How to use from
MLX LM
Generate or start a chat session
# Install MLX LM
uv tool install mlx-lm
# Interactive chat REPL
mlx_lm.chat --model "dawncr0w/Qwen3.6-35B-A3B-Uncensored-HauhauCS-Aggressive-OptiQ-6bpw-MLX"
Run an OpenAI-compatible server
# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "dawncr0w/Qwen3.6-35B-A3B-Uncensored-HauhauCS-Aggressive-OptiQ-6bpw-MLX"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
   -H "Content-Type: application/json" \
   --data '{
     "model": "dawncr0w/Qwen3.6-35B-A3B-Uncensored-HauhauCS-Aggressive-OptiQ-6bpw-MLX",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'
Quick Links

Qwen3.6-35B-A3B-Uncensored-HauhauCS-Aggressive OptiQ 6bpw

Source: ericpandev/Qwen3.6-35B-A3B-Uncensored-HauhauCS-Aggressive-bf16 Original: HauhauCS/Qwen3.6-35B-A3B-Uncensored-HauhauCS-Aggressive

Quantization

Field Value
Method OptiQ mixed-precision MLX
Target BPW 6
Achieved BPW 6.12
Candidate bits 4, 8
Group size 64
Calibration optiq (24 samples)
Model size 18.64 GiB
Layer allocation 163 high-bit / 228 low-bit
Layer policy entries 391

Notes

  • This is a text-only MLX export; vision components are omitted.
  • Native MTP is disabled for this build.
  • The original model is multimodal; this repository contains only the language model weights.
  • Built from the bf16 source snapshot listed above.

Usage

huggingface-cli download dawncr0w/Qwen3.6-35B-A3B-Uncensored-HauhauCS-Aggressive-OptiQ-6bpw-MLX --local-dir Qwen3.6-35B-A3B-Uncensored-HauhauCS-Aggressive-OptiQ-6bpw-MLX
python -m mlx_lm.generate --model Qwen3.6-35B-A3B-Uncensored-HauhauCS-Aggressive-OptiQ-6bpw-MLX --prompt "Write a short greeting."
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