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AI モデル 84759

• mvGPT↗

  nenuadrian

• Llama-2-7b-chat-hf↗

  NousResearch

  Llama 2 is a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 70 billion parameters. This is the repository for the 7B fine-tuned model, optimized for dialogue use cases and converted for the Hugging Face Transformers format. Links to other models can be found in the index at the bottom. Meta developed and publicly released the Llama 2 family of large language models (LLMs), a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 70 billion parameters. Our fine-tuned LLMs, called Llama-2-Chat, are optimized for dialogue use cases. Llama-2-Chat models outperform open-source chat models on most benchmarks we tested, and in our human evaluations for helpfulness and safety, are on par with some popular closed-source models like ChatGPT and PaLM. **Variations** Llama 2 comes in a range of parameter sizes — 7B, 13B, and 70B — as well as pretrained and fine-tuned variations.

• DeepSeek-V4-Flash-0731-NVFP4↗

  NVIDIA

  The NVIDIA DeepSeek-V4-Flash-0731-NVFP4 model is the quantized version of DeepSeek AI's DeepSeek-V4-Flash-0731 model, an autoregressive Mixture-of-Experts language model that uses an optimized Transformer architecture with hybrid attention (Compressed Sparse Attention and Heavily Compressed Attention) and Manifold-Constrained Hyper-Connections. DeepSeek-V4-Flash-0731 is the official DeepSeek-V4-Flash release and includes DeepSeek's DSpark speculative decoding module in the same checkpoint. For more information, refer to the DeepSeek-V4-Flash-0731 model card. The NVIDIA DeepSeek-V4-Flash-0731-NVFP4 model is quantized with Model Optimizer. This model is not owned or developed by NVIDIA. This model has been developed and built to a third-party’s requirements for this application and use case; see link to Non-NVIDIA (DeepSeek-V4-Flash-0731) Model Card. DeepSeek V4 is well-suited for advanced reasoning, agentic AI applications, tool use scenarios, and complex problem-solving in domains such as mathematics, software engineering, and enterprise AI assistants.

• Qwen3-Next-80B-A3B-Thinking-NVFP4↗

  NVIDIA

• OctoMed-7B↗

  OctoMed

• tofu_Llama-3.2-1B-Instruct_full↗

  open-unlearning

• OpenMed-NER-DNADetect-TinyMed-135M↗

  OpenMed

  **Specialized model for Biomedical Entity Recognition - Proteins, DNA, RNA, cell lines, and cell types** This model is a **state-of-the-art** fine-tuned transformer engineered to deliver **enterprise-grade accuracy** for biomedical entity recognition - proteins, dna, rna, cell lines, and cell types. 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. JNLPBA corpus focuses on biomedical named entity recognition for protein, DNA, RNA, cell line, and cell type entities.

• tiny-random-VisionEncoderDecoderModel-vit-gpt2↗

  optimum-intel-internal-testing

• Qwen3-4B-LLVQ-Tetra↗

  Pier-Jean

• VieNeu-TTS-0.3B-q4-gguf↗

  pnnbao-ump

  **VieNeu-TTS-0.3B-Q4-0-GGUF** is a **Q4_0** quantized version of VieNeu-TTS-0.3B. This model is specifically optimized to run directly on the **CPU**, providing extremely fast speech synthesis without the need for a dedicated GPU. Training high-quality TTS models requires significant GPU resources. If you find this model useful, please consider supporting the development: eSpeak NG is **mandatory** for phonemization. - **Windows:** Download `.msi` from eSpeak NG Releases. - **macOS:** `brew install espeak` - **Linux:** `sudo apt install espeak-ng`