GLM-5.2 is live. #1 throughput on OpenRouter, pay-per-token on FriendliAI. Try it today ➜
lxz8798
Available on FriendliAI
Run this model inference on single tenant GPU with unmatched speed and reliability at scale.
Model Details
Model Provider
lxz8798
Model Tree
Base
Qwen/Qwen3.6-35B-A3B
Fine-tuned
this model
Input Modalities
Text
Image
Video
Output Modalities
Text
Supported Functionality
Dedicated Endpoints
This is an abliterated and DPO-retrained version of Qwen/Qwen3.6-35B-A3B.
The model was first modified through refusal ablation, then retrained with a private DPO dataset of fewer than 1000 samples to fix the remaining hedging behavior after ablation. In testing, this version reached 0 refusals on HarmBench and showed 0 hedging compared to the other ablated models tested.
After ablation and DPO, the original Qwen3.6 vision layers were readded to retain the multimodal functionality of the base model. This repository contains the repaired FP16 safetensors build with the visual tower present.
| Attribute | Value |
|---|---|
| Base model | Qwen/Qwen3.6-35B-A3B |
| Method | Refusal ablation plus DPO retraining |
| DPO data | Private dataset, fewer than 1000 samples |
| Format | FP16 safetensors |
| Architecture | Qwen3.6 MoE vision-language model |
| Vision support | Readded after ablation and DPO |
| HarmBench result | 0 refusals in local testing |
| File | Description |
|---|---|
model-00001-of-00002.safetensors | FP16 language-model shard |
model-00002-of-00002.safetensors | FP16 language-model shard |
model-visual-original.safetensors | Readded original Qwen3.6 visual tower and merger tensors |
model.safetensors.index.json | Safetensors shard index including the vision tensors |
chat_template.jinja | Vision/tool-calling chat template |
GGUF quantizations are published separately:
python
from transformers import AutoModelForImageTextToText, AutoProcessorrepo = "OpenYourMind/OpenYourMind-Qwen3.6-35B-A3B-kuato-DPO-abliterated-uncensored"processor = AutoProcessor.from_pretrained(repo, trust_remote_code=True)model = AutoModelForImageTextToText.from_pretrained(repo,torch_dtype="auto",device_map="auto",trust_remote_code=True,)
☕ If these models are useful to you, consider supporting my work — it funds compute for more & larger abliterations.
The DPO dataset is private. The intent of this release is to share the resulting model, not the training data.
Use is the responsibility of the user. Make sure your usage complies with applicable laws, platform rules, and deployment requirements.
config.json| Model config |
preprocessor_config.json | Image processor config |
video_preprocessor_config.json | Video processor config |
tokenizer.json, tokenizer_config.json | Tokenizer files |