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AI 模型 449
- Llama-3.2-0.5B-Instruct↗
inference-optimization
This model uses a single `model.safetensors` file containing all weights. The checkpoint structure is identical to the original model, with the standard Llama architecture tensors: - `model.embed_tokens.weight` - `model.layers.*.self_attn.{q,k,v,o}_proj.weight` - `model.layers.*.mlp.{gate,up,down}_proj.weight` - `model.layers.*.{input,post_attention}_layernorm.weight` - `model.norm.weight` 1. Inspected the original model configuration to identify key parameters 2. Created a tiny version by reducing `num_hidden_layers` from 16 to 4 3. Fine-tuned the model on a toy dataset (famous copypastas) to validate learning capability 4. Achieved target perplexity of ~1.02 on the validation text 5. Validated checkpoint structure matches the original model format 6. Confirmed successful loading and inference
- Qwen2.5-Coder-7B-GGUF↗
QuantFactory
license: apache-2.0 license_link: https://huggingface.co/Qwen/Qwen2.5-Coder-7B/blob/main/LICENSE language: - en base_model: - Qwen/Qwen2.5-7B pipeline_tag: text-generation library_name: transformers tags: - code - qwen - qwen-coder - codeqwen This is quantized version of Qwen/Qwen2.5-Coder-7B created using llama.cpp Qwen2.5-Coder is the latest series of Code-Specific Qwen large language models (formerly known as CodeQwen). For Qwen2.5-Coder, we release three base language models and instruction-tuned language models, 1.5, 7 and 32 (coming soon) billion parameters. Qwen2.5-Coder brings the following improvements upon CodeQwen1.5:
- Magistral-Small-2509-MLX-6bit↗
lmstudio-community
_👾 LM Studio Community models highlights program. Highlighting new & noteworthy models by the community. Join the conversation on Discord_. **Model creator**: mistralai **Original model**: Magistral-Small-2509 **MLX quantization**: provided by LM Studio team using mlx_vlm LM Studio is not the creator, originator, or owner of any Model featured in the Community Model Program. Each Community Model is created and provided by third parties. LM Studio does not endorse, support, represent or guarantee the completeness, truthfulness, accuracy, or reliability of any Community Model. You understand that Community Models can produce content that might be offensive, harmful, inaccurate or otherwise inappropriate, or deceptive. Each Community Model is the sole responsibility of the person or entity who originated such Model. LM Studio may not monitor or control the Community Models and cannot, and does not, take responsibility for any such Model. LM Studio disclaims all warranties or guarantees about the accuracy, reliability or benefits of the Community Models. LM Studio further disclaims any warranty that the Community Model will meet your requirements, be secure, uninterrupted or available at any time or location, or error-free, viruses-free, or that any errors will be corrected, or otherwise. You will be solely responsible for any damage resulting from your use of or access to the Community Models, your downloading of any Community Model, or use of any other Community Model provided by or through LM Studio.
- OpenMed-NER-GenomeDetect-PubMed-v2-109M↗
OpenMed
This model is a **state-of-the-art** fine-tuned transformer engineered to deliver **enterprise-grade accuracy** for gene/protein entity recognition - gene and protein mentions. This specialized model excels at identifying and extracting biomedical entities from clinical texts, research papers, and healthcare documents, enabling applications such as **drug interaction detection**, **medication extraction from patient records**, **adverse event monitoring**, **literature mining for drug discovery**, and **biomedical knowledge graph construction** with **production-ready reliability** for clinical and research applications. BC2GM corpus targets gene and protein mention recognition from the BioCreative II Gene Mention task. The BC2GM (BioCreative II Gene Mention) corpus is a foundational dataset for gene and protein name recognition in biomedical literature, created for the BioCreative II challenge. This corpus contains thousands of sentences from MEDLINE abstracts with manually annotated gene and protein mentions, serving as a critical benchmark for genomics and molecular biology NER systems. The dataset addresses the challenging task of identifying gene names, which often have complex nomenclature and ambiguous boundaries. It has been instrumental in advancing automated gene recognition systems used in functional genomics research, gene expression analysis, and molecular biology text mining. The corpus continues to be widely used for training and evaluating biomedical NER models.
- Huihui-Qwable-3.6-27b-abliterated-MTP-GGUF↗
huihui-ai
This is an uncensored version of Mia-AiLab/Qwable-3.6-27b created with abliteration. This ablation was performed entirely using llama.cpp's GGUF model, without relying on Transformers, For the specific ablation method, please refer to cvector-generator.
- Supra2-100M-Instruct↗
SupraLabs
**Supra2-100M Instruct** is a 100M-parameter decoder-only language model pretrained from scratch by **SupraLabs** on 30B tokens of English web text. It uses the **Qwen3** architecture with a custom 32,768-token tokenizer. This is the instruct version of Supra2-100M Base and it was full-finetuned on a single RTX 5060 Ti 16GB. Prompt: "What is AI?" Answer: > AI refers to the application of computer science and artificial intelligence to solve problems and make decisions. It's a field that combines computer science, machine learning, and natural language processing to create new forms of intelligence, such as natural language understanding, speech recognition, and virtual assistants. > > AI is used in various fields, including healthcare, finance, education, and social sciences, to improve efficiency, reduce costs, and enhance human performance. It can also be applied to areas like robotics, artificial intelligence, and cybersecurity. > In the context of AI, it's often used to analyze vast amounts of data, identify patterns, and make predictions. For example, in natural language analysis, AI can help humans understand and interpret language, such that they can make more accurate and informed decisions. > The field of AI is constantly evolving, with new technologies emerging and being developed to address specific challenges. Some examples include: > > Artificial intelligence (AI) is being used in robotics, where it can learn from human movement and adapt to changing environments. > AI-powered virtual assistants, such virtual assistants that can help users interact with machines, such Aspire and Alexa, are being developed. > Artificially intelligent vehicles, such vehicles that can learn and adapt based on user input, are also being developed, such cars and trucks. > Smart home systems, such smart thermostats and smart lighting, are becoming increasingly sophisticated and integrated into homes. > Autonomous vehicles, like autonomous cars and drones, are increasingly used in urban areas and in remote areas. > These are just a few examples, but the field of artificial intelligence is constantly expanding and evolving, and AI is being applied to a wide range of applications. Followup prompt: "Can you give me more information about how it will influence work life?" --> Answer: > The impact of AI on work life will depend on several factors, including the type of work
- Huihui-Qwen3.5-27B-abliterated↗
huihui-ai
This is an uncensored version of Qwen/Qwen3.5-27B created with abliteration (see remove-refusals-with-transformers to know more about it). This is a crude, proof-of-concept implementation to remove refusals from an LLM model without using TransformerLens. Please use the latest version of ollama v0.17.5 You can use huihui_ai/qwen3.5-abliterated directly,
- gpt2-large↗
openai-community
- Model Details - How To Get Started With the Model - Uses - Risks, Limitations and Biases - Training - Evaluation - Environmental Impact - Technical Specifications - Citation Information - Model Card Authors **Model Description:** GPT-2 Large is the **774M parameter** version of GPT-2, a transformer-based language model created and released by OpenAI. The model is a pretrained model on English language using a causal language modeling (CLM) objective. - **Developed by:** OpenAI, see associated research paper and GitHub repo for model developers. - **Model Type:** Transformer-based language model - **Language(s):** English - **License:** Modified MIT License - **Related Models:** GPT-2, GPT-Medium and GPT-XL - **Resources for more information:** - Research Paper - OpenAI Blog Post - GitHub Repo - OpenAI Model Card for GPT-2 - Test the full generation capabilities here: https://transformer.huggingface.co/doc/gpt2-large
- Huihui-gemma-4-12B-agentic-fable5-abliterated-GGUF↗
huihui-ai
This is an uncensored version of yuxinlu1/gemma-4-12B-agentic-fable5-composer2.5-v2-3.5x-tau2-GGUF created with abliteration. If you like it, please click 'like' and follow us for more updates. You can follow x.com/support_huihui to get the latest model information from huihui.ai.
- gemma-3-12b-it-qat-GGUF↗
unsloth
> [!Note] > This repository corresponds to the 12B **instruction-tuned** version of the Gemma 3 model using Quantization Aware Training (QAT). > > **The checkpoint in this repository is unquantized, please make sure to quantize with Q4_0 with your favorite tool** > > Thanks to QAT, the model is able to preserve similar quality as `bfloat16` while significantly reducing the memory requirements > to load the model. * [Gemma 3 Technical Report][g3-tech-report] * [Responsible Generative AI Toolkit][rai-toolkit] * [Gemma on Kaggle][kaggle-gemma] * [Gemma on Vertex Model Garden][vertex-mg-gemma3] Gemma is a family of lightweight, state-of-the-art open models from Google, built from the same research and technology used to create the Gemini models. Gemma 3 models are multimodal, handling text and image input and generating text output, with open weights for both pre-trained variants and instruction-tuned variants. Gemma 3 has a large, 128K context window, multilingual support in over 140 languages, and is available in more sizes than previous versions. Gemma 3 models are well-suited for a variety of text generation and image understanding tasks, including question answering, summarization, and reasoning. Their relatively small size makes it possible to deploy them in environments with limited resources such as laptops, desktops or your own cloud infrastructure, democratizing access to state of the art AI models and helping foster innovation for everyone.