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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
qwen3.8-9b-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
qwen3.5-4b-claude-4.6-opus-reasoning-distilled
Qwen3.5-4B-Claude-4.6-Opus-Reasoning-Distilled-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
vllm-omni-z-image-turbo
Z-Image-Turbo via vLLM-Omni - A distilled version of Z-Image optimized for speed with only 8 NFEs. Offers sub-second inference latency on enterprise-grade H800 GPUs and fits within 16GB VRAM. Excels in photorealistic image generation, bilingual text rendering (English & Chinese), and robust instruction adherence.
Repository: localaiLicense: apache-2.0
z-image-diffusers
Z-Image is the foundation model of the ⚡️-Image family, engineered for good quality, robust generative diversity, broad stylistic coverage, and precise prompt adherence. While Z-Image-Turbo is built for speed, Z-Image is a full-capacity, undistilled transformer designed to be the backbone for creators, researchers, and developers who require the highest level of creative freedom.
Repository: localaiLicense: apache-2.0
z-image-turbo-diffusers
🚀 Z-Image-Turbo – A distilled version of Z-Image that matches or exceeds leading competitors with only 8 NFEs (Number of Function Evaluations). It offers ⚡️sub-second inference latency⚡️ on enterprise-grade H800 GPUs and fits comfortably within 16G VRAM consumer devices. It excels in photorealistic image generation, bilingual text rendering (English & Chinese), and robust instruction adherence.
Repository: localaiLicense: apache-2.0
kalomaze_qwen3-16b-a3b
A man-made horror beyond your comprehension.
But no, seriously, this is my experiment to:
measure the probability that any given expert will activate (over my personal set of fairly diverse calibration data), per layer
prune 64/128 of the least used experts per layer (with reordered router and indexing per layer)
It can still write semi-coherently without any additional training or distillation done on top of it from the original 30b MoE. The .txt files with the original measurements are provided in the repo along with the exported weights.
Custom testing to measure the experts was done on a hacked version of vllm, and then I made a bespoke script to selectively export the weights according to the measurements.
Repository: localaiLicense: apache-2.0
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