Qwen3.6-27B-Fable-Fusion-711-Uncensored-Heretic-NM-DAU — oQ4 MLX Quant
oMLX oQ4 recipe-driven quantization of the DavidAU
Qwen3.6-27B-Fable-Fusion-711-Uncensored-Heretic-NM-DAU merge (Qwen3.6-27B base).
Per-tensor hard floors preserved on all 298 recipe-pinned tensors; only the base tier
was downgraded to oQ4 to land a smaller file.
Quantization summary
| Quant method | oMLX oQ, enhanced |
| Base level | oQ4 — base bits 4, group size 64, affine |
| Recipe pins | 298 tensors (187 × 8-bit, 111 × 6-bit) — preserved verbatim |
| oQ4 self-boosts | 45 extra tensors lifted to 5-bit (linear-attention, calibration-driven) |
| Final override map | 343 tensors (187 × 8-bit, 111 × 6-bit, 45 × 5-bit) |
| Model size | 20.16 GB on disk (5 shards) |
| Language model | ~21.6 GB dry-run estimate at oQ6e reference; actual oQ4 build ~19.7 GB LM |
| Text-only | vision encoder stripped |
| MTP | stripped |
| Dtype | float16 |
Recipe / pinned floors
- FA6 — 6-bit floor on all 16 full-attention layers (self_attn + MLP)
- QKV6 — 6-bit floor on all
linear_attn.in_proj_qkv - Tail6 — 6-bit floor on layers 56–58
- E6 —
embed_tokensat 6-bit - b8 — 8-bit bump set (187 tensors, incl.
lm_head)
Verified: recipe → config override map matches exactly (0 missing, 0 bit mismatches); on-disk safetensors dtypes confirm pinned tensors are packed-quantized (U32), not fp16.
Use with mlx
pip install mlx-lm
from mlx_lm import load, generate
model, tokenizer = load("DavidAU/Qwen3.6-27B-Fable-Fusion-711-Uncensored-Heretic-NM-DAU-oQ4-fa6-qkv6-tail6-e6-b8")
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)
About the base
This MLX quant is derived from the fp source Qwen3.6-27B-Fable-Fusion-711-Uncensored-Heretic-NM-DAU (Qwen3.6-27B base, Fable-Fusion-711 merge family, uncensored / abliterated, DavidAU). 64-layer hybrid architecture: 48 linear-attention + 16 full-attention layers. Original model license: apache-2.0.
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Base model
Qwen/Qwen3.6-27B
# Make sure mlx-lm is installed # pip install --upgrade mlx-lm # Generate text with mlx-lm from mlx_lm import load, generate model, tokenizer = load("Johneeee/Qwen3.6-27B-Fable-Fusion-711-Uncensored-Heretic-NM-DAU-oQ4-fa6-qkv6-tail6-e6-b8") prompt = "Write a story about Einstein" messages = [{"role": "user", "content": prompt}] prompt = tokenizer.apply_chat_template( messages, add_generation_prompt=True ) text = generate(model, tokenizer, prompt=prompt, verbose=True)