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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
qwen3.8-9b-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
qwen3.6-35b-a3b-claude-4.6-opus-reasoning-distilled
# 🔥 Qwen3.6-35B-A3B-Claude-4.6-Opus-Reasoning-Distilled
A reasoning SFT fine-tune of `Qwen/Qwen3.6-35B-A3B` on chain-of-thought (CoT) distillation mostly sourced from Claude Opus 4.6. The goal is to preserve Qwen3.6's strong agentic coding and reasoning base while nudging the model toward structured Claude Opus-style reasoning traces and more stable long-form problem solving.
The training path is text-only. The Qwen3.6 base architecture includes a vision encoder, but this fine-tuning run did not train on image or video examples.
- **Developed by:** @hesamation
- **Base model:** `Qwen/Qwen3.6-35B-A3B`
- **License:** apache-2.0
This fine-tuning run is inspired by Jackrong/Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled, including the notebook/training workflow style and Claude Opus reasoning-distillation direction.
[](https://x.com/Hesamation) [](https://discord.gg/vtJykN3t)
## Benchmark Results
The MMLU-Pro pass used 70 total questions per model: `--limit 5` across 14 MMLU-Pro subjects. Treat this as a smoke/comparative check, not a release-quality full benchmark.
...
Repository: localaiLicense: apache-2.0
qwen3.5-9b-glm5.1-distill-v1
# 🪐 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
qwen3.5-27b-claude-4.6-opus-reasoning-distilled-heretic-i1
Repository: localaiLicense: apache-2.0
qwen3.5-27b-claude-4.6-opus-reasoning-distilled-i1
Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled-i1-GGUF - A GGUF quantized model optimized for local inference. Specialized for reasoning and chain-of-thought tasks. Based on Qwen 3.5 architecture with enhanced language understanding. Available in multiple quantization levels for various hardware requirements. Distilled from Claude-style reasoning models for enhanced logical reasoning capabilities.
Repository: localaiLicense: apache-2.0
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