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jan1k/Qwen3.6-35B-A3B-Uncensored-Genesis-NVFP4-GGUF

sourceHugging Faceapache-2.0updated 3d agoView on Hugging Face
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Qwen3.6-35B-A3B-Uncensored-Genesis — NVFP4 GGUF

NVFP4 quantisation of LuffyTheFox/Qwen3.6-35B-A3B-Uncensored-Genesis-Final-GGUF, built with advanced-gguf-quantizer (a llama.cpp fork focused on NVFP4/MXFP6 quantization).

This is the non-Hermes version — Genesis tensor repair on the HauhauCS uncensored base, without the Hermes finetune transfer. For the Hermes version, see jan1k/Qwen3.6-35B-A3B-Uncensored-Genesis-Hermes-Final-NVFP4-GGUF.

Files

v4 — Recommended (inline scales, LM Studio compatible)

FileSizeTensors
Qwen3.6-35B-A3B-Uncensored-Genesis-NVFP4-v4.gguf~20 GB733
mmproj-Qwen3.6-35B-A3B-Uncensored-Genesis-Final-F16.gguf~899 MBvision projector

Re-quantised 2026-09-24 from Luffy's updated Q8KP source.

v4 uses --nvfp4-inline-scales-only to emit native inline UE4M3 scales without separate .scale/.input_scale tensors. This is required for LM Studio / Pelican and other runtimes that do not support the extended NVFP4 scale tensor contract.

Imatrix variants will follow after the plain v4 is confirmed working in target runtimes.

Source

Source GGUFQwen3.6-35B-A3B-Uncensored-Genesis-Final-Q8_K_P.gguf (43.6 GB, 10.06 BPW)
Architectureqwen35moe (MoE), 35B total / ~3B active, 40 layers
Context262144
general.file_type39 (LLAMA_FTYPE_MOSTLY_NVFP4)
MTP/NextNnone

Two-step pipeline: Q8KP -> F16 intermediate -> NVFP4. The F16 step gives the NVFP4 encoder clean data (no Q8_0 quantization noise).

Tensor mix (v4)

typecountnotes
F32331norms, ssm scalars, gate inputs
F16175sensitive weights (blk.0 attn, ssm, etc.)
Q6_K1output.weight
NVFP4226bulk weights
total733
separate .scale/.input_scale0inline UE4M3 only

Format fix (v3/v2 -> v4)

The older v2/v3 files use the extended NVFP4 format with separate .scale and .input_scale tensors. Runtimes like LM Studio / Pelican only read inline UE4M3 scales and ignore the separate scale tensors, producing garbage output.

v4 uses --nvfp4-inline-scales-only to:

  • —Skip writing .scale and .input_scale tensors
  • —Force tensor_scale=1.0 (no pre-scaling)
  • —Force input_scale=identity
  • —Make inline UE4M3 scales self-contained

Tensor protection policy

F16 singular-collapse protection:

tensortype
blk.0.attn_gate.weightF16
blk.0.attn_qkv.weightF16
blk.0.ffn_down_exps.weightF16
blk.13.ffn_down_exps.weightF16

F32 architecture-specific protection:

tensorreason
blk.*.attn_norm.weight1D norm
blk.*.post_attention_norm.weight1D norm
blk.*.attn_q_norm.weight1D norm
blk.*.attn_k_norm.weight1D norm
blk.*.ssm_norm.weight1D norm
output_norm.weight1D norm
blk.*.ssm_conv1d.weightCUDA SSM conv kernel requires F32
blk.*.ssm_dt.biasSSM scalar, kernel compatibility
blk.*.ssm_aSSM scalar, kernel compatibility
blk.*.ffn_gate_inp_shexp.weightshared expert gate input
token_embd.weightF16 (embedding quality)

Forced NVFP4 (do not push lower):

tensortype
blk.0.ssm_out.weightNVFP4
blk.1.attn_gate.weightNVFP4
blk.1.attn_qkv.weightNVFP4

Usage

bash
llama-cli -m Qwen3.6-35B-A3B-Uncensored-Genesis-NVFP4-v4.gguf \
  --mmproj mmproj-Qwen3.6-35B-A3B-Uncensored-Genesis-Final-F16.gguf \
  --jinja -c 131072 -ngl 99
  • —Set K cache and V cache quantization to F16
  • —Set GPU offload to maximum, active experts to 8
  • —Set number of layers for which to force MoE weights onto CPU to 40

Hardware

  • —Blackwell (RTX 50xx): native FP4 path, fastest
  • —Ampere (RTX 30xx): NVFP4 inference works via fallback kernels
  • —Quantisation was done CPU-only (Ampere CUDA NVFP4 encoder hangs on MoE)

Reproducibility

bash
# Step 1: Q8_K_P -> F16 intermediate
llama-quantize --allow-requantize \
  Qwen3.6-35B-A3B-Uncensored-Genesis-Q8_K_P.gguf temp_f16.gguf F16 6

# Step 2: F16 -> NVFP4 v4 (inline scales only, CPU-only)
CUDA_VISIBLE_DEVICES="" llama-quantize \
  --allow-requantize \
  --nvfp4-inline-scales-only \
  --tensor-type-file tensor_types_protection.txt \
  temp_f16.gguf \
  Qwen3.6-35B-A3B-Uncensored-Genesis-NVFP4-v4.gguf \
  NVFP4 6

# Cleanup
rm temp_f16.gguf

Credits