Qwen3.6-35B-A3B-NVFP4-GGUF – AI Model by FreedomAISVR | AlphaNeural AI

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FreedomAISVR/
Qwen3.6-35B-A3B-NVFP4-GGUF

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Qwen3.6-35B-A3B-NVFP4-GGUF

NVFP4 GGUF quantization of Qwen/Qwen3.6-35B-A3B.

Multimodal model: vision encoder (903 MB) + text MoE LLM (18.36 GB).

About NVFP4

NVFP4 is a Blackwell-native FP4 format (EFM4: 1 sign, 1 mantissa, 2 exponent bits). Applied uniformly to all tensors.

Total params: 35.95B (3B active, 256 experts, 8/token)
Quantization: NVFP4 (~4.55 BPW)
File size: 18.36 GiB (text) + 903 MB (vision)
Vision encoder: 27-layer ViT, hidden 1152, 3->1152x2 patch embed (temporal)
Context: 262,144 tokens natively

NVFP4 requires a Blackwell (RTX 50-series or B-series) GPU for hardware acceleration.

Files

Filename	Type	Size	Description
`qwen3.6-35b-a3b-nvfp4.gguf`	NVFP4	18.36 GiB	Text MoE LLM weights
`mmproj-qwen36-35b-src-BF16.gguf`	MMProj	903 MB	Vision encoder weights

Usage

llama.cpp CLI (text only)

llama-cli -hf FreedomAISVR/Qwen3.6-35B-A3B-NVFP4-GGUF -cnv -p "You are a helpful assistant"

llama-server (multimodal)

llama-server -hf FreedomAISVR/Qwen3.6-35B-A3B-NVFP4-GGUF --mmproj mmproj-qwen36-35b-src-BF16.gguf --ctx-size 0 --jinja

llama-cpp-python

python

from llama_cpp import Llama

llm = Llama.from_pretrained(
    repo_id="FreedomAISVR/Qwen3.6-35B-A3B-NVFP4-GGUF",
    filename="qwen3.6-35b-a3b-nvfp4.gguf",
)

Quantization Pipeline

bash

# 1. Convert HF model to intermediate GGUF
python convert_hf_to_gguf.py ./models/qwen3.6-35b/ --outfile qwen3.6-35b-a3b-f16.gguf --outtype bf16

# 2. Export vision encoder
python convert_hf_to_gguf.py ./models/qwen3.6-35b/ --mmproj --outtype bf16

# 3. Quantize to NVFP4
llama-quantize --allow-requantize qwen3.6-35b-a3b-f16.gguf qwen3.6-35b-a3b-nvfp4.gguf NVFP4

Hardware

GPU	VRAM	Notes
NVIDIA RTX 5060 Ti	16 GB	Quantization performed on this GPU

License

Apache-2.0 (same as Qwen/Qwen3.6-35B-A3B)