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glm-4.7-flash-derestricted
This model is a quantized version of the original GLM-4.7-Flash-Derestricted model, derived from the base model `koute/GLM-4.7-Flash-Derestricted`. It is designed for restricted use, featuring tags like "derestricted," "uncensored," and "unlimited." The quantized versions (e.g., Q2_K, Q4_K_S, Q6_K) offer varying trade-offs between accuracy and efficiency, with the Q4_K_S and Q6_K variants being recommended for balanced performance. The model is optimized for fast inference and supports multiple quantization schemes, though some advanced quantization options (like IQ4_XS) are not available. It is intended for use in environments with specific constraints or restrictions.

Repository: localaiLicense: mit

huihui-glm-4.7-flash-abliterated-i1
The model is a quantized version of **huihui-ai/Huihui-GLM-4.7-Flash-abliterated**, optimized for efficiency and deployment. It uses GGUF files with various quantization levels (e.g., IQ1_M, IQ2_XXS, Q4_K_M) and is designed for tasks requiring low-resource deployment. Key features include: - **Base Model**: Huihui-GLM-4.7-Flash-abliterated (unmodified, original model). - **Quantization**: Supports IQ1_M to Q4_K_M, balancing accuracy and efficiency. - **Use Cases**: Suitable for applications needing lightweight inference, such as edge devices or resource-constrained environments. - **Downloads**: Available in GGUF format with varying quality and size (e.g., 0.2GB to 18.2GB). - **Tags**: Abliterated, uncensored, and optimized for specific tasks. This model is a modified version of the original GLM-4.7, tailored for deployment with quantized weights.

Repository: localaiLicense: mit

glm-4.7-flash
**GLM-4.7-Flash** is a 30B-A3B MoE (Model Organism Ensemble) model designed for efficient deployment. It outperforms competitors in benchmarks like AIME 25, GPQA, and τ²-Bench, offering strong accuracy while balancing performance and efficiency. Optimized for lightweight use cases, it supports inference via frameworks like vLLM and SGLang, with detailed deployment instructions in the official repository. Ideal for applications requiring high-quality text generation with minimal resource consumption.

Repository: localaiLicense: mit