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AI Models 85003

• Mistral-Small-3.1-24B-Instruct-2503-GGUF↗

  unsloth

  Building upon Mistral Small 3 (2501), Mistral Small 3.1 (2503) **adds state-of-the-art vision understanding** and enhances **long context capabilities up to 128k tokens** without compromising text performance. With 24 billion parameters, this model achieves top-tier capabilities in both text and vision tasks. This model is an instruction-finetuned version of: Mistral-Small-3.1-24B-Base-2503. Mistral Small 3.1 can be deployed locally and is exceptionally "knowledge-dense," fitting within a single RTX 4090 or a 32GB RAM MacBook once quantized. It is ideal for: - Fast-response conversational agents. - Low-latency function calling. - Subject matter experts via fine-tuning. - Local inference for hobbyists and organizations handling sensitive data. - Programming and math reasoning. - Long document understanding. - Visual understanding.

• C2S-Pythia-410m-cell-type-prediction↗

  vandijklab

• Qwen3.8-27B-Abliterated-MLX-oQ6e-mtp↗

  windowsxp811203

• qwen3.5-9b-rp-lora-v2↗

  www622846

• ViBT↗

  Yuanshi

• Smaug-AI-Image-Detector↗

  abacusai

  Smaug-AI-Image-Detector sets a new state of the art for open-source AI-generated image detection. It is a fine-tune of Google's EmbeddingGemma-2 by Abacus.AI and the strongest open-source detector on real-world images. **A dedicated vision model, 41% smaller than its base.** We streamlined EmbeddingGemma-2 into a vision-only architecture by removing its 305M-parameter audio tower: 439M parameters instead of 744M, with no loss in accuracy. Architecture EmbeddingGemma-2 image encoder + linear classifier Total Parameters 439M (base model: 744M) Audio Tower Removed: vision-only architecture, −305M parameters Input Any image; scored as three 528 × 528 crops at native resolution Output Probability that the image is AI-generated Hardware NVIDIA GPU (~1 GB), Apple Silicon (Metal), or CPU Precision bfloat16 weights Formats PyTorch / sentence-transformers (this repo); GGUF for llama.cpp: Smaug-AI-Image-Detector-GGUF Base Model google/embeddinggemma-2 Adaptation Full fine-tune

• smol_llama-101M-midjourney-messages-GGUF↗

  afrideva

• gpt2-small-arabic-poetry↗

  akhooli

• qwen2.5-coder-14b-instruct↗

  阿里巴巴 · Qwen

  Qwen2.5-Coder is the latest series of Code-Specific Qwen large language models (formerly known as CodeQwen). As of now, Qwen2.5-Coder has covered six mainstream model sizes, 0.5, 1.5, 3, 7, 14, 32 billion parameters, to meet the needs of different developers. Qwen2.5-Coder brings the following improvements upon CodeQwen1.5: - Significantly improvements in **code generation**, **code reasoning** and **code fixing**. Base on the strong Qwen2.5, we scale up the training tokens into 5.5 trillion including source code, text-code grounding, Synthetic data, etc. Qwen2.5-Coder-32B has become the current state-of-the-art open-source codeLLM, with its coding abilities matching those of GPT-4o. - A more comprehensive foundation for real-world applications such as **Code Agents**. Not only enhancing coding capabilities but also maintaining its strengths in mathematics and general competencies. - **Long-context Support** up to 128K tokens. **This repo contains the instruction-tuned 14B Qwen2.5-Coder model**, which has the following features: - Type: Causal Language Models - Training Stage: Pretraining & Post-training - Architecture: transformers with RoPE, SwiGLU, RMSNorm, and Attention QKV bias - Number of Parameters: 14.7B - Number of Paramaters (Non-Embedding): 13.1B - Number of Layers: 48 - Number of Attention Heads (GQA): 40 for Q and 8 for KV - Context Length: Full 131,072 tokens - Please refer to this section for detailed instructions on how to deploy Qwen2.5 for handling long texts.

• specter2↗

  allenai