Qwen3.5-4B-MTP-NVFP4-GGUF

NVFP4 (NVIDIA Blackwell FP4) GGUF quantization of Qwen/Qwen3.5-4B with Multi-Token Prediction (MTP) support.

Thinking is enabled by default. You may disable it by passing enable_thinking=false in the generation parameters. This matches the original model's behavior.

About NVFP4

NVFP4 is NVIDIA's native 4-bit floating-point format (E4M3) designed for Blackwell architecture GPUs (RTX 5000 series). It offers several advantages over traditional integer quantization formats:

Feature	NVFP4 (E4M3)	INT4 (Q4_K_M, etc.)
Block size	16 elements	32 elements
Dynamic range	±448	±7
Hardware acceleration	Blackwell Tensor Cores	CPU/CUDA cores
Dequantization overhead	None (native)	Required
BPW	~4.5-4.8	~4.5

When to use NVFP4:

• You have an RTX 5060/5070/5080/5090 (Blackwell GPU)
• You want optimal inference speed with native FP4 tensor cores
• You need the best quality-per-bit among 4-bit formats

When to use other formats:

• Pre-Blackwell GPUs: Use Q4_K_M or Q4_0
• CPU inference: Use Q4_K_M or Q5_K_M
• Maximum quality: Use Q6_K or Q8_0

With MTP enabled, the model predicts 2 tokens per inference step (1 base + 1 predicted), increasing throughput by up to 30-40% for text generation while maintaining quality.

Files

Filename	Type	Size	Description
qwen35-4b-mtp-nvfp4.gguf	Model	2.48 GB	NVFP4 quantized text model with MTP
mmproj-qwen35-4b-f16.gguf	Vision encoder	672 MB	Multimodal projector (SigLIP ViT, F16)

Quantization Details

Property	Value
Format	NVFP4 (E4M3)
Block size	16
BPW	4.81
HW target	NVIDIA Blackwell (RTX 5000 series)
VRAM required	~3 GB (with mmproj)
MTP heads	1 (nextn_predict_layers=1)
Thinking	Enabled by default (opt-out via enable_thinking=false)

Model Description

Qwen3.5-4B is a multilingual multimodal model from the Qwen team at Alibaba, featuring:

• 2560-dimensional hidden states
• 9216-dimensional feed-forward layers
• 32 base transformer layers + 1 MTP head (33 total blocks)
• Multi-Head Latent Attention (MLA) with per-head KV norms
• Hybrid architecture: Attention + SSM (Mamba-2) layers
• 262,144 token context window
• 3D MRoPE (Multi-modal Rotary Position Embedding)
• Built-in vision encoder (SigLIP ViT)

Usage

llama.cpp CLI (text-only)

./llama-cli -m qwen35-4b-mtp-nvfp4.gguf \
  -p "Hello, how are you?" \
  -n 256

llama.cpp CLI (multimodal — with vision)

./llama-cli -m qwen35-4b-mtp-nvfp4.gguf \
  --mmproj mmproj-qwen35-4b-f16.gguf \
  --image photo.jpg \
  -p "Describe this image" \
  -n 256

llama-cpp-python

from llama_cpp import Llama

llm = Llama(
    model_path="qwen35-4b-mtp-nvfp4.gguf",
    n_ctx=32768,
    n_gpu_layers=-1,  # Full GPU offload
)

output = llm.create_chat_completion(
    messages=[{"role": "user", "content": "Hello!"}],
    max_tokens=256,
)
print(output["choices"][0]["message"]["content"])

Download from HuggingFace Hub

huggingface-cli download FreedomAISVR/Qwen3.5-4B-MTP-NVFP4-GGUF \
  qwen35-4b-mtp-nvfp4.gguf \
  --local-dir . --local-dir-use-symlinks False

# Also download mmproj for vision support:
huggingface-cli download FreedomAISVR/Qwen3.5-4B-MTP-NVFP4-GGUF \
  mmproj-qwen35-4b-f16.gguf \
  --local-dir . --local-dir-use-symlinks False

Conversion Pipeline

# 1. Download source model
huggingface-cli download Qwen/Qwen3.5-4B --local-dir ./qwen35-4b-src

# 2. Convert to F16 GGUF (MTP auto-included)
python convert_hf_to_gguf.py ./qwen35-4b-src \
  --outfile qwen35-4b-f16.gguf --outtype f16

# 3. Extract vision encoder mmproj
python convert_hf_to_gguf.py ./qwen35-4b-src \
  --mmproj --outfile mmproj-qwen35-4b-f16.gguf --outtype f16

# 4. Quantize to NVFP4
./llama-quantize qwen35-4b-f16.gguf \
  qwen35-4b-mtp-nvfp4.gguf NVFP4

Hardware

Component	Specification
GPU	RTX 5060 Ti 16GB (Blackwell)
VRAM	16 GB GDDR7
System RAM	64 GB
OS	Windows 11

License

This quantization is distributed under Apache 2.0. The original Qwen3.5-4B model is released under Apache 2.0 by Qwen (Alibaba Group).