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mimo-v2.6-distill-qwen-9b

MiMo-V2.6-Distill-Qwen-9B is Xiaomi MiMo's 9B Qwen3.5 fine-tune for coding, agent tasks, and visual coding. This Q4_K_M GGUF build uses llama.cpp with the model's embedded chat template and includes the F16 vision projector.

Repository: localaiLicense: mit

mimo-v2.6-distill-qwen-9b-q8

MiMo-V2.6-Distill-Qwen-9B is Xiaomi MiMo's 9B Qwen3.5 fine-tune for coding, agent tasks, and visual coding. This Q8_0 GGUF build uses llama.cpp with the model's embedded chat template and includes the F16 vision projector.

Repository: localaiLicense: mit

qwen3.8-27b-fable-5-coding-distilled

A 27B Qwen3.8 fine-tune for coding, debugging, and agentic reasoning. This Q4_K_M GGUF build uses llama.cpp, the embedded chat template, and the F16 vision projector.

Repository: localaiLicense: apache-2.0

qwen3.8-27b-fable-5-coding-distilled-q8

A 27B Qwen3.8 fine-tune for coding, debugging, and agentic reasoning. This Q8_0 GGUF build uses llama.cpp, the embedded chat template, and the F16 vision projector.

Repository: localaiLicense: apache-2.0

qwen3.5-9b-kimi-k3-distilled-q4

A 9B Qwen3.5 fine-tune trained on Kimi K3 coding, debugging, and tool-use traces. This Q4_K_M GGUF build uses llama.cpp and the embedded chat template, with an F16 vision projector and a 32,768-token context.

Repository: localaiLicense: apache-2.0

qwen3.5-9b-kimi-k3-distilled-q8

A 9B Qwen3.5 fine-tune trained on Kimi K3 coding, debugging, and tool-use traces. This Q8_0 GGUF build uses llama.cpp and the embedded chat template, with an F16 vision projector and a 32,768-token context.

Repository: localaiLicense: apache-2.0

qwen3.8-9b-q4

Qwen3.8-9B is Empero AI's full-parameter distillation of Qwen3.8 2.4T A95B into the dense Qwen3.5-9B architecture. It targets reasoning, mathematics, coding, instruction following, and tool use, and supports a native 262K-token context window. This default entry uses Q4_K_M weights; a higher-quality Q8_0 build is available as a variant.

Repository: localaiLicense: apache-2.0

qwen3.8-4b-q4

Qwen3.8-4B is Empero AI's full-parameter distillation of Qwen3.8 2.4T A95B into the Qwen3.5-4B architecture. It targets mathematics, reasoning, instruction following, and tool use with a native 262K-token context window. This default entry uses Q4_K_M weights; a higher-quality Q8_0 build is available as a variant.

Repository: localaiLicense: apache-2.0

qwen3.8-2b-q4

Qwen3.8-2B is Empero AI's smallest Qwen3.8 reasoning distillation. It uses the Qwen3.5-2B architecture and targets mathematics, instruction following, tool use, and edge deployment with a native 262K-token context window. This default entry uses Q4_K_M weights; a higher-quality Q8_0 build is available as a variant.

Repository: localaiLicense: apache-2.0

qwen3.8-2b-distill-q4

Qwen3.8 2B Distill is an Apache-2.0, text-only Qwen3.5 2B fine-tune distilled from Qwen3.8 2.4T A95B reasoning traces. It targets compact reasoning, coding, instruction following, and function calling with a 262K native context window. This entry uses the balanced Q4_K_M GGUF quantization; the Q8_0 variant offers higher fidelity.

Repository: localaiLicense: apache-2.0

qwen3.8-2b-distill-q8

Qwen3.8 2B Distill in the higher-fidelity Q8_0 GGUF format. This text-only Qwen3.5 2B fine-tune targets reasoning, coding, instruction following, and function calling with a 262K native context window.

Repository: localaiLicense: apache-2.0

qwen3.8-4b-distill-q4

Qwen3.8 4B Distill is an Apache-2.0, text-only Qwen3.5 4B fine-tune distilled from Qwen3.8 2.4T A95B reasoning traces. It targets reasoning, coding, instruction following, and function calling with a 262K native context window. This entry uses the balanced Q4_K_M GGUF quantization; the Q8_0 variant offers higher fidelity.

Repository: localaiLicense: apache-2.0

qwen3.8-4b-distill-q8

Qwen3.8 4B Distill in the higher-fidelity Q8_0 GGUF format. This text-only Qwen3.5 4B fine-tune targets reasoning, coding, instruction following, and function calling with a 262K native context window.

Repository: localaiLicense: apache-2.0

qwen3.8-35b-a3b-distill-q4

Empero's Qwen3.8 distillation into Qwen3.6-35B-A3B has 35B total parameters and about 3B active per token. This Q4_K_M GGUF build uses the embedded reasoning template and includes the F16 vision projector. Vision is inherited from the base and was not evaluated by the publisher.

Repository: localaiLicense: apache-2.0

qwen3.8-35b-a3b-distill-q5

Empero's Qwen3.8 distillation into Qwen3.6-35B-A3B has 35B total parameters and about 3B active per token. This Q5_K_M GGUF build uses the embedded reasoning template and includes the F16 vision projector. Vision is inherited from the base and was not evaluated by the publisher.

Repository: localaiLicense: apache-2.0

qwen3.8-35b-a3b-distill-q8

Empero's Qwen3.8 distillation into Qwen3.6-35B-A3B has 35B total parameters and about 3B active per token. This Q8_0 GGUF build uses the embedded reasoning template and includes the F16 vision projector. Vision is inherited from the base and was not evaluated by the publisher.

Repository: localaiLicense: apache-2.0

qwen3.8-9b-distill-q4

Qwen3.8 9B Distill is an Apache-2.0, text-only Qwen3.5 9B fine-tune distilled from Qwen3.8 2.4T A95B reasoning traces. It targets mathematics, coding, instruction following, and function calling with a 262K native context window. This entry uses the balanced Q4_K_M GGUF quantization; the Q8_0 variant offers higher fidelity.

Repository: localaiLicense: apache-2.0

qwen3.8-9b-distill-q8

Qwen3.8 9B Distill in the higher-fidelity Q8_0 GGUF format. This text-only Qwen3.5 9B fine-tune targets reasoning, coding, instruction following, and function calling with a 262K native context window.

Repository: localaiLicense: apache-2.0

qwen3.6-14b-a3b-fablevibes

Qwen3.6-14B-A3B-FableVibes is an Apache-2.0 mixture-of-experts reasoning model distilled from Fable 5 and Claude Opus traces, with additional tool calling and coding data. It retains Qwen 3.6 vision support while pruning the 35B-A3B base to a 14B consumer-oriented footprint. This default entry uses the recommended Q4_K_M GGUF quantization and its Q8_0 multimodal projector.

Repository: localaiLicense: apache-2.0

qwen3.6-14b-a3b-fablevibes-q8

Qwen3.6-14B-A3B-FableVibes is an Apache-2.0 mixture-of-experts reasoning model distilled from Fable 5 and Claude Opus traces, with additional tool calling and coding data. This entry uses the near-lossless Q8_0 GGUF quantization and its matching Q8_0 multimodal projector.

Repository: localaiLicense: apache-2.0

qwopus-glm-18b-merged

# 🪐 Qwen3.5-9B-GLM5.1-Distill-v1 ## 📌 Model Overview **Model Name:** `Jackrong/Qwen3.5-9B-GLM5.1-Distill-v1` **Base Model:** Qwen3.5-9B **Training Type:** Supervised Fine-Tuning (SFT, Distillation) **Parameter Scale:** 9B **Training Framework:** Unsloth This model is a distilled variant of **Qwen3.5-9B**, trained on high-quality reasoning data derived from **GLM-5.1**. The primary goals are to: - Improve **structured reasoning ability** - Enhance **instruction-following consistency** - Activate **latent knowledge via better reasoning structure** ## 📊 Training Data ### Main Dataset - `Jackrong/GLM-5.1-Reasoning-1M-Cleaned` - Cleaned from the original `Kassadin88/GLM-5.1-1000000x` dataset. - Generated from a **GLM-5.1 teacher model** - Approximately **700x** the scale of `Qwen3.5-reasoning-700x` - Training used a **filtered subset**, not the full source dataset. ### Auxiliary Dataset - `Jackrong/Qwen3.5-reasoning-700x` ...

Repository: localaiLicense: apache-2.0

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