Qwen3.5-9B-Instruct-MXFP4-GGUF

Qwen3.5-9B-Instruct-MXFP4-GGUF

MXFP4 (OCP Microscaling FP4) GGUF quantization of the Qwen3.5-9B-Instruct multimodal language model with thinking disabled by default.

About MXFP4

MXFP4 (E2M1) is the open-standard 4-bit floating-point format defined by the OCP Microscaling Formats (MX) specification. Unlike block-scaled integer formats (Q4_K_M), MXFP4 uses per-group scaling factors (block size 32) with shared exponent via the E2M1 format (2 exponent bits, 1 mantissa bit). This provides better dynamic range utilization than integer formats and works across GPU vendors (NVIDIA, AMD, Intel) and CPUs.

Key Advantages

• Cross-vendor: Runs on any GPU or CPU (no vendor lock-in)
• E2M1 format: 2 exponent bits + 1 mantissa bit + shared scale per 32 elements
• Floating-point: Better dynamic range than integer quantization
• Open standard: OCP MX specification, widely supported

Repo Contents

Quantization Details

Usage

llama.cpp

# Basic text generation (thinking disabled by default)
./llama-cli -m qwen35-9b-instruct-mxfp4.gguf -p "What is the capital of France?" -n 256

# Vision (requires mmproj)
./llama-cli -m qwen35-9b-instruct-mxfp4.gguf --mmproj mmproj-qwen35-9b-f16.gguf -p "Describe this image" --image photo.jpg -n 256

# Enable thinking
./llama-cli -m qwen35-9b-instruct-mxfp4.gguf -p "Solve: 2+2=?" -n 512 -e enable_thinking=true

Python

from llama_cpp import Llama

llm = Llama(
    model_path="qwen35-9b-instruct-mxfp4.gguf",
    n_ctx=8192,
    chat_format="qwen3",
)

# Basic chat (no thinking)
output = llm.create_chat_completion([{"role": "user", "content": "What is AI?"}])

# With thinking enabled
output = llm.create_chat_completion(
    [{"role": "user", "content": "Solve 2+2=?"}],
    extra_body={"enable_thinking": True},
)

Download

huggingface-cli download FreedomAISVR/Qwen3.5-9B-Instruct-MXFP4-GGUF --local-dir . --local-dir-use-symlinks False

Original Model

Qwen3.5-9B is Alibaba Cloud's efficient multimodal foundation model (Apache 2.0, March 2026) featuring:

• Hybrid Gated DeltaNet + Gated Attention (3:1 ratio)
• 262K native context (extensible to 1M)
• Text + Image + Video input
• 201 language support

Conversion

python convert_hf_to_gguf.py --no-mtp --outfile qwen35-9b-f16.gguf D:\qwen35-9b-src
python convert_hf_to_gguf.py --mmproj --outfile mmproj-qwen35-9b-f16.gguf D:\qwen35-9b-src
llama-quantize.exe qwen35-9b-f16.gguf qwen35-9b-instruct-mxfp4.gguf MXFP4

License

Apache 2.0 (same as Qwen3.5-9B)