leonsarmiento/Qwen3.6-27B-uncensored-heretic-v2-3bit-mlx

This model leonsarmiento/Qwen3.6-27B-uncensored-heretic-v2-3bit-mlx was converted to MLX format from llmfan46/Qwen3.6-27B-uncensored-heretic-v2 using mlx-lm version 0.31.2.

using mlx-lm version 0.31.2.

Quantization Details

The model uses mixed quantization:

• Embedding layers: 5-bit with group_size=64
• Prediction layers: 5-bit with group_size=64
• All other layers: 3-bit with group_size=64

This mixed precision approach provides a balance between compression and quality.

Use with mlx

pip install mlx-lm

Recommended Inference Parameters - Add to Jinja template on LM studio or Chat Template Kwargs on oMLX

Thinking Preserve ({%- set preserve_thinking = true %}):

• General tasks: temperature=1.0, top_p=0.95, top_k=20, min_p=0.0, presence_penalty=1.5, repetition_penalty=1.05
• Coding tasks: temperature=0.6, top_p=0.95, top_k=20, min_p=0.0, presence_penalty=0.0, repetition_penalty=1.05

Instruct Mode ({%- set enable_thinking = false -%}):

• General tasks: temperature=0.7, top_p=0.8, top_k=20, min_p=0.0, presence_penalty=1.5, repetition_penalty=1.05
• Reasoning tasks: temperature=1.0, top_p=0.95, top_k=20, min_p=0.0, presence_penalty=1.5, repetition_penalty=1.05

Use with mlx

pip install mlx-lm

from mlx_lm import load, generate

model, tokenizer = load("leonsarmiento/Qwen3.6-27B-uncensored-heretic-v2-3bit-mlx")

prompt = "hello"

if tokenizer.chat_template is not None:
    messages = [{"role": "user", "content": prompt}]
    prompt = tokenizer.apply_chat_template(
        messages, add_generation_prompt=True, return_dict=False,
    )

response = generate(model, tokenizer, prompt=prompt, verbose=True)