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  tomerRest

  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

• llavon-ime-llama-250m-GGUF↗

  tony65535

• c38-r16c↗

  totomarry

• llama3.2-typhoon2-3b-instruct↗

  typhoon-ai

• gemma-3-1b-pt-unsloth-bnb-4bit↗

  unsloth

• gemma-4-E2B-it-UD-MLX-4bit↗

  unsloth

• MiniMax-M2.7-GGUF↗

  unsloth

  **MiniMax-M2.7** is our first model deeply participating in its own evolution. M2.7 is capable of building complex agent harnesses and completing highly elaborate productivity tasks, leveraging Agent Teams, complex Skills, and dynamic tool search. For more details, see our blog post. M2.7 initiates a cycle of model self-evolution: during development, we let the model update its own memory, build dozens of complex skills for RL experiments, and improve its own learning process based on experiment results. An internal version of M2.7 autonomously optimized a programming scaffold over 100+ rounds — analyzing failure trajectories, modifying code, running evaluations, and deciding to keep or revert — achieving a **30% performance improvement**. On MLE Bench Lite (22 ML competitions), M2.7 achieved a **66.6% medal rate**, second only to Opus-4.6 and GPT-5.4. M2.7 delivers outstanding real-world programming capabilities spanning log analysis, bug troubleshooting, refactoring, code security, and machine learning. Beyond code generation, M2.7 demonstrates strong system-level reasoning — correlating monitoring metrics, conducting trace analysis, verifying root causes in databases, and making SRE-level decisions. Using M2.7, we have reduced live production incident recovery time to **under three minutes** on multiple occasions.

• Qwen3-30B-A3B-Thinking-2507-GGUF↗

  unsloth

  Over the past three months, we have continued to scale the **thinking capability** of Qwen3-30B-A3B, improving both the **quality and depth** of reasoning. We are pleased to introduce **Qwen3-30B-A3B-Thinking-2507**, featuring the following key enhancements: **NOTE**: This version has an increased thinking length. We strongly recommend its use in highly complex reasoning tasks. **Qwen3-30B-A3B-Thinking-2507** has the following features: - Type: Causal Language Models - Training Stage: Pretraining & Post-training - Number of Parameters: 30.5B in total and 3.3B activated - Number of Paramaters (Non-Embedding): 29.9B - Number of Layers: 48 - Number of Attention Heads (GQA): 32 for Q and 4 for KV - Number of Experts: 128 - Number of Activated Experts: 8 - Context Length: **262,144 natively**.

• Z-Image-Turbo-GGUF↗

  vantagewithai

  Z-Image is a powerful and highly efficient image generation model with **6B** parameters. Currently there are three variants: 📸 **Photorealistic Quality**: **Z-Image-Turbo** delivers strong photorealistic image generation while maintaining excellent aesthetic quality. 📖 **Accurate Bilingual Text Rendering**: **Z-Image-Turbo** excels at accurately rendering complex Chinese and English text.

• Qwen3.8-27B-test-DFlash2-v0.3-GGUF↗

  vvsotnikov