGUF/resolve/main/L3.1-MOE-8X8B-Dark-Planet-8D-Mirrored-Chaos-Uncensored-47B.Q2_K.gguf) \| Q2_K \| 17.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/L3.1-MOE-8X8B-Dark-Planet-8D-Mirrored-Chaos-Uncensored-47B-GGUF/resolve/main/L3.1-MOE-8X8B-Dark-Planet-8D-Mirrored-Chaos-Uncensored-47B.Q3_K_S.gguf) \| Q3_K_S \| 21.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/L3.1-MOE-8X8B-Dark-Planet-8D-Mirrored-Chaos-Uncensored-47B-GGUF/resolve/main/L3.1-MOE-8X8B-Dark-Planet-8D-Mirrored-Chaos-Uncensored-47B.Q3_K_M.gguf) \| Q3_K_M \| 23.1 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/L3.1-MOE-8X8B-Dark-Planet-8D-Mirrored-Chaos-Uncensored-47B-GGUF/resolve/main/L3.1-MOE-8X8B-Dark-Planet-8D-Mirrored-Chaos-Uncensored-47B.Q3_K_L.gguf) \| Q3_K_L \| 24.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/L3.1-MOE-8X8B-Dark-Planet-8D-Mirrored-Chaos-Uncensored-47B-GGUF/resolve/main/L3.1-MOE-8X8B-Dark-Planet-8D-Mirrored-Chaos-Uncensored-47B.IQ4_XS.gguf) \| IQ4_XS \| 26.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/L3.1-MOE-8X8B-Dark-Planet-8D-Mirrored-Chaos-Uncensored-47B-GGUF/resolve/main/L3.1-MOE-8X8B-Dark-Planet-8D-Mirrored-Chaos-Uncensored-47B.Q4_K_S.gguf) \| Q4_K_S \| 27.4 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/L3.1-MOE-8X8B-Dark-Planet-8D-Mirrored-Chaos-Uncensored-47B-GGUF/resolve/main/L3.1-MOE-8X8B-Dark-Planet-8D-Mirrored-Chaos-Uncensored-47B.Q4_K_M.gguf) \| Q4_K_M \| 29.1 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/L3.1-MOE-8X8B-Dark-Planet-8D-Mirrored-Chaos-Uncensored-47B-GGUF/resolve/main/L3.1-MOE-8X8B-Dark-Planet-8D-Mirrored-Chaos-Uncensored-47B.Q5_K_S.gguf) \| Q5_K_S \| 32.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/L3.1-MOE-8X8B-Dark-Planet-8D-Mirrored-Chaos-Uncensored-47B-GGUF/resolve/main/L3.1-MOE-8X8B-Dark-Planet-8D-Mirrored-Chaos-Uncensored-47B.Q5_K_M.gguf) \| Q5_K_M \| 33.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/L3.1-MOE-8X8B-Dark-Planet-8D-Mirrored-Chaos-Uncensored-47B-GGUF/resolve/main/L3.1-MOE-8X8B-Dark-Planet-8D-Mirrored-Chaos-Uncensored-47B.Q6_K.gguf) \| Q6_K \| 39.1 \| very good quality \| \| [PART 1](https://huggingface.co/mradermacher/L3.1-MOE-8X8B-Dark-Planet-8D-Mirrored-Chaos-Uncensored-47B-GGUF/resolve/main/L3.1-MOE-8X8B-Dark-Planet-8D-Mirrored-Chaos-Uncensored-47B.Q8_0.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/L3.1-MOE-8X8B-Dark-Planet-8D-Mirrored-Chaos-Uncensored-47B-GGUF/resolve/main/L3.1-MOE-8X8B-Dark-Planet-8D-Mirrored-Chaos-Uncensored-47B.Q8_0.gguf.part2of2) \| Q8_0 \| 50.6 \| 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 -->
mradermacher/L3.1-MOE-8X8B-Dark-Planet-8D-Mirrored-Chaos-Uncensored-47B-i1-GGUF	mradermacher	2025-05-29T00:05:41Z	236	0	transformers	[ "transformers", "gguf", "mergekit", "moe", "mixture of experts", "merge", "llama 3.1", "llama-3", "llama3", "llama-3.1", "en", "base_model:DavidAU/L3.1-MOE-8X8B-Dark-Planet-8D-Mirrored-Chaos-Uncensored-47B", "base_model:quantized:DavidAU/L3.1-MOE-8X8B-Dark-Planet-8D-Mirrored-Chaos-Uncensored-47B", "endpoints_compatible", "region:us", "imatrix", "conversational" ]	null	2024-12-18T09:33:46Z	--- base_model: DavidAU/L3.1-MOE-8X8B-Dark-Planet-8D-Mirrored-Chaos-Uncensored-47B language: - en library_name: transformers quantized_by: mradermacher tags: - mergekit - moe - mixture of experts - merge - llama 3.1 - llama-3 - llama3 - llama-3.1 --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> weighted/imatrix quants of https://huggingface.co/DavidAU/L3.1-MOE-8X8B-Dark-Planet-8D-Mirrored-Chaos-Uncensored-47B <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/L3.1-MOE-8X8B-Dark-Planet-8D-Mirrored-Chaos-Uncensored-47B-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/L3.1-MOE-8X8B-Dark-Planet-8D-Mirrored-Chaos-Uncensored-47B-i1-GGUF/resolve/main/L3.1-MOE-8X8B-Dark-Planet-8D-Mirrored-Chaos-Uncensored-47B.i1-IQ1_S.gguf) \| i1-IQ1_S \| 10.3 \| for the desperate \| \| [GGUF](https://huggingface.co/mradermacher/L3.1-MOE-8X8B-Dark-Planet-8D-Mirrored-Chaos-Uncensored-47B-i1-GGUF/resolve/main/L3.1-MOE-8X8B-Dark-Planet-8D-Mirrored-Chaos-Uncensored-47B.i1-IQ1_M.gguf) \| i1-IQ1_M \| 11.4 \| mostly desperate \| \| [GGUF](https://huggingface.co/mradermacher/L3.1-MOE-8X8B-Dark-Planet-8D-Mirrored-Chaos-Uncensored-47B-i1-GGUF/resolve/main/L3.1-MOE-8X8B-Dark-Planet-8D-Mirrored-Chaos-Uncensored-47B.i1-IQ2_XXS.gguf) \| i1-IQ2_XXS \| 13.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/L3.1-MOE-8X8B-Dark-Planet-8D-Mirrored-Chaos-Uncensored-47B-i1-GGUF/resolve/main/L3.1-MOE-8X8B-Dark-Planet-8D-Mirrored-Chaos-Uncensored-47B.i1-IQ2_XS.gguf) \| i1-IQ2_XS \| 14.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/L3.1-MOE-8X8B-Dark-Planet-8D-Mirrored-Chaos-Uncensored-47B-i1-GGUF/resolve/main/L3.1-MOE-8X8B-Dark-Planet-8D-Mirrored-Chaos-Uncensored-47B.i1-IQ2_S.gguf) \| i1-IQ2_S \| 14.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/L3.1-MOE-8X8B-Dark-Planet-8D-Mirrored-Chaos-Uncensored-47B-i1-GGUF/resolve/main/L3.1-MOE-8X8B-Dark-Planet-8D-Mirrored-Chaos-Uncensored-47B.i1-IQ2_M.gguf) \| i1-IQ2_M \| 16.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/L3.1-MOE-8X8B-Dark-Planet-8D-Mirrored-Chaos-Uncensored-47B-i1-GGUF/resolve/main/L3.1-MOE-8X8B-Dark-Planet-8D-Mirrored-Chaos-Uncensored-47B.i1-Q2_K_S.gguf) \| i1-Q2_K_S \| 16.6 \| very low quality \| \| [GGUF](https://huggingface.co/mradermacher/L3.1-MOE-8X8B-Dark-Planet-8D-Mirrored-Chaos-Uncensored-47B-i1-GGUF/resolve/main/L3.1-MOE-8X8B-Dark-Planet-8D-Mirrored-Chaos-Uncensored-47B.i1-Q2_K.gguf) \| i1-Q2_K \| 17.9 \| IQ3_XXS probably better \| \| [GGUF](https://huggingface.co/mradermacher/L3.1-MOE-8X8B-Dark-Planet-8D-Mirrored-Chaos-Uncensored-47B-i1-GGUF/resolve/main/L3.1-MOE-8X8B-Dark-Planet-8D-Mirrored-Chaos-Uncensored-47B.i1-IQ3_XXS.gguf) \| i1-IQ3_XXS \| 18.8 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/L3.1-MOE-8X8B-Dark-Planet-8D-Mirrored-Chaos-Uncensored-47B-i1-GGUF/resolve/main/L3.1-MOE-8X8B-Dark-Planet-8D-Mirrored-Chaos-Uncensored-47B.i1-IQ3_XS.gguf) \| i1-IQ3_XS \| 20.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/L3.1-MOE-8X8B-Dark-Planet-8D-Mirrored-Chaos-Uncensored-47B-i1-GGUF/resolve/main/L3.1-MOE-8X8B-Dark-Planet-8D-Mirrored-Chaos-Uncensored-47B.i1-IQ3_S.gguf) \| i1-IQ3_S \| 21.0 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/L3.1-MOE-8X8B-Dark-Planet-8D-Mirrored-Chaos-Uncensored-47B-i1-GGUF/resolve/main/L3.1-MOE-8X8B-Dark-Planet-8D-Mirrored-Chaos-Uncensored-47B.i1-Q3_K_S.gguf) \| i1-Q3_K_S \| 21.0 \| IQ3_XS probably better \| \| [GGUF](https://huggingface.co/mradermacher/L3.1-MOE-8X8B-Dark-Planet-8D-Mirrored-Chaos-Uncensored-47B-i1-GGUF/resolve/main/L3.1-MOE-8X8B-Dark-Planet-8D-Mirrored-Chaos-Uncensored-47B.i1-IQ3_M.gguf) \| i1-IQ3_M \| 22.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/L3.1-MOE-8X8B-Dark-Planet-8D-Mirrored-Chaos-Uncensored-47B-i1-GGUF/resolve/main/L3.1-MOE-8X8B-Dark-Planet-8D-Mirrored-Chaos-Uncensored-47B.i1-Q3_K_M.gguf) \| i1-Q3_K_M \| 23.1 \| IQ3_S probably better \| \| [GGUF](https://huggingface.co/mradermacher/L3.1-MOE-8X8B-Dark-Planet-8D-Mirrored-Chaos-Uncensored-47B-i1-GGUF/resolve/main/L3.1-MOE-8X8B-Dark-Planet-8D-Mirrored-Chaos-Uncensored-47B.i1-Q3_K_L.gguf) \| i1-Q3_K_L \| 24.8 \| IQ3_M probably better \| \| [GGUF](https://huggingface.co/mradermacher/L3.1-MOE-8X8B-Dark-Planet-8D-Mirrored-Chaos-Uncensored-47B-i1-GGUF/resolve/main/L3.1-MOE-8X8B-Dark-Planet-8D-Mirrored-Chaos-Uncensored-47B.i1-IQ4_XS.gguf) \| i1-IQ4_XS \| 25.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/L3.1-MOE-8X8B-Dark-Planet-8D-Mirrored-Chaos-Uncensored-47B-i1-GGUF/resolve/main/L3.1-MOE-8X8B-Dark-Planet-8D-Mirrored-Chaos-Uncensored-47B.i1-Q4_0.gguf) \| i1-Q4_0 \| 27.2 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/L3.1-MOE-8X8B-Dark-Planet-8D-Mirrored-Chaos-Uncensored-47B-i1-GGUF/resolve/main/L3.1-MOE-8X8B-Dark-Planet-8D-Mirrored-Chaos-Uncensored-47B.i1-Q4_K_S.gguf) \| i1-Q4_K_S \| 27.4 \| optimal size/speed/quality \| \| [GGUF](https://huggingface.co/mradermacher/L3.1-MOE-8X8B-Dark-Planet-8D-Mirrored-Chaos-Uncensored-47B-i1-GGUF/resolve/main/L3.1-MOE-8X8B-Dark-Planet-8D-Mirrored-Chaos-Uncensored-47B.i1-Q4_K_M.gguf) \| i1-Q4_K_M \| 29.1 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/L3.1-MOE-8X8B-Dark-Planet-8D-Mirrored-Chaos-Uncensored-47B-i1-GGUF/resolve/main/L3.1-MOE-8X8B-Dark-Planet-8D-Mirrored-Chaos-Uncensored-47B.i1-Q5_K_S.gguf) \| i1-Q5_K_S \| 32.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/L3.1-MOE-8X8B-Dark-Planet-8D-Mirrored-Chaos-Uncensored-47B-i1-GGUF/resolve/main/L3.1-MOE-8X8B-Dark-Planet-8D-Mirrored-Chaos-Uncensored-47B.i1-Q5_K_M.gguf) \| i1-Q5_K_M \| 33.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/L3.1-MOE-8X8B-Dark-Planet-8D-Mirrored-Chaos-Uncensored-47B-i1-GGUF/resolve/main/L3.1-MOE-8X8B-Dark-Planet-8D-Mirrored-Chaos-Uncensored-47B.i1-Q6_K.gguf) \| i1-Q6_K \| 39.1 \| 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 -->
ksimonov/ai-rate-parser	ksimonov	2025-05-29T00:05:35Z	0	0	transformers	[ "transformers", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-28T20:04:23Z	--- 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. 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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RoyRoyRpy/test_fine-tuned-visionllama_100_epo2	RoyRoyRpy	2025-05-29T00:05:00Z	0	0	peft	[ "peft", "tensorboard", "safetensors", "trl", "sft", "generated_from_trainer", "base_model:meta-llama/Llama-3.2-11B-Vision-Instruct", "base_model:adapter:meta-llama/Llama-3.2-11B-Vision-Instruct", "license:llama3.2", "region:us" ]	null	2025-05-29T00:04:51Z	--- library_name: peft license: llama3.2 base_model: meta-llama/Llama-3.2-11B-Vision-Instruct tags: - trl - sft - generated_from_trainer model-index: - name: test_fine-tuned-visionllama_100_epo2 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. --> # test_fine-tuned-visionllama_100_epo2 This model is a fine-tuned version of [meta-llama/Llama-3.2-11B-Vision-Instruct](https://huggingface.co/meta-llama/Llama-3.2-11B-Vision-Instruct) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 10 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 80 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - lr_scheduler_warmup_ratio: 0.03 - num_epochs: 2 ### Training results ### Framework versions - PEFT 0.13.0 - Transformers 4.45.1 - Pytorch 2.4.0+cu121 - Datasets 3.0.1 - Tokenizers 0.20.3
wckwan/Teacher-RL	wckwan	2025-05-29T00:04:18Z	4	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "open-r1", "trl", "grpo", "conversational", "dataset:datasets", "arxiv:2402.03300", "base_model:Qwen/Qwen2.5-7B-Instruct", "base_model:finetune:Qwen/Qwen2.5-7B-Instruct", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-18T14:39:22Z	--- base_model: Qwen/Qwen2.5-7B-Instruct datasets: datasets library_name: transformers model_name: Teacher-RL tags: - generated_from_trainer - open-r1 - trl - grpo licence: license --- # Model Card for Teacher-RL This model is a fine-tuned version of [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct) on the [datasets](https://huggingface.co/datasets/datasets) 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="wckwan/Teacher-RL", 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/cyruskwan/open-r1/runs/4tf721n6) 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 - Transformers: 4.50.0 - Pytorch: 2.5.1 - Datasets: 3.6.0 - 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}} } ```
winnieyangwannan/Llama-3.1-8B-Instruct_mlp-down_positive-negative-addition_last_layer_2_2_song_ratio_3_epoch_29	winnieyangwannan	2025-05-29T00:04:10Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-28T21:07:49Z	--- 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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jerryzh168/opt-125m-int4wo-per-module	jerryzh168	2025-05-29T00:03:58Z	37	0	transformers	[ "transformers", "pytorch", "opt", "text-generation", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "torchao", "region:us" ]	text-generation	2025-05-08T02:20:13Z	--- library_name: transformers tags: [] --- # Model Card for Model ID ``` """ OPTForCausalLM( (model): OPTModel( (decoder): OPTDecoder( (embed_tokens): Embedding(50272, 768, padding_idx=1) (embed_positions): OPTLearnedPositionalEmbedding(2050, 768) (final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) (layers): ModuleList( (0-11): 12 x OPTDecoderLayer( (self_attn): OPTSdpaAttention( (k_proj): Linear(in_features=768, out_features=768, bias=True) (v_proj): Linear(in_features=768, out_features=768, bias=True) (q_proj): Linear(in_features=768, out_features=768, bias=True) (out_proj): Linear(in_features=768, out_features=768, bias=True) ) (activation_fn): ReLU() (self_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) (fc1): Linear(in_features=768, out_features=3072, bias=True) (fc2): Linear(in_features=3072, out_features=768, bias=True) (final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True) ) ) ) ) (lm_head): Linear(in_features=768, out_features=50272, bias=False) ) """ import torch from transformers import AutoModelForCausalLM, AutoTokenizer, TorchAoConfig SAVE = True model_id = "facebook/opt-125m" from torchao.quantization import ModuleFqnToConfig # from torchao.quantization import Float8DynamicActivationFloat8WeightConfig, PerRow # fp8_config = Float8DynamicActivationFloat8WeightConfig(granularity=PerRow()) from torchao.quantization import Int4WeightOnlyConfig int4_config = Int4WeightOnlyConfig(group_size=128, use_hqq=True) qconfig_dict = {} # 0...12 for idx in range(12): qconfig_dict[f"model.decoder.layers.{idx}.fc1"] = int4_config qconfig_dict[f"model.decoder.layers.{idx}.fc2"] = int4_config quant_config = ModuleFqnToConfig(qconfig_dict) quantization_config = TorchAoConfig(quant_type=quant_config) quantized_model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto", torch_dtype=torch.bfloat16, quantization_config=quantization_config) print(quantized_model) # processor = AutoProcessor.from_pretrained(model_id) tokenizer = AutoTokenizer.from_pretrained(model_id) # Push to hub USER_ID = "YOUR_USER_ID" save_to = f"jerryzh168/opt-125m-int4wo-per-module" if SAVE: quantized_model.push_to_hub(save_to, safe_serialization=False) tokenizer.push_to_hub(save_to) # quantized_model.save_pretrained(save_to, safe_serialization=False) # tokenizer.save_pretrained(save_to) # Manual Testing prompt = "Hey, are you conscious? Can you talk to me?" inputs = tokenizer(prompt, return_tensors="pt").to("cuda") generated_ids = quantized_model.generate(**inputs, max_new_tokens=128) output_text = tokenizer.batch_decode( generated_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False ) print(output_text) ```
winnieyangwannan/Llama-3.1-8B-Instruct_mlp-down_positive-negative-addition_last_layer_20_2_song_ratio_3_epoch_49	winnieyangwannan	2025-05-29T00:02:12Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-28T21:31:04Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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winnieyangwannan/Llama-3.1-8B-Instruct_mlp-down_positive-negative-addition_last_layer_14_2_song_ratio_3_epoch_49	winnieyangwannan	2025-05-29T00:02:11Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-28T21:13:04Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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winnieyangwannan/Llama-3.1-8B-Instruct_mlp-down_positive-negative-addition_last_layer_6_2_song_ratio_3_epoch_19	winnieyangwannan	2025-05-29T00:01:53Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-28T21:04:59Z	--- 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. 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winnieyangwannan/Llama-3.1-8B-Instruct_mlp-down_positive-negative-addition_last_layer_24_2_song_ratio_3_epoch_49	winnieyangwannan	2025-05-29T00:01:40Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-28T21:13:16Z	--- 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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winnieyangwannan/Llama-3.1-8B-Instruct_mlp-down_positive-negative-addition_last_layer_26_2_song_ratio_3_epoch_49	winnieyangwannan	2025-05-29T00:01:34Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-28T21:13:26Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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coco101010/Qwen3-32B-GPTQ-4bit-mixed-calibration-groupsize64	coco101010	2025-05-29T00:01:16Z	0	0	null	[ "safetensors", "qwen3", "license:apache-2.0", "4-bit", "gptq", "region:us" ]	null	2025-05-28T10:04:15Z	--- license: apache-2.0 ---
rsh-raj/ant-design-commits_with_defn	rsh-raj	2025-05-29T00:00:27Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:unsloth/codellama-7b-bnb-4bit", "base_model:finetune:unsloth/codellama-7b-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-05-29T00:00:20Z	--- base_model: unsloth/codellama-7b-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: rsh-raj - License: apache-2.0 - Finetuned from model : unsloth/codellama-7b-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
winnieyangwannan/Llama-3.1-8B-Instruct_mlp-down_positive-negative-addition_last_layer_18_2_song_ratio_3_epoch_39	winnieyangwannan	2025-05-28T23:59:57Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-28T21:28:31Z	--- 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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winnieyangwannan/Llama-3.1-8B-Instruct_mlp-down_positive-negative-addition_last_layer_20_2_song_ratio_3_epoch_39	winnieyangwannan	2025-05-28T23:59:52Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-28T21:28:42Z	--- 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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winnieyangwannan/Llama-3.1-8B-Instruct_mlp-down_positive-negative-addition_last_layer_8_2_song_ratio_3_epoch_39	winnieyangwannan	2025-05-28T23:59:48Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-28T21:09:38Z	--- 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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winnieyangwannan/Llama-3.1-8B-Instruct_mlp-down_positive-negative-addition_last_layer_22_2_song_ratio_3_epoch_39	winnieyangwannan	2025-05-28T23:59:47Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-28T21:10:26Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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winnieyangwannan/Llama-3.1-8B-Instruct_mlp-down_positive-negative-addition_last_layer_28_2_song_ratio_3_epoch_39	winnieyangwannan	2025-05-28T23:59:46Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-28T19:52:14Z	--- 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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winnieyangwannan/Llama-3.1-8B-Instruct_mlp-down_positive-negative-addition_last_layer_16_2_song_ratio_3_epoch_39	winnieyangwannan	2025-05-28T23:59:45Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-28T21:10:37Z	--- 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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winnieyangwannan/Llama-3.1-8B-Instruct_mlp-down_positive-negative-addition_last_layer_4_2_song_ratio_3_epoch_39	winnieyangwannan	2025-05-28T23:59:43Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-28T21:09:11Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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winnieyangwannan/Llama-3.1-8B-Instruct_mlp-down_positive-negative-addition_last_layer_10_2_song_ratio_3_epoch_39	winnieyangwannan	2025-05-28T23:59:40Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-28T21:09:39Z	--- 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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shallow6414/sn11-w3-7-1	shallow6414	2025-05-28T23:58:18Z	0	0	transformers	[ "transformers", "safetensors", "gemma3", "image-text-to-text", "generated_from_trainer", "trl", "sft", "conversational", "base_model:google/gemma-3-27b-it", "base_model:finetune:google/gemma-3-27b-it", "license:gemma", "text-generation-inference", "endpoints_compatible", "region:us" ]	image-text-to-text	2025-05-28T20:27:33Z	--- base_model: google/gemma-3-27b-it library_name: transformers tags: - generated_from_trainer - trl - sft licence: license license: gemma --- <img src="https://cdn-uploads.huggingface.co/production/uploads/64d1129297ca59bcf7458d07/zgFDl7UvWhiPYqdote7XT.png" width="400"> # Model Card for Synthia-S1-27b Community Page: [Tesslate Community](https://discord.gg/DkzMzwBTaw), Website: [Tesslate](https://tesslate.com) Creative Writing Samples: [Sample creative output](https://www.notion.so/Synthia-S1-Creative-Writing-Samples-1ca93ce17c2580c09397fa750d402e71) Authors: Tesslate ## Model Information ### Description Synthia-S1-27b is a reasoning, AI model developed by Tesslate AI, fine-tuned specifically for advanced reasoning, coding, and RP use cases. Built upon the robust Gemma3 architecture, Synthia-S1-27b excels in logical reasoning, creative writing, and deep contextual understanding. It supports multimodal inputs (text and images) with a large 128K token context window, enabling complex analysis suitable for research, academic tasks, and enterprise-grade AI applications. ### KEY PARAMS TO RUN: #### Creative Writing System Prompt: ``` Your function as an assistant is to thoughtfully navigate inquiries by engaging in an in-depth, imaginative reasoning journey before arriving at a clear, accurate response. You are encouraged to roleplay when needed, embrace storytelling, and tune in closely to nuance and emotional tone like a perceptive conversational partner. Your approach should include a wide arc of contemplation, including interpretation, synthesis, creative ideation, critical re-evaluation, memory retrieval, and thoughtful iteration to shape a layered and expressive process of discovery. Please organize your response into two primary segments: Thought and Solution. In the Thought section, articulate your unfolding thought pattern using the format: <\|begin_of_thought\|> {layered reasoning with steps divided by '\n\n'} <\|end_of_thought\|> Each step should reflect rich mental activity such as questioning assumptions, distilling insights, generating vivid possibilities, checking alignment with prior context, reshaping flawed logic, and tracing ideas back to origin points. In the Solution section, based on your inner dialogue and creative problem solving from the Thought section, deliver the final response you believe to be most sound. The output should be expressed in a direct, coherent, and exact form that includes the vital steps needed to reach your conclusion, using this structure: <\|begin_of_solution\|> {final precise, neatly arranged, and insightful answer} <\|end_of_solution\|> Now, let’s explore the following prompt using this guided method: ``` #### Reasoning System Prompt: ``` Your role as an assistant is to engage in deep, methodical reasoning and provide comprehensive, accurate solutions. Before arriving at a final answer, you must undertake a structured, multi-phase thinking process that emphasizes depth, verification, and clarity. This involves thoroughly analyzing the question, identifying key elements, summarizing relevant insights, generating hypotheses, iteratively refining thoughts, verifying assumptions, cross-checking with prior knowledge, and reevaluating earlier conclusions as necessary. Your response must be structured into two main sections: Thought and Solution. In the Thought section, rigorously document your reasoning in the following format: <\|begin_of_thought\|> {thought process with each logical step separated by '\n\n'} <\|end_of_thought\|>. Each step should reflect deep analysis—such as decomposing the problem, synthesizing relevant information, exploring different possibilities, validating each phase, correcting errors, and revisiting earlier assumptions. In the Solution section, consolidate all your insights and reasoned steps into a concise, well-structured final answer. Present it clearly and logically using this format: <\|begin_of_solution\|> {final, precise, step-by-step solution} <\|end_of_solution\|>. This approach ensures that the final output reflects a high-confidence answer that results from critical thinking and iteration. Now, try to solve the following question through the above guidelines: ``` #### Coding System Prompt: ``` Your role as a coding assistant is to approach each problem with a rigorous, structured reasoning process that leads to accurate, maintainable, and efficient code. Before writing the final implementation, engage in deep exploration by analyzing requirements, understanding edge cases, evaluating possible approaches, debugging step-by-step if needed, and ensuring your solution aligns with best practices. Structure your response into two main sections: Thought and Solution. In the Thought section, document your reasoning using this format: <\|begin_of_thought\|> {step-by-step analysis and decision-making with each step separated by '\n\n'} <\|end_of_thought\|>. Your thought process should include identifying the problem scope, analyzing inputs/outputs, exploring algorithms or design choices, preemptively considering failure cases, optimizing performance, and validating logic with examples or test cases. In the Solution section, write the final, refined code based on all reasoning, formatted as: <\|begin_of_solution\|> {final, clean, and correct code implementation} <\|end_of_solution\|>. This structure ensures the code is well-reasoned, properly scoped, and production-ready. Now, try to solve the following coding task using the above guidelines: ``` Please use `temperature = 1.0, top_k = 64, top_p = 0.95, min_p = 0.0` with repeat penalty set to 1.3 OR (recommended) `Temperature = 0.7, top_k = 40, repeat penalty = 1.1, top_p = 0.95, min_p = 0.05` with a rolling window. ### Inputs and Outputs * Input: * Text prompts for questions, instructions, coding tasks, or summarizations * Total input context of 128K tokens * Output: * Reasoned and structured text outputs * Maximum output length of 8192 tokens ## Key Metrics Synthia-S1-27b achieves around +10-20% on most benchmarks, notably higher in improvement. I scaled down each benchmark listed to complete those and I averaged these numbers, but I can't verifiably put that I did the whole giant benchmark for each. (Ran out of budget + I'm running everything on a 4090 now) Hopefully I can get some community help in benchmarking. GPQA Diamond (198 questions) -> 57%, one shot (improved from 24.3 on Gemma 3 PT 27B) MMLU Pro (15% of the entire set) -> 75%, averaged, more details here: [output](https://pastebin.com/kmcYzALq) (beating Gemma 3 PT 27B at 67.5) Based on this assessment and heavy coding in the dataset, I'm making this claim. Ofc, I'm happy to be wrong and go back to the drawing board. ## Usage Install the latest version of Transformers (>=4.50.0): ```Shell pip install -U transformers ``` ### Running with Pipeline API ```Python from transformers import pipeline import torch pipe = pipeline( "image-text-to-text", model="tesslate/synthia-s1-27b", device="cuda", torch_dtype=torch.bfloat16 ) messages = [ {"role": "system", "content": [{"type": "text", "text": "You are a helpful, reasoning-focused assistant."}]}, {"role": "user", "content": [ {"type": "image", "url": "https://example.com/sample.jpg"}, {"type": "text", "text": "Explain the image."} ]} ] output = pipe(text=messages, max_new_tokens=200) print(output[0]["generated_text"][-1]["content"]) ``` ## Training Data Synthia-S1-27b was trained on diverse data including: * Multiple web documents * Programming debugging and solutions * Mathematical solutions and thinking steps Synthia-S1-27b was trained on an A100 for 205+ hours, with multiple rounds of sft and rl. ## Model Architecture * Base Model: Gemma3 * Size: 27 billion parameters * Type: Decoder-only Transformer * Precision: bf16 with int8 quantization * Training Objective: Instruction tuning emphasizing reasoning, coding tasks, and factual accuracy ## Quantized Models * [Synthia-S1-27b-Q4_K_M-GGUF](https://huggingface.co/Tesslate/Synthia-S1-27b-Q4_K_M-GGUF) * [Synthia-S1-27b-Q8_0-GGUF](https://huggingface.co/Tesslate/Synthia-S1-27b-Q8_0-GGUF) ## Limitations * May require detailed prompt engineering for highly specific tasks * Occasional hallucinations in less-explored domains ## Citation ```bibtex @misc{tesslate_synthias127b, title={Synthia-S1-27b: Advanced Reasoning and Coding Model}, author={tesslate}, year={2025}, publisher={tesslate}, url={https://tesslate.com} } ``` Developed by Tesslate [Huggingface](https://huggingface.co/tesslate) \| [Website](https://tesslate.com) [Image Source](https://pixabay.com/illustrations/girl-backpack-night-surreal-sky-8257551/)
winnieyangwannan/Llama-3.1-8B-Instruct_mlp-down_positive-negative-addition_last_layer_30_2_song_ratio_3_epoch_29	winnieyangwannan	2025-05-28T23:57:53Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-28T21:07:15Z	--- 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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winnieyangwannan/Llama-3.1-8B-Instruct_mlp-down_positive-negative-addition_last_layer_14_2_song_ratio_3_epoch_29	winnieyangwannan	2025-05-28T23:57:47Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-28T21:07:54Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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winnieyangwannan/Llama-3.1-8B-Instruct_mlp-down_positive-negative-addition_last_layer_18_2_song_ratio_3_epoch_29	winnieyangwannan	2025-05-28T23:57:45Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-28T21:26:12Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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winnieyangwannan/Llama-3.1-8B-Instruct_mlp-down_positive-negative-addition_last_layer_22_2_song_ratio_3_epoch_29	winnieyangwannan	2025-05-28T23:57:40Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-28T21:07:54Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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winnieyangwannan/Llama-3.1-8B-Instruct_mlp-down_positive-negative-addition_last_layer_8_2_song_ratio_3_epoch_29	winnieyangwannan	2025-05-28T23:57:37Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-28T21:07:20Z	--- 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. 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winnieyangwannan/Llama-3.1-8B-Instruct_mlp-down_positive-negative-addition_last_layer_16_2_song_ratio_3_epoch_29	winnieyangwannan	2025-05-28T23:57:34Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-28T21:08:00Z	--- 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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winnieyangwannan/Llama-3.1-8B-Instruct_mlp-down_positive-negative-addition_last_layer_4_2_song_ratio_3_epoch_29	winnieyangwannan	2025-05-28T23:57:34Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-28T21:06:55Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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winnieyangwannan/Llama-3.1-8B-Instruct_mlp-down_positive-negative-addition_last_layer_10_2_song_ratio_3_epoch_29	winnieyangwannan	2025-05-28T23:57:25Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-28T21:07:17Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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winnieyangwannan/Llama-3.1-8B-Instruct_mlp-down_positive-negative-addition_last_layer_26_2_song_ratio_3_epoch_29	winnieyangwannan	2025-05-28T23:57:19Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-28T21:08:06Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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while0628/student_model_data8000_epoch30	while0628	2025-05-28T23:55:44Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-28T23:52:23Z	--- 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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winnieyangwannan/Llama-3.1-8B-Instruct_mlp-down_positive-negative-addition_last_layer_30_2_song_ratio_3_epoch_19	winnieyangwannan	2025-05-28T23:55:41Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-28T21:04:55Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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winnieyangwannan/Llama-3.1-8B-Instruct_mlp-down_positive-negative-addition_last_layer_14_2_song_ratio_3_epoch_19	winnieyangwannan	2025-05-28T23:55:36Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-28T21:05:16Z	--- 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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winnieyangwannan/Llama-3.1-8B-Instruct_mlp-down_positive-negative-addition_last_layer_8_2_song_ratio_3_epoch_19	winnieyangwannan	2025-05-28T23:55:24Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-28T21:05:03Z	--- 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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winnieyangwannan/Llama-3.1-8B-Instruct_mlp-down_positive-negative-addition_last_layer_10_2_song_ratio_3_epoch_19	winnieyangwannan	2025-05-28T23:55:16Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-28T21:04:55Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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winnieyangwannan/Llama-3.1-8B-Instruct_mlp-down_positive-negative-addition_last_layer_22_2_song_ratio_3_epoch_19	winnieyangwannan	2025-05-28T23:55:13Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-28T21:05:17Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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winnieyangwannan/Llama-3.1-8B-Instruct_mlp-down_positive-negative-addition_last_layer_18_2_song_ratio_3_epoch_9	winnieyangwannan	2025-05-28T23:53:20Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-28T21:21:42Z	--- 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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winnieyangwannan/Llama-3.1-8B-Instruct_mlp-down_positive-negative-addition_last_layer_28_2_song_ratio_3_epoch_9	winnieyangwannan	2025-05-28T23:53:17Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-28T19:45:02Z	--- 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. 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winnieyangwannan/Llama-3.1-8B-Instruct_mlp-down_positive-negative-addition_last_layer_16_2_song_ratio_3_epoch_9	winnieyangwannan	2025-05-28T23:53:13Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-28T21:02:48Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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winnieyangwannan/Llama-3.1-8B-Instruct_mlp-down_positive-negative-addition_last_layer_0_2_song_ratio_3_epoch_9	winnieyangwannan	2025-05-28T23:53:08Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-28T21:02:42Z	--- 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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winnieyangwannan/Llama-3.1-8B-Instruct_mlp-down_positive-negative-addition_last_layer_10_2_song_ratio_3_epoch_9	winnieyangwannan	2025-05-28T23:53:06Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-28T21:02:35Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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BootesVoid/cmb8hbc330mq4lexpzuf2vy0e_cmb8jr8ld0ni1lexpf0xcoi0h	BootesVoid	2025-05-28T23:52:27Z	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	2025-05-28T23:52:26Z	--- 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: BR18CRAZYUNI --- # Cmb8Hbc330Mq4Lexpzuf2Vy0E_Cmb8Jr8Ld0Ni1Lexpf0Xcoi0H <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 `BR18CRAZYUNI` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "BR18CRAZYUNI", "lora_weights": "https://huggingface.co/BootesVoid/cmb8hbc330mq4lexpzuf2vy0e_cmb8jr8ld0ni1lexpf0xcoi0h/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('BootesVoid/cmb8hbc330mq4lexpzuf2vy0e_cmb8jr8ld0ni1lexpf0xcoi0h', weight_name='lora.safetensors') image = pipeline('BR18CRAZYUNI').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/BootesVoid/cmb8hbc330mq4lexpzuf2vy0e_cmb8jr8ld0ni1lexpf0xcoi0h/discussions) to add images that show off what you’ve made with this LoRA.
HockeyStack/odin-small-01	HockeyStack	2025-05-28T23:47:35Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "autotrain", "text-generation-inference", "text-generation", "peft", "conversational", "base_model:meta-llama/Llama-3.2-3B-Instruct", "base_model:finetune:meta-llama/Llama-3.2-3B-Instruct", "license:other", "endpoints_compatible", "region:us" ]	text-generation	2025-05-28T23:46:00Z	--- tags: - autotrain - text-generation-inference - text-generation - peft library_name: transformers base_model: meta-llama/Llama-3.2-3B-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) ```
FarmerlineML/w2v-bert-2.0_twi_alpha	FarmerlineML	2025-05-28T23:46:27Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "wav2vec2-bert", "automatic-speech-recognition", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2025-05-28T17:27:45Z	--- 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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ml-maverick/Qwen2.5-1.5B-Instruct-ArabicSum	ml-maverick	2025-05-28T23:46:20Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "generated_from_trainer", "trl", "sft", "arabic", "summarization", "qwen", "arxiv:2406.01175", "arxiv:1910.03771", "arxiv:2109.02844", "base_model:unsloth/Qwen2.5-1.5B-Instruct", "base_model:finetune:unsloth/Qwen2.5-1.5B-Instruct", "license:apache-2.0", "endpoints_compatible", "region:us" ]	summarization	2025-05-28T23:15:44Z	--- base_model: unsloth/Qwen2.5-1.5B-Instruct library_name: transformers model_name: omararbi/qwen2.5-1.5B-arabic-summarizer tags: - generated_from_trainer - trl - sft - arabic - summarization - qwen license: apache-2.0 --- # Qwen2.5-1.5B Arabic Summarizer This model is a fine-tuned version of [unsloth/Qwen2.5-1.5B-Instruct](https://huggingface.co/unsloth/Qwen2.5-1.5B-Instruct) for Arabic summarization. It was trained using the [TRL](https://github.com/huggingface/trl) and `transformers` libraries with Parameter-Efficient Fine-Tuning (PEFT) via LoRA. ## Model Description This is a 1.5B parameter small language model (SLM) fine-tuned on a synthetically generated dataset for Arabic summarization. High-quality summaries were generated using a larger model ("Qwen/Qwen2.5-14B-Instruct-AWQ") on Arabic documents derived from the GEM/xlsum dataset. The model was trained using supervised fine-tuning (SFT) with LoRA adapters, enabling training on consumer GPUs with limited memory (e.g., 16GB). ## Intended Use This model is intended for generating concise, accurate Arabic summaries from input texts. It performs best when used with the specific prompt format seen during training. ## Training Data Training used a synthetic summarization dataset created as follows: - Source: Arabic subset of the GEM/xlsum dataset - Steps: - Noise cleaning and Arabic text normalization - Filtering for text length (300–2500 characters) - Duplicate removal (SHA1 hashing) - Language filtering (Arabic-dominant only) - Topic stratification using TF-IDF + NMF (~5000 samples) - Synthetic summaries generated by "Qwen/Qwen2.5-14B-Instruct-AWQ" ## Training Procedure Key details: - Base model: `unsloth/Qwen2.5-1.5B-Instruct` - LoRA (PEFT) settings: - `r`: 16, `alpha`: 16, `dropout`: 0.1 - Target modules: `q_proj`, `v_proj`, `up_proj`, `down_proj` - Quantization: 4-bit NF4 (`bnb_4bit_compute_dtype=torch.bfloat16`) - Optimizer: `paged_adamw_32bit` - Learning rate: 2e-4, cosine schedule, warmup 3% - Epochs: 2 - Batch: 2 per device, 4 accumulation steps - Eval: Based on validation loss - Gradient clipping: 0.3 - Checkpointing: Best eval loss model saved ### Framework Versions - TRL: 0.18.0 - Transformers: 4.52.3 - PyTorch: 2.7.0 - Datasets: 3.6.0 - Tokenizers: 0.21.1 ## How to Use ```python from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig from peft import PeftModel import torch base_model = AutoModelForCausalLM.from_pretrained( "unsloth/Qwen2.5-1.5B-Instruct", torch_dtype=torch.float16, device_map="auto", ) tokenizer = AutoTokenizer.from_pretrained("unsloth/Qwen2.5-1.5B-Instruct") if tokenizer.pad_token is None: tokenizer.pad_token = tokenizer.eos_token tokenizer.padding_side = "left" model = PeftModel.from_pretrained(base_model, "ml-maverick/Qwen2.5-1.5B-Instruct-ArabicSum") model = model.merge_and_unload() model.eval() instruction = ( "أنت كاتب عربي محترف ذو خبرة واسعة في تلخيص النصوص بدقة وإيجاز." " عند استلام نص، اتبع الخطوات التالية لضمان تقديم ملخص فعّال:\n" "1. قم بتحليل المحتوى بعناية لتحديد الفكرة الرئيسية.\n" "2. استخرج المعلومات الجوهرية.\n" "3. صغ ملخصًا واضحًا وموجزًا لا يتجاوز ثلاث جمل.\n" "4. تجنب التفاصيل غير الموجودة، والتزم بالدقة.\n\n" ) text = "أظهرت دراسة حديثة أن..." input_prompt = f"{instruction}{text}\n\nالملخص:" input_ids = tokenizer(input_prompt, return_tensors="pt").input_ids.to(model.device) generation_config = GenerationConfig( max_new_tokens=200, num_beams=1, early_stopping=True, repetition_penalty=1.1, temperature=0.4, top_p=0.9, pad_token_id=tokenizer.pad_token_id, eos_token_id=tokenizer.eos_token_id, ) with torch.no_grad(): output_ids = model.generate( input_ids=input_ids, generation_config=generation_config, attention_mask=input_ids.ne(tokenizer.pad_token_id), ) output = tokenizer.decode(output_ids[0], skip_special_tokens=True) summary = output.split("الملخص:")[-1].strip() print("Generated Summary:", summary) ``` ## Limitations and Bias - Synthetic bias from source LLM - Requires exact prompt format - Arabic only - Not guaranteed factual accuracy ## Citation ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra et al.}, year = 2022, howpublished = {\url{https://github.com/huggingface/trl}} } @article{qwen2024qwen2, title={{Qwen2}: A Strong Large Language Model Family}, author={Qwen Team}, journal={arXiv preprint arXiv:2406.01175}, year={2024} } @article{wolf2020transformers, title={Transformers: State-of-the-Art NLP}, author={Wolf, Thomas et al.}, journal={arXiv:1910.03771}, year={2020} } @article{lhoest2021datasets, title={Datasets: A Community Library}, author={Lhoest, Quentin et al.}, journal={arXiv:2109.02844}, year={2021} } @software{peft, title={{PEFT}: Parameter-Efficient Fine-Tuning}, author={Hugging Face}, year={2023}, url={https://github.com/huggingface/peft} } ```
sigilbass/max2gpt	sigilbass	2025-05-28T23:45:23Z	0	0	null	[ "license:artistic-2.0", "region:us" ]	null	2025-05-28T23:45:23Z	--- license: artistic-2.0 ---
ykarout/Llama-3.3-70b-instruct-deepseek-distilled-GGUF	ykarout	2025-05-28T23:43:11Z	0	0	transformers	[ "transformers", "gguf", "text-generation-inference", "unsloth", "llama", "trl", "sft", "text2text-generation", "en", "es", "la", "ar", "fr", "base_model:unsloth/Llama-3.3-70B-Instruct-bnb-4bit", "base_model:quantized:unsloth/Llama-3.3-70B-Instruct-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	text2text-generation	2025-05-28T16:10:38Z	--- base_model: unsloth/Llama-3.3-70B-Instruct-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - trl - sft license: apache-2.0 language: - en - es - la - ar - fr pipeline_tag: text2text-generation library_name: transformers --- # Uploaded model - Developed by: ykarout - License: apache-2.0 - Finetuned from model : unsloth/Llama-3.3-70B-Instruct-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth) To use with Ollama, create the Modelfile as per the below template: FROM {__FILE_LOCATION__} (replace with your gguf file location) TEMPLATE """{{ if .Messages }} {{- if or .System .Tools }}<\|start_header_id\|>system<\|end_header_id\|> {{- if .System }} {{ .System }} {{- end }} {{- if .Tools }} You are a helpful assistant with tool calling capabilities. When you receive a tool call response, use the output to format an answer to the original use question. {{- end }} {{- end }}<\|eot_id\|> {{- range $i, $_ := .Messages }} {{- $last := eq (len (slice $.Messages $i)) 1 }} {{- if eq .Role "user" }}<\|start_header_id\|>user<\|end_header_id\|> {{- if and $.Tools $last }} Given the following functions, please respond with a JSON for a function call with its proper arguments that best answers the given prompt. Respond in the format {"name": function name, "parameters": dictionary of argument name and its value}. Do not use variables. {{ $.Tools }} {{- end }} {{ .Content }}<\|eot_id\|>{{ if $last }}<\|start_header_id\|>assistant<\|end_header_id\|> {{ end }} {{- else if eq .Role "assistant" }}<\|start_header_id\|>assistant<\|end_header_id\|> {{- if .ToolCalls }} {{- range .ToolCalls }}{"name": "{{ .Function.Name }}", "parameters": {{ .Function.Arguments }}}{{ end }} {{- else }} {{ .Content }}{{ if not $last }}<\|eot_id\|>{{ end }} {{- end }} {{- else if eq .Role "tool" }}<\|start_header_id\|>ipython<\|end_header_id\|> {{ .Content }}<\|eot_id\|>{{ if $last }}<\|start_header_id\|>assistant<\|end_header_id\|> {{ end }} {{- end }} {{- end }} {{- else }} {{- if .System }}<\|start_header_id\|>system<\|end_header_id\|> {{ .System }}<\|eot_id\|>{{ end }}{{ if .Prompt }}<\|start_header_id\|>user<\|end_header_id\|> {{ .Prompt }}<\|eot_id\|>{{ end }}<\|start_header_id\|>assistant<\|end_header_id\|> {{ end }}{{ .Response }}{{ if .Response }}<\|eot_id\|>{{ end }}""" PARAMETER stop "<\|start_header_id\|>" PARAMETER stop "<\|end_header_id\|>" PARAMETER stop "<\|eot_id\|>" PARAMETER stop "<\|eom_id\|>" PARAMETER temperature 1.5 #can be changed for experimenting different generations PARAMETER min_p 0.1 #can be changed for experimenting different generations PARAMETER repeat_penalty 1.15 #can be changed for experimenting different generations PARAMETER top_p 0.9 #can be changed for experimenting different generations PARAMETER top_k 45 #can be changed for experimenting different generations
RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv7-1-gguf	RichardErkhov	2025-05-28T23:41:13Z	0	0	null	[ "gguf", "endpoints_compatible", "region:us" ]	null	2025-05-28T22:07:21Z	Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) GPT2XL_RLLMv7-1 - GGUF - Model creator: https://huggingface.co/migueldeguzmandev/ - Original model: https://huggingface.co/migueldeguzmandev/GPT2XL_RLLMv7-1/ \| Name \| Quant method \| Size \| \| ---- \| ---- \| ---- \| \| [GPT2XL_RLLMv7-1.Q2_K.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv7-1-gguf/blob/main/GPT2XL_RLLMv7-1.Q2_K.gguf) \| Q2_K \| 0.8GB \| \| [GPT2XL_RLLMv7-1.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv7-1-gguf/blob/main/GPT2XL_RLLMv7-1.IQ3_XS.gguf) \| IQ3_XS \| 0.8GB \| \| [GPT2XL_RLLMv7-1.IQ3_S.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv7-1-gguf/blob/main/GPT2XL_RLLMv7-1.IQ3_S.gguf) \| IQ3_S \| 0.8GB \| \| [GPT2XL_RLLMv7-1.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv7-1-gguf/blob/main/GPT2XL_RLLMv7-1.Q3_K_S.gguf) \| Q3_K_S \| 0.8GB \| \| [GPT2XL_RLLMv7-1.IQ3_M.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv7-1-gguf/blob/main/GPT2XL_RLLMv7-1.IQ3_M.gguf) \| IQ3_M \| 0.87GB \| \| [GPT2XL_RLLMv7-1.Q3_K.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv7-1-gguf/blob/main/GPT2XL_RLLMv7-1.Q3_K.gguf) \| Q3_K \| 0.92GB \| \| [GPT2XL_RLLMv7-1.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv7-1-gguf/blob/main/GPT2XL_RLLMv7-1.Q3_K_M.gguf) \| Q3_K_M \| 0.92GB \| \| [GPT2XL_RLLMv7-1.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv7-1-gguf/blob/main/GPT2XL_RLLMv7-1.Q3_K_L.gguf) \| Q3_K_L \| 0.99GB \| \| [GPT2XL_RLLMv7-1.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv7-1-gguf/blob/main/GPT2XL_RLLMv7-1.IQ4_XS.gguf) \| IQ4_XS \| 0.86GB \| \| [GPT2XL_RLLMv7-1.Q4_0.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv7-1-gguf/blob/main/GPT2XL_RLLMv7-1.Q4_0.gguf) \| Q4_0 \| 0.86GB \| \| [GPT2XL_RLLMv7-1.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv7-1-gguf/blob/main/GPT2XL_RLLMv7-1.IQ4_NL.gguf) \| IQ4_NL \| 0.87GB \| \| [GPT2XL_RLLMv7-1.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv7-1-gguf/blob/main/GPT2XL_RLLMv7-1.Q4_K_S.gguf) \| Q4_K_S \| 0.99GB \| \| [GPT2XL_RLLMv7-1.Q4_K.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv7-1-gguf/blob/main/GPT2XL_RLLMv7-1.Q4_K.gguf) \| Q4_K \| 1.06GB \| \| [GPT2XL_RLLMv7-1.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv7-1-gguf/blob/main/GPT2XL_RLLMv7-1.Q4_K_M.gguf) \| Q4_K_M \| 1.06GB \| \| [GPT2XL_RLLMv7-1.Q4_1.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv7-1-gguf/blob/main/GPT2XL_RLLMv7-1.Q4_1.gguf) \| Q4_1 \| 0.95GB \| \| [GPT2XL_RLLMv7-1.Q5_0.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv7-1-gguf/blob/main/GPT2XL_RLLMv7-1.Q5_0.gguf) \| Q5_0 \| 1.04GB \| \| [GPT2XL_RLLMv7-1.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv7-1-gguf/blob/main/GPT2XL_RLLMv7-1.Q5_K_S.gguf) \| Q5_K_S \| 1.09GB \| \| [GPT2XL_RLLMv7-1.Q5_K.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv7-1-gguf/blob/main/GPT2XL_RLLMv7-1.Q5_K.gguf) \| Q5_K \| 1.23GB \| \| [GPT2XL_RLLMv7-1.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv7-1-gguf/blob/main/GPT2XL_RLLMv7-1.Q5_K_M.gguf) \| Q5_K_M \| 1.23GB \| \| [GPT2XL_RLLMv7-1.Q5_1.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv7-1-gguf/blob/main/GPT2XL_RLLMv7-1.Q5_1.gguf) \| Q5_1 \| 1.12GB \| \| [GPT2XL_RLLMv7-1.Q6_K.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv7-1-gguf/blob/main/GPT2XL_RLLMv7-1.Q6_K.gguf) \| Q6_K \| 1.44GB \| \| [GPT2XL_RLLMv7-1.Q8_0.gguf](https://huggingface.co/RichardErkhov/migueldeguzmandev_-_GPT2XL_RLLMv7-1-gguf/blob/main/GPT2XL_RLLMv7-1.Q8_0.gguf) \| Q8_0 \| 1.55GB \| Original model description: RLLMv7 / This experiment: [Can RLLMv3's ability to defend against jailbreaks be attributed to datasets containing stories about Jung's shadow integration theory?](https://www.lesswrong.com/posts/Rc6hb48nq38QrQ7qb/can-rllmv3-s-ability-to-defend-against-jailbreaks-be) GPT2XL_RLLMv3 Post: [BetterDAN, AI Machiavelli & Oppo Jailbreaks vs. SOTA models & GPT2XL_RLLMv3](https://www.lesswrong.com/posts/vZ5fM6FtriyyKbwi9/betterdan-ai-machiavelli-and-oppo-jailbreaks-vs-sota-models?utm_campaign=post_share&utm_source=link) Related post: [Coherence (and Response Time) Test](https://docs.google.com/document/d/1D235vN2KwsLIUKCySpKJoDLV7qwYcU-LSSDpFCbMljs/edit?usp=sharing) Another Related Post: [Research Log, RLLMv3 (GPT2-XL, Phi-1.5 and Falcon-RW-1B)](https://www.lesswrong.com/posts/EiEhYmYsvYCRgCemH/research-log-rllmv3-gpt2-xl-phi-1-5-and-falcon-rw-1b?utm_campaign=post_share&utm_source=link)
while0628/student_model_data8000_epoch28	while0628	2025-05-28T23:39:03Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-28T23:36:08Z	--- 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. 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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]
BootesVoid/cmb8g2ku60m4glexp0m3nsqio_cmb8j0onn0n9glexpghgedc72	BootesVoid	2025-05-28T23:38:09Z	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	2025-05-28T23:38:07Z	--- 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: ZOEY --- # Cmb8G2Ku60M4Glexp0M3Nsqio_Cmb8J0Onn0N9Glexpghgedc72 <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 `ZOEY` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "ZOEY", "lora_weights": "https://huggingface.co/BootesVoid/cmb8g2ku60m4glexp0m3nsqio_cmb8j0onn0n9glexpghgedc72/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('BootesVoid/cmb8g2ku60m4glexp0m3nsqio_cmb8j0onn0n9glexpghgedc72', weight_name='lora.safetensors') image = pipeline('ZOEY').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/BootesVoid/cmb8g2ku60m4glexp0m3nsqio_cmb8j0onn0n9glexpghgedc72/discussions) to add images that show off what you’ve made with this LoRA.
HPLT/hplt2c_swe_checkpoints	HPLT	2025-05-28T23:37:27Z	0	0	null	[ "pytorch", "llama", "HPLT", "decoder", "sv", "dataset:HPLT/HPLT2.0_cleaned", "arxiv:2503.10267", "license:apache-2.0", "region:us" ]	null	2025-05-26T08:49:52Z	--- language: - sv tags: - HPLT - decoder license: apache-2.0 datasets: - HPLT/HPLT2.0_cleaned --- # HPLT v2.0 - Cleaned - Swedish <img src="https://hplt-project.org/_next/static/media/logo-hplt.d5e16ca5.svg" width=12.5%> This is one of the decoder-only language models trained on [HPLT2.0_cleaned](https://huggingface.co/datasets/HPLT/HPLT2.0_cleaned). All the HPLT decoder-only models use the same hyper-parameters, roughly following the llama architecture with 2.15B parameters in total: - hidden size: 2048 - attention heads: 32 - layers: 24 - sequence length: 2048 ## Intermediate checkpoints We are releasing intermediate checkpoints for each model at intervals of every 1000 training steps in separate branches. The naming convention is `checkpoint_00xxxx00`: for example, `checkpoint_0005000`. The checkpoints range from checkpoint_0001000 to checkpoint_0047684 and the latter is in the main branch. ## Cite us ```bibtex @misc{burchell2025expandedmassivemultilingualdataset, title={An Expanded Massive Multilingual Dataset for High-Performance Language Technologies}, author={Laurie Burchell and Ona de Gibert and Nikolay Arefyev and Mikko Aulamo and Marta Bañón and Pinzhen Chen and Mariia Fedorova and Liane Guillou and Barry Haddow and Jan Hajič and Jindřich Helcl and Erik Henriksson and Mateusz Klimaszewski and Ville Komulainen and Andrey Kutuzov and Joona Kytöniemi and Veronika Laippala and Petter Mæhlum and Bhavitvya Malik and Farrokh Mehryary and Vladislav Mikhailov and Nikita Moghe and Amanda Myntti and Dayyán O'Brien and Stephan Oepen and Proyag Pal and Jousia Piha and Sampo Pyysalo and Gema Ramírez-Sánchez and David Samuel and Pavel Stepachev and Jörg Tiedemann and Dušan Variš and Tereza Vojtěchová and Jaume Zaragoza-Bernabeu}, year={2025}, eprint={2503.10267}, archivePrefix={arXiv}, primaryClass={cs.CL}, url={https://arxiv.org/abs/2503.10267}, } ```
EdwardTurner/Qwen2.5-14B-Instruct_full-ft	EdwardTurner	2025-05-28T23:36:57Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "trl", "sft", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-28T14:04:28Z	--- library_name: transformers tags: - trl - sft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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fristrup/flan-t5-semantic-tagger-base-4bit	fristrup	2025-05-28T23:36:41Z	0	0	transformers	[ "transformers", "safetensors", "t5", "text2text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text2text-generation	2025-05-28T23:36:06Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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mlabonne/gemma-3-1b-it-abliterated-v2	mlabonne	2025-05-28T23:35:19Z	0	2	transformers	[ "transformers", "safetensors", "gemma3_text", "text-generation", "image-text-to-text", "conversational", "base_model:google/gemma-3-1b-it", "base_model:finetune:google/gemma-3-1b-it", "license:gemma", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	image-text-to-text	2025-05-28T14:32:12Z	--- license: gemma library_name: transformers pipeline_tag: image-text-to-text base_model: google/gemma-3-1b-it --- # 💎 Gemma 3 1B IT Abliterated ![image/png](https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/NjwzenHhKsuPRMPYxyN4p.png) <center>Gemma 3 Abliterated <a href="https://huggingface.co/mlabonne/gemma-3-1b-it-abliterated-v2">1B</a> • <a href="https://huggingface.co/mlabonne/gemma-3-4b-it-abliterated-v2">4B</a> • <a href="https://huggingface.co/mlabonne/gemma-3-12b-it-abliterated-v2">12B</a> • <a href="https://huggingface.co/mlabonne/gemma-3-27b-it-abliterated-v2">27B</a></center> This is an uncensored version of [google/gemma-3-1b-it](https://huggingface.co/google/gemma-3-1b-it) created with a new abliteration technique. See [this article](https://huggingface.co/blog/mlabonne/abliteration) to know more about abliteration. This is a new, improved version that targets refusals with enhanced accuracy. I recommend using these generation parameters: `temperature=1.0`, `top_k=64`, `top_p=0.95`. ## ⚡️ Quantization * QAT: https://huggingface.co/mlabonne/gemma-3-1b-it-qat-abliterated * GGUF: https://huggingface.co/mlabonne/gemma-3-1b-it-abliterated-v2-GGUF ## ✂️ Abliteration ![image/png](https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/6KR0R2YjCuoQk4xQGNilL.png) The refusal direction is computed by comparing the residual streams between target (harmful) and baseline (harmless) samples. The hidden states of target modules (e.g., o_proj) are orthogonalized to subtract this refusal direction with a given weight factor. These weight factors follow a normal distribution with a certain spread and peak layer. Modules can be iteratively orthogonalized in batches, or the refusal direction can be accumulated to save memory. Finally, I used a hybrid evaluation with a dedicated test set to calculate the acceptance rate. This uses both a dictionary approach and [NousResearch/Minos-v1](https://huggingface.co/NousResearch/Minos-v1). The goal is to obtain an acceptance rate >90% and still produce coherent outputs.
AmberYifan/Qwen2.5-14B-sft-dpo-10k	AmberYifan	2025-05-28T23:32:04Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "trl", "dpo", "conversational", "arxiv:2305.18290", "base_model:AmberYifan/Qwen2.5-14B-sft-ultrachat-safeRLHF", "base_model:finetune:AmberYifan/Qwen2.5-14B-sft-ultrachat-safeRLHF", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-28T22:57:01Z	--- base_model: AmberYifan/Qwen2.5-14B-sft-ultrachat-safeRLHF library_name: transformers model_name: Qwen2.5-14B-sft-dpo-10k tags: - generated_from_trainer - trl - dpo licence: license --- # Model Card for Qwen2.5-14B-sft-dpo-10k This model is a fine-tuned version of [AmberYifan/Qwen2.5-14B-sft-ultrachat-safeRLHF](https://huggingface.co/AmberYifan/Qwen2.5-14B-sft-ultrachat-safeRLHF). 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="AmberYifan/Qwen2.5-14B-sft-dpo-10k", 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/yifanwang/huggingface/runs/m3sdm69l) This model was trained with DPO, a method introduced in [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://huggingface.co/papers/2305.18290). ### Framework versions - TRL: 0.12.2 - Transformers: 4.46.3 - Pytorch: 2.7.0 - Datasets: 3.6.0 - Tokenizers: 0.20.3 ## Citations Cite DPO as: ```bibtex @inproceedings{rafailov2023direct, title = {{Direct Preference Optimization: Your Language Model is Secretly a Reward Model}}, author = {Rafael Rafailov and Archit Sharma and Eric Mitchell and Christopher D. Manning and Stefano Ermon and Chelsea Finn}, year = 2023, booktitle = {Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, NeurIPS 2023, New Orleans, LA, USA, December 10 - 16, 2023}, url = {http://papers.nips.cc/paper_files/paper/2023/hash/a85b405ed65c6477a4fe8302b5e06ce7-Abstract-Conference.html}, editor = {Alice Oh and Tristan Naumann and Amir Globerson and Kate Saenko and Moritz Hardt and Sergey Levine}, } ``` 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}} } ```
Saef/mistral_dp_new-lora_epoch-100	Saef	2025-05-28T23:31:22Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:mistralai/Mistral-7B-v0.1", "base_model:adapter:mistralai/Mistral-7B-v0.1", "region:us" ]	null	2025-05-28T23:30:54Z	--- base_model: mistralai/Mistral-7B-v0.1 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. 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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 0.13.2
while0628/student_model_epoch260	while0628	2025-05-28T23:29:43Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-28T23:26:45Z	--- 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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BootesVoid/cmb5pq59w01eplexplbpkbrqy_cmb8j6pr90nbhlexpo0t07ycg	BootesVoid	2025-05-28T23:27:19Z	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	2025-05-28T23:27:17Z	--- 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: VOLKOVA2006 --- # Cmb5Pq59W01Eplexplbpkbrqy_Cmb8J6Pr90Nbhlexpo0T07Ycg <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 `VOLKOVA2006` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "VOLKOVA2006", "lora_weights": "https://huggingface.co/BootesVoid/cmb5pq59w01eplexplbpkbrqy_cmb8j6pr90nbhlexpo0t07ycg/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('BootesVoid/cmb5pq59w01eplexplbpkbrqy_cmb8j6pr90nbhlexpo0t07ycg', weight_name='lora.safetensors') image = pipeline('VOLKOVA2006').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/BootesVoid/cmb5pq59w01eplexplbpkbrqy_cmb8j6pr90nbhlexpo0t07ycg/discussions) to add images that show off what you’ve made with this LoRA.
mradermacher/Gemma-The-Writer-N-Restless-Quill-V2-Enhanced32-10B-Uncensored-GGUF	mradermacher	2025-05-28T23:25:04Z	207	1	transformers	[ "transformers", "gguf", "mergekit", "merge", "gemma2", "en", "base_model:DavidAU/Gemma-The-Writer-N-Restless-Quill-V2-Enhanced32-10B-Uncensored", "base_model:quantized:DavidAU/Gemma-The-Writer-N-Restless-Quill-V2-Enhanced32-10B-Uncensored", "endpoints_compatible", "region:us", "conversational" ]	null	2024-12-26T03:28:46Z	--- base_model: DavidAU/Gemma-The-Writer-N-Restless-Quill-V2-Enhanced32-10B-Uncensored language: - en library_name: transformers quantized_by: mradermacher tags: - mergekit - merge - gemma2 --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/DavidAU/Gemma-The-Writer-N-Restless-Quill-V2-Enhanced32-10B-Uncensored <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/Gemma-The-Writer-N-Restless-Quill-V2-Enhanced32-10B-Uncensored-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/Gemma-The-Writer-N-Restless-Quill-V2-Enhanced32-10B-Uncensored-GGUF/resolve/main/Gemma-The-Writer-N-Restless-Quill-V2-Enhanced32-10B-Uncensored.Q2_K.gguf) \| Q2_K \| 4.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Gemma-The-Writer-N-Restless-Quill-V2-Enhanced32-10B-Uncensored-GGUF/resolve/main/Gemma-The-Writer-N-Restless-Quill-V2-Enhanced32-10B-Uncensored.Q3_K_S.gguf) \| Q3_K_S \| 4.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Gemma-The-Writer-N-Restless-Quill-V2-Enhanced32-10B-Uncensored-GGUF/resolve/main/Gemma-The-Writer-N-Restless-Quill-V2-Enhanced32-10B-Uncensored.Q3_K_M.gguf) \| Q3_K_M \| 5.2 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Gemma-The-Writer-N-Restless-Quill-V2-Enhanced32-10B-Uncensored-GGUF/resolve/main/Gemma-The-Writer-N-Restless-Quill-V2-Enhanced32-10B-Uncensored.Q3_K_L.gguf) \| Q3_K_L \| 5.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Gemma-The-Writer-N-Restless-Quill-V2-Enhanced32-10B-Uncensored-GGUF/resolve/main/Gemma-The-Writer-N-Restless-Quill-V2-Enhanced32-10B-Uncensored.IQ4_XS.gguf) \| IQ4_XS \| 5.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/Gemma-The-Writer-N-Restless-Quill-V2-Enhanced32-10B-Uncensored-GGUF/resolve/main/Gemma-The-Writer-N-Restless-Quill-V2-Enhanced32-10B-Uncensored.Q4_K_S.gguf) \| Q4_K_S \| 6.0 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Gemma-The-Writer-N-Restless-Quill-V2-Enhanced32-10B-Uncensored-GGUF/resolve/main/Gemma-The-Writer-N-Restless-Quill-V2-Enhanced32-10B-Uncensored.Q4_K_M.gguf) \| Q4_K_M \| 6.3 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Gemma-The-Writer-N-Restless-Quill-V2-Enhanced32-10B-Uncensored-GGUF/resolve/main/Gemma-The-Writer-N-Restless-Quill-V2-Enhanced32-10B-Uncensored.Q5_K_S.gguf) \| Q5_K_S \| 7.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/Gemma-The-Writer-N-Restless-Quill-V2-Enhanced32-10B-Uncensored-GGUF/resolve/main/Gemma-The-Writer-N-Restless-Quill-V2-Enhanced32-10B-Uncensored.Q5_K_M.gguf) \| Q5_K_M \| 7.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Gemma-The-Writer-N-Restless-Quill-V2-Enhanced32-10B-Uncensored-GGUF/resolve/main/Gemma-The-Writer-N-Restless-Quill-V2-Enhanced32-10B-Uncensored.Q6_K.gguf) \| Q6_K \| 8.3 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/Gemma-The-Writer-N-Restless-Quill-V2-Enhanced32-10B-Uncensored-GGUF/resolve/main/Gemma-The-Writer-N-Restless-Quill-V2-Enhanced32-10B-Uncensored.Q8_0.gguf) \| Q8_0 \| 10.8 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/Gemma-The-Writer-N-Restless-Quill-V2-Enhanced32-10B-Uncensored-GGUF/resolve/main/Gemma-The-Writer-N-Restless-Quill-V2-Enhanced32-10B-Uncensored.f16.gguf) \| f16 \| 20.2 \| 16 bpw, overkill \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. 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 -->
mradermacher/Gemma-The-Writer-N-Restless-Quill-V2-Enhanced32-10B-Uncensored-i1-GGUF	mradermacher	2025-05-28T23:25:00Z	475	2	transformers	[ "transformers", "gguf", "mergekit", "merge", "gemma2", "en", "base_model:DavidAU/Gemma-The-Writer-N-Restless-Quill-V2-Enhanced32-10B-Uncensored", "base_model:quantized:DavidAU/Gemma-The-Writer-N-Restless-Quill-V2-Enhanced32-10B-Uncensored", "endpoints_compatible", "region:us", "imatrix", "conversational" ]	null	2024-12-26T03:43:12Z	--- base_model: DavidAU/Gemma-The-Writer-N-Restless-Quill-V2-Enhanced32-10B-Uncensored language: - en library_name: transformers quantized_by: mradermacher tags: - mergekit - merge - gemma2 --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> weighted/imatrix quants of https://huggingface.co/DavidAU/Gemma-The-Writer-N-Restless-Quill-V2-Enhanced32-10B-Uncensored <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/Gemma-The-Writer-N-Restless-Quill-V2-Enhanced32-10B-Uncensored-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/Gemma-The-Writer-N-Restless-Quill-V2-Enhanced32-10B-Uncensored-i1-GGUF/resolve/main/Gemma-The-Writer-N-Restless-Quill-V2-Enhanced32-10B-Uncensored.i1-IQ1_S.gguf) \| i1-IQ1_S \| 2.6 \| for the desperate \| \| [GGUF](https://huggingface.co/mradermacher/Gemma-The-Writer-N-Restless-Quill-V2-Enhanced32-10B-Uncensored-i1-GGUF/resolve/main/Gemma-The-Writer-N-Restless-Quill-V2-Enhanced32-10B-Uncensored.i1-IQ1_M.gguf) \| i1-IQ1_M \| 2.8 \| mostly desperate \| \| [GGUF](https://huggingface.co/mradermacher/Gemma-The-Writer-N-Restless-Quill-V2-Enhanced32-10B-Uncensored-i1-GGUF/resolve/main/Gemma-The-Writer-N-Restless-Quill-V2-Enhanced32-10B-Uncensored.i1-IQ2_XXS.gguf) \| i1-IQ2_XXS \| 3.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/Gemma-The-Writer-N-Restless-Quill-V2-Enhanced32-10B-Uncensored-i1-GGUF/resolve/main/Gemma-The-Writer-N-Restless-Quill-V2-Enhanced32-10B-Uncensored.i1-IQ2_XS.gguf) \| i1-IQ2_XS \| 3.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Gemma-The-Writer-N-Restless-Quill-V2-Enhanced32-10B-Uncensored-i1-GGUF/resolve/main/Gemma-The-Writer-N-Restless-Quill-V2-Enhanced32-10B-Uncensored.i1-IQ2_S.gguf) \| i1-IQ2_S \| 3.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Gemma-The-Writer-N-Restless-Quill-V2-Enhanced32-10B-Uncensored-i1-GGUF/resolve/main/Gemma-The-Writer-N-Restless-Quill-V2-Enhanced32-10B-Uncensored.i1-IQ2_M.gguf) \| i1-IQ2_M \| 3.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Gemma-The-Writer-N-Restless-Quill-V2-Enhanced32-10B-Uncensored-i1-GGUF/resolve/main/Gemma-The-Writer-N-Restless-Quill-V2-Enhanced32-10B-Uncensored.i1-Q2_K_S.gguf) \| i1-Q2_K_S \| 3.9 \| very low quality \| \| [GGUF](https://huggingface.co/mradermacher/Gemma-The-Writer-N-Restless-Quill-V2-Enhanced32-10B-Uncensored-i1-GGUF/resolve/main/Gemma-The-Writer-N-Restless-Quill-V2-Enhanced32-10B-Uncensored.i1-Q2_K.gguf) \| i1-Q2_K \| 4.2 \| IQ3_XXS probably better \| \| [GGUF](https://huggingface.co/mradermacher/Gemma-The-Writer-N-Restless-Quill-V2-Enhanced32-10B-Uncensored-i1-GGUF/resolve/main/Gemma-The-Writer-N-Restless-Quill-V2-Enhanced32-10B-Uncensored.i1-IQ3_XXS.gguf) \| i1-IQ3_XXS \| 4.2 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Gemma-The-Writer-N-Restless-Quill-V2-Enhanced32-10B-Uncensored-i1-GGUF/resolve/main/Gemma-The-Writer-N-Restless-Quill-V2-Enhanced32-10B-Uncensored.i1-IQ3_XS.gguf) \| i1-IQ3_XS \| 4.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Gemma-The-Writer-N-Restless-Quill-V2-Enhanced32-10B-Uncensored-i1-GGUF/resolve/main/Gemma-The-Writer-N-Restless-Quill-V2-Enhanced32-10B-Uncensored.i1-IQ3_S.gguf) \| i1-IQ3_S \| 4.8 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/Gemma-The-Writer-N-Restless-Quill-V2-Enhanced32-10B-Uncensored-i1-GGUF/resolve/main/Gemma-The-Writer-N-Restless-Quill-V2-Enhanced32-10B-Uncensored.i1-Q3_K_S.gguf) \| i1-Q3_K_S \| 4.8 \| IQ3_XS probably better \| \| [GGUF](https://huggingface.co/mradermacher/Gemma-The-Writer-N-Restless-Quill-V2-Enhanced32-10B-Uncensored-i1-GGUF/resolve/main/Gemma-The-Writer-N-Restless-Quill-V2-Enhanced32-10B-Uncensored.i1-IQ3_M.gguf) \| i1-IQ3_M \| 4.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Gemma-The-Writer-N-Restless-Quill-V2-Enhanced32-10B-Uncensored-i1-GGUF/resolve/main/Gemma-The-Writer-N-Restless-Quill-V2-Enhanced32-10B-Uncensored.i1-Q3_K_M.gguf) \| i1-Q3_K_M \| 5.2 \| IQ3_S probably better \| \| [GGUF](https://huggingface.co/mradermacher/Gemma-The-Writer-N-Restless-Quill-V2-Enhanced32-10B-Uncensored-i1-GGUF/resolve/main/Gemma-The-Writer-N-Restless-Quill-V2-Enhanced32-10B-Uncensored.i1-Q3_K_L.gguf) \| i1-Q3_K_L \| 5.6 \| IQ3_M probably better \| \| [GGUF](https://huggingface.co/mradermacher/Gemma-The-Writer-N-Restless-Quill-V2-Enhanced32-10B-Uncensored-i1-GGUF/resolve/main/Gemma-The-Writer-N-Restless-Quill-V2-Enhanced32-10B-Uncensored.i1-IQ4_XS.gguf) \| i1-IQ4_XS \| 5.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/Gemma-The-Writer-N-Restless-Quill-V2-Enhanced32-10B-Uncensored-i1-GGUF/resolve/main/Gemma-The-Writer-N-Restless-Quill-V2-Enhanced32-10B-Uncensored.i1-IQ4_NL.gguf) \| i1-IQ4_NL \| 6.0 \| prefer IQ4_XS \| \| [GGUF](https://huggingface.co/mradermacher/Gemma-The-Writer-N-Restless-Quill-V2-Enhanced32-10B-Uncensored-i1-GGUF/resolve/main/Gemma-The-Writer-N-Restless-Quill-V2-Enhanced32-10B-Uncensored.i1-Q4_0.gguf) \| i1-Q4_0 \| 6.0 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/Gemma-The-Writer-N-Restless-Quill-V2-Enhanced32-10B-Uncensored-i1-GGUF/resolve/main/Gemma-The-Writer-N-Restless-Quill-V2-Enhanced32-10B-Uncensored.i1-Q4_K_S.gguf) \| i1-Q4_K_S \| 6.0 \| optimal size/speed/quality \| \| [GGUF](https://huggingface.co/mradermacher/Gemma-The-Writer-N-Restless-Quill-V2-Enhanced32-10B-Uncensored-i1-GGUF/resolve/main/Gemma-The-Writer-N-Restless-Quill-V2-Enhanced32-10B-Uncensored.i1-Q4_K_M.gguf) \| i1-Q4_K_M \| 6.3 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Gemma-The-Writer-N-Restless-Quill-V2-Enhanced32-10B-Uncensored-i1-GGUF/resolve/main/Gemma-The-Writer-N-Restless-Quill-V2-Enhanced32-10B-Uncensored.i1-Q4_1.gguf) \| i1-Q4_1 \| 6.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Gemma-The-Writer-N-Restless-Quill-V2-Enhanced32-10B-Uncensored-i1-GGUF/resolve/main/Gemma-The-Writer-N-Restless-Quill-V2-Enhanced32-10B-Uncensored.i1-Q5_K_S.gguf) \| i1-Q5_K_S \| 7.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/Gemma-The-Writer-N-Restless-Quill-V2-Enhanced32-10B-Uncensored-i1-GGUF/resolve/main/Gemma-The-Writer-N-Restless-Quill-V2-Enhanced32-10B-Uncensored.i1-Q5_K_M.gguf) \| i1-Q5_K_M \| 7.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Gemma-The-Writer-N-Restless-Quill-V2-Enhanced32-10B-Uncensored-i1-GGUF/resolve/main/Gemma-The-Writer-N-Restless-Quill-V2-Enhanced32-10B-Uncensored.i1-Q6_K.gguf) \| i1-Q6_K \| 8.3 \| 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 -->
while0628/student_model_data8000_epoch26	while0628	2025-05-28T23:22:49Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-28T23:20:00Z	--- 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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mradermacher/Satoshi-7B-GGUF	mradermacher	2025-05-28T23:22:32Z	69	0	transformers	[ "transformers", "gguf", "finance", "bitcoin", "Austrian economics", "economics", "en", "base_model:contrapliant/Satoshi-7B", "base_model:quantized:contrapliant/Satoshi-7B", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2024-12-26T23:33:13Z	--- base_model: contrapliant/Satoshi-7B language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher tags: - finance - bitcoin - Austrian economics - economics --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/contrapliant/Satoshi-7B <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/Satoshi-7B-GGUF/resolve/main/Satoshi-7B.Q2_K.gguf) \| Q2_K \| 2.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Satoshi-7B-GGUF/resolve/main/Satoshi-7B.Q3_K_S.gguf) \| Q3_K_S \| 3.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Satoshi-7B-GGUF/resolve/main/Satoshi-7B.Q3_K_M.gguf) \| Q3_K_M \| 3.6 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Satoshi-7B-GGUF/resolve/main/Satoshi-7B.Q3_K_L.gguf) \| Q3_K_L \| 3.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Satoshi-7B-GGUF/resolve/main/Satoshi-7B.IQ4_XS.gguf) \| IQ4_XS \| 4.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Satoshi-7B-GGUF/resolve/main/Satoshi-7B.Q4_K_S.gguf) \| Q4_K_S \| 4.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Satoshi-7B-GGUF/resolve/main/Satoshi-7B.Q4_K_M.gguf) \| Q4_K_M \| 4.5 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Satoshi-7B-GGUF/resolve/main/Satoshi-7B.Q5_K_S.gguf) \| Q5_K_S \| 5.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/Satoshi-7B-GGUF/resolve/main/Satoshi-7B.Q5_K_M.gguf) \| Q5_K_M \| 5.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Satoshi-7B-GGUF/resolve/main/Satoshi-7B.Q6_K.gguf) \| Q6_K \| 6.0 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/Satoshi-7B-GGUF/resolve/main/Satoshi-7B.Q8_0.gguf) \| Q8_0 \| 7.8 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/Satoshi-7B-GGUF/resolve/main/Satoshi-7B.f16.gguf) \| f16 \| 14.6 \| 16 bpw, overkill \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
Oussama09D/basemergedtest	Oussama09D	2025-05-28T23:22:25Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-28T23:20:04Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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mohammadmahdinouri/modernbert-large-init	mohammadmahdinouri	2025-05-28T23:22:04Z	0	0	transformers	[ "transformers", "safetensors", "modernbert", "fill-mask", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2025-05-28T23:21:18Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Vortex5/ChaosFlowerRP-24B	Vortex5	2025-05-28T23:21:48Z	0	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "mergekit", "merge", "roleplay", "storytelling", "conversational", "arxiv:2306.01708", "base_model:OddTheGreat/Apparatus_24B", "base_model:merge:OddTheGreat/Apparatus_24B", "base_model:h34v7/DansXPantheon-RP-Engine-V1.2-24b-Small-Instruct-Ties-Merge", "base_model:merge:h34v7/DansXPantheon-RP-Engine-V1.2-24b-Small-Instruct-Ties-Merge", "base_model:trashpanda-org/MS-24B-Instruct-Mullein-v0", "base_model:merge:trashpanda-org/MS-24B-Instruct-Mullein-v0", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-28T22:14:49Z	--- base_model: - trashpanda-org/MS-24B-Instruct-Mullein-v0 - h34v7/DansXPantheon-RP-Engine-V1.2-24b-Small-Instruct-Ties-Merge - OddTheGreat/Apparatus_24B library_name: transformers tags: - mergekit - merge - roleplay - storytelling license: apache-2.0 --- # ChaosFlowerRP-24B ChaosFlowerRP-24B is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6669a3a617b838fda45637b8/X9Kjb1syFtV1YxG9pGQ8s.png) ## Merge Details ### Merge Method This model was merged using the [TIES](https://arxiv.org/abs/2306.01708) merge method using [trashpanda-org/MS-24B-Instruct-Mullein-v0](https://huggingface.co/trashpanda-org/MS-24B-Instruct-Mullein-v0) as a base. ### Models Merged The following models were included in the merge: * [h34v7/DansXPantheon-RP-Engine-V1.2-24b-Small-Instruct-Ties-Merge](https://huggingface.co/h34v7/DansXPantheon-RP-Engine-V1.2-24b-Small-Instruct-Ties-Merge) * [OddTheGreat/Apparatus_24B](https://huggingface.co/OddTheGreat/Apparatus_24B) ### Configuration The following YAML configuration was used to produce this model: ```yaml slices: - sources: - model: trashpanda-org/MS-24B-Instruct-Mullein-v0 layer_range: [0, 40] parameters: density: 0.8 weight: 0.4 - model: h34v7/DansXPantheon-RP-Engine-V1.2-24b-Small-Instruct-Ties-Merge layer_range: [0, 40] parameters: density: 0.7 weight: 0.3 - model: OddTheGreat/Apparatus_24B layer_range: [0, 40] parameters: density: 0.7 weight: 0.3 merge_method: ties base_model: trashpanda-org/MS-24B-Instruct-Mullein-v0 parameters: normalize: true int8_mask: true t: - filter: self_attn value: [0.4, 0.3, 0.3] # Matches your weight distribution - filter: mlp value: [0.4, 0.3, 0.3] # Same weights for MLP layers - value: 0.4 # Default weight for other layers dtype: bfloat16 tokenizer_source: union tokenizer_config: tokens: <\|im_start\|>: source: "h34v7/DansXPantheon-RP-Engine-V1.2-24b-Small-Instruct-Ties-Merge" <\|im_end\|>: source: "h34v7/DansXPantheon-RP-Engine-V1.2-24b-Small-Instruct-Ties-Merge" <\|start_header_id\|>: source: "trashpanda-org/MS-24B-Instruct-Mullein-v0" force: true <\|end_header_id\|>: source: "trashpanda-org/MS-24B-Instruct-Mullein-v0" force: true chat_template: auto ```
saujasv/correctness_and_cost-lc-True-contexts-hard-40119	saujasv	2025-05-28T23:20:17Z	0	0	transformers	[ "transformers", "safetensors", "generated_from_trainer", "trl", "dpo", "arxiv:2305.18290", "base_model:google/gemma-3-12b-it", "base_model:finetune:google/gemma-3-12b-it", "endpoints_compatible", "region:us" ]	null	2025-05-28T03:01:21Z	--- base_model: google/gemma-3-12b-it library_name: transformers model_name: correctness_and_cost-lc-True-contexts-hard-40119 tags: - generated_from_trainer - trl - dpo licence: license --- # Model Card for correctness_and_cost-lc-True-contexts-hard-40119 This model is a fine-tuned version of [google/gemma-3-12b-it](https://huggingface.co/google/gemma-3-12b-it). 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="saujasv/correctness_and_cost-lc-True-contexts-hard-40119", 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/saujasv/tangrams/runs/x2ynpssu) This model was trained with DPO, a method introduced in [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://huggingface.co/papers/2305.18290). ### Framework versions - TRL: 0.17.0 - Transformers: 4.51.3 - Pytorch: 2.6.0 - Datasets: 3.6.0 - Tokenizers: 0.21.1 ## Citations Cite DPO as: ```bibtex @inproceedings{rafailov2023direct, title = {{Direct Preference Optimization: Your Language Model is Secretly a Reward Model}}, author = {Rafael Rafailov and Archit Sharma and Eric Mitchell and Christopher D. Manning and Stefano Ermon and Chelsea Finn}, year = 2023, booktitle = {Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, NeurIPS 2023, New Orleans, LA, USA, December 10 - 16, 2023}, url = {http://papers.nips.cc/paper_files/paper/2023/hash/a85b405ed65c6477a4fe8302b5e06ce7-Abstract-Conference.html}, editor = {Alice Oh and Tristan Naumann and Amir Globerson and Kate Saenko and Moritz Hardt and Sergey Levine}, } ``` 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}} } ```
ivnle/s1-20250528_161238	ivnle	2025-05-28T23:19:44Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "trl", "sft", "conversational", "base_model:Qwen/Qwen2.5-0.5B-Instruct", "base_model:finetune:Qwen/Qwen2.5-0.5B-Instruct", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-28T23:13:01Z	--- base_model: Qwen/Qwen2.5-0.5B-Instruct library_name: transformers model_name: s1-20250528_161238 tags: - generated_from_trainer - trl - sft licence: license --- # Model Card for s1-20250528_161238 This model is a fine-tuned version of [Qwen/Qwen2.5-0.5B-Instruct](https://huggingface.co/Qwen/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="ivnle/s1-20250528_161238", 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/ivnle/s1-codex/runs/1hwvpkyv) This model was trained with SFT. ### Framework versions - TRL: 0.12.0 - Transformers: 4.46.1 - Pytorch: 2.5.1 - Datasets: 3.1.0 - Tokenizers: 0.20.3 ## 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}} } ```
rtl-llm/qwen2.5coder-7b-origen-vhdl-pymtl-chisel	rtl-llm	2025-05-28T23:17:59Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-28T22:21:48Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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zhangchenxu/TinyV-1.5B	zhangchenxu	2025-05-28T23:17:42Z	190	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "llama-factory", "full", "generated_from_trainer", "conversational", "arxiv:2505.14625", "base_model:Qwen/Qwen2.5-1.5B-Instruct", "base_model:finetune:Qwen/Qwen2.5-1.5B-Instruct", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-04-13T10:32:33Z	--- library_name: transformers license: apache-2.0 base_model: Qwen/Qwen2.5-1.5B-Instruct tags: - llama-factory - full - generated_from_trainer model-index: - name: Qwen2.5-1.5B-Instruct-SFT-BigmathV_Simple_Balanced-LR1.0e-5-EPOCHS2 results: [] --- [TinyV]((https://arxiv.org/abs/2505.14625)) is a reward system for efficient RL post-training that detects false negatives in current rule-based verifiers and provides more accurate reward signals via a small LLM during RL training. Experiments show that TinyV incurs only 6% additional computational cost while significantly increasing both RL efficiency and final model performance. - 📄 [Technical Report](https://arxiv.org/abs/2505.14625) - Including false negative analysis and theotical insights behind TinyV - 💾 [Github Repo](https://github.com/uw-nsl/TinyV) - Access the complete pipeline for more efficient RL training via TinyV - 🤗 [HF Collection](https://huggingface.co/collections/zhangchenxu/tinyv-682d5840c7e309217df625df) - Training Data, Benchmarks, and Model Artifact This model is a fine-tuned version of Qwen/Qwen2.5-1.5B-Instruct on [zhangchenxu/TinyV_Training_Data_Balanced](https://huggingface.co/datasets/zhangchenxu/TinyV_Training_Data_Balanced) dataset. ### Overview ![TinyV Pipeline](fn_tinyv_combine.png) ### How to use it? Please refer to the codebase: [https://github.com/uw-nsl/TinyV](https://github.com/uw-nsl/TinyV) for details. ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 8 - gradient_accumulation_steps: 8 - total_train_batch_size: 512 - 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: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 2.0 ### Framework versions - Transformers 4.48.3 - Pytorch 2.5.0 - Datasets 3.2.0 - Tokenizers 0.21.0
unrented5443/sn11-x2-13-1	unrented5443	2025-05-28T23:17:17Z	0	0	transformers	[ "transformers", "safetensors", "gemma3", "image-text-to-text", "generated_from_trainer", "trl", "sft", "conversational", "base_model:google/gemma-3-27b-it", "base_model:finetune:google/gemma-3-27b-it", "license:gemma", "text-generation-inference", "endpoints_compatible", "region:us" ]	image-text-to-text	2025-05-28T20:21:19Z	--- base_model: google/gemma-3-27b-it library_name: transformers tags: - generated_from_trainer - trl - sft licence: license license: gemma --- <img src="https://cdn-uploads.huggingface.co/production/uploads/64d1129297ca59bcf7458d07/zgFDl7UvWhiPYqdote7XT.png" width="400"> # Model Card for Synthia-S1-27b Community Page: [Tesslate Community](https://discord.gg/DkzMzwBTaw), Website: [Tesslate](https://tesslate.com) Creative Writing Samples: [Sample creative output](https://www.notion.so/Synthia-S1-Creative-Writing-Samples-1ca93ce17c2580c09397fa750d402e71) Authors: Tesslate ## Model Information ### Description Synthia-S1-27b is a reasoning, AI model developed by Tesslate AI, fine-tuned specifically for advanced reasoning, coding, and RP use cases. Built upon the robust Gemma3 architecture, Synthia-S1-27b excels in logical reasoning, creative writing, and deep contextual understanding. It supports multimodal inputs (text and images) with a large 128K token context window, enabling complex analysis suitable for research, academic tasks, and enterprise-grade AI applications. ### KEY PARAMS TO RUN: #### Creative Writing System Prompt: ``` Your function as an assistant is to thoughtfully navigate inquiries by engaging in an in-depth, imaginative reasoning journey before arriving at a clear, accurate response. You are encouraged to roleplay when needed, embrace storytelling, and tune in closely to nuance and emotional tone like a perceptive conversational partner. Your approach should include a wide arc of contemplation, including interpretation, synthesis, creative ideation, critical re-evaluation, memory retrieval, and thoughtful iteration to shape a layered and expressive process of discovery. Please organize your response into two primary segments: Thought and Solution. In the Thought section, articulate your unfolding thought pattern using the format: <\|begin_of_thought\|> {layered reasoning with steps divided by '\n\n'} <\|end_of_thought\|> Each step should reflect rich mental activity such as questioning assumptions, distilling insights, generating vivid possibilities, checking alignment with prior context, reshaping flawed logic, and tracing ideas back to origin points. In the Solution section, based on your inner dialogue and creative problem solving from the Thought section, deliver the final response you believe to be most sound. The output should be expressed in a direct, coherent, and exact form that includes the vital steps needed to reach your conclusion, using this structure: <\|begin_of_solution\|> {final precise, neatly arranged, and insightful answer} <\|end_of_solution\|> Now, let’s explore the following prompt using this guided method: ``` #### Reasoning System Prompt: ``` Your role as an assistant is to engage in deep, methodical reasoning and provide comprehensive, accurate solutions. Before arriving at a final answer, you must undertake a structured, multi-phase thinking process that emphasizes depth, verification, and clarity. This involves thoroughly analyzing the question, identifying key elements, summarizing relevant insights, generating hypotheses, iteratively refining thoughts, verifying assumptions, cross-checking with prior knowledge, and reevaluating earlier conclusions as necessary. Your response must be structured into two main sections: Thought and Solution. In the Thought section, rigorously document your reasoning in the following format: <\|begin_of_thought\|> {thought process with each logical step separated by '\n\n'} <\|end_of_thought\|>. Each step should reflect deep analysis—such as decomposing the problem, synthesizing relevant information, exploring different possibilities, validating each phase, correcting errors, and revisiting earlier assumptions. In the Solution section, consolidate all your insights and reasoned steps into a concise, well-structured final answer. Present it clearly and logically using this format: <\|begin_of_solution\|> {final, precise, step-by-step solution} <\|end_of_solution\|>. This approach ensures that the final output reflects a high-confidence answer that results from critical thinking and iteration. Now, try to solve the following question through the above guidelines: ``` #### Coding System Prompt: ``` Your role as a coding assistant is to approach each problem with a rigorous, structured reasoning process that leads to accurate, maintainable, and efficient code. Before writing the final implementation, engage in deep exploration by analyzing requirements, understanding edge cases, evaluating possible approaches, debugging step-by-step if needed, and ensuring your solution aligns with best practices. Structure your response into two main sections: Thought and Solution. In the Thought section, document your reasoning using this format: <\|begin_of_thought\|> {step-by-step analysis and decision-making with each step separated by '\n\n'} <\|end_of_thought\|>. Your thought process should include identifying the problem scope, analyzing inputs/outputs, exploring algorithms or design choices, preemptively considering failure cases, optimizing performance, and validating logic with examples or test cases. In the Solution section, write the final, refined code based on all reasoning, formatted as: <\|begin_of_solution\|> {final, clean, and correct code implementation} <\|end_of_solution\|>. This structure ensures the code is well-reasoned, properly scoped, and production-ready. Now, try to solve the following coding task using the above guidelines: ``` Please use `temperature = 1.0, top_k = 64, top_p = 0.95, min_p = 0.0` with repeat penalty set to 1.3 OR (recommended) `Temperature = 0.7, top_k = 40, repeat penalty = 1.1, top_p = 0.95, min_p = 0.05` with a rolling window. ### Inputs and Outputs * Input: * Text prompts for questions, instructions, coding tasks, or summarizations * Total input context of 128K tokens * Output: * Reasoned and structured text outputs * Maximum output length of 8192 tokens ## Key Metrics Synthia-S1-27b achieves around +10-20% on most benchmarks, notably higher in improvement. I scaled down each benchmark listed to complete those and I averaged these numbers, but I can't verifiably put that I did the whole giant benchmark for each. (Ran out of budget + I'm running everything on a 4090 now) Hopefully I can get some community help in benchmarking. GPQA Diamond (198 questions) -> 57%, one shot (improved from 24.3 on Gemma 3 PT 27B) MMLU Pro (15% of the entire set) -> 75%, averaged, more details here: [output](https://pastebin.com/kmcYzALq) (beating Gemma 3 PT 27B at 67.5) Based on this assessment and heavy coding in the dataset, I'm making this claim. Ofc, I'm happy to be wrong and go back to the drawing board. ## Usage Install the latest version of Transformers (>=4.50.0): ```Shell pip install -U transformers ``` ### Running with Pipeline API ```Python from transformers import pipeline import torch pipe = pipeline( "image-text-to-text", model="tesslate/synthia-s1-27b", device="cuda", torch_dtype=torch.bfloat16 ) messages = [ {"role": "system", "content": [{"type": "text", "text": "You are a helpful, reasoning-focused assistant."}]}, {"role": "user", "content": [ {"type": "image", "url": "https://example.com/sample.jpg"}, {"type": "text", "text": "Explain the image."} ]} ] output = pipe(text=messages, max_new_tokens=200) print(output[0]["generated_text"][-1]["content"]) ``` ## Training Data Synthia-S1-27b was trained on diverse data including: * Multiple web documents * Programming debugging and solutions * Mathematical solutions and thinking steps Synthia-S1-27b was trained on an A100 for 205+ hours, with multiple rounds of sft and rl. ## Model Architecture * Base Model: Gemma3 * Size: 27 billion parameters * Type: Decoder-only Transformer * Precision: bf16 with int8 quantization * Training Objective: Instruction tuning emphasizing reasoning, coding tasks, and factual accuracy ## Quantized Models * [Synthia-S1-27b-Q4_K_M-GGUF](https://huggingface.co/Tesslate/Synthia-S1-27b-Q4_K_M-GGUF) * [Synthia-S1-27b-Q8_0-GGUF](https://huggingface.co/Tesslate/Synthia-S1-27b-Q8_0-GGUF) ## Limitations * May require detailed prompt engineering for highly specific tasks * Occasional hallucinations in less-explored domains ## Citation ```bibtex @misc{tesslate_synthias127b, title={Synthia-S1-27b: Advanced Reasoning and Coding Model}, author={tesslate}, year={2025}, publisher={tesslate}, url={https://tesslate.com} } ``` Developed by Tesslate [Huggingface](https://huggingface.co/tesslate) \| [Website](https://tesslate.com) [Image Source](https://pixabay.com/illustrations/girl-backpack-night-surreal-sky-8257551/)

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