Qwen3.8-27B-nvfp4-mlx

Qwen3.8-27B NVFP4 (MLX)

This model was converted to MLX format from mlx-community/Qwen3.8-27B-bf16 using mlx-vlm version 0.6.13. Refer to the original model card for more details on the model.

<p align="center"> <a href="https://github.com/ml-explore/mlx"><img src="https://img.shields.io/badge/MLX-Apple_Silicon-black?style=for-the-badge&logo=apple" alt="MLX"></a> <a href="https://huggingface.co/zherebetskyy/Qwen3.8-27B-4bit-mlx"><img src="https://img.shields.io/badge/Q4_4--bit-Variant-blue?style=for-the-badge" alt="Q4 Variant"></a> <a href="https://huggingface.co/zherebetskyy/Qwen3.8-27B-6bit-mlx"><img src="https://img.shields.io/badge/Q6_6--bit-Variant-green?style=for-the-badge" alt="Q6 Variant"></a> <a href="https://huggingface.co/zherebetskyy/Qwen3.8-27B-nvfp4-mlx/tree/main"><img src="https://img.shields.io/badge/License-Apache_2.0-yellow.svg?style=for-the-badge" alt="License"></a> </p>

This repository contains Qwen3.8-27B quantized to NVFP4 (4-bit Floating-Point with Normalization/Scaling) for Apple Silicon devices using MLX.

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🚀 Why NVFP4 over Standard INT4 / Q4?

Traditional INT4 (Q4) quantization uniformly discretizes weights into integers. While memory-efficient, integer quantization often suffers from noticeable perplexity degradation—especially in large-scale multimodal models with non-uniform weight distributions and sensitive activations.

Key Advantages of NVFP4:

1. Logarithmic/Dynamic Range: NVFP4 utilizes a dynamic 4-bit floating-point format (E2M1/E3M0 structure) rather than fixed integer steps. This represents smaller parameters with higher precision and maintains wider dynamic range for outlier activation scales.
2. Superior Perplexity & Coherence: Retains near-BF16 reasoning accuracy, coding proficiency, and complex context tracking compared to standard INT4/Q4 quantizations.
3. Optimized Memory Footprint: Delivers performance almost equivalent to Q6, while keeping memory consumption close to Q4 (~18–19 GB VRAM).
4. Enhanced Multimodal Preservation: Crucial for vision-language models where visual projection layers are particularly sensitive to coarse integer rounding.

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📊 Quantization Comparison & Selection Guide

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💻 Hardware Requirements

• Processor: Apple Silicon (M1/M2/M3/M4 Max or Ultra recommended).
• Unified Memory:
  • Minimum: 24 GB (for short contexts).
  • Recommended: 36 GB+ (for long-context multimodal processing and fast generation).

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🛠️ Quickstart Guide

1. Python API (mlx-vlm)

Install or upgrade mlx-vlm:

pip install -U mlx-vlm

python -m mlx_vlm.generate --model zherebetskyy/Qwen3.8-27B-nvfp4-mlx --max-tokens 100 --temperature 0.0 --prompt "Describe this image." --image <path_to_image>

Run vision-language inference in Python:

from mlx_vlm import load, generate
from mlx_vlm.prompt_utils import apply_chat_template
from mlx_vlm.utils import load_config

# Load model and processor
model_id = "zherebetskyy/Qwen3.8-27B-nvfp4-mlx"
model, processor = load(model_id)
config = load_config(model_id)

# Input image and prompt
image = ["[http://images.cocodataset.org/val2017/000000039769.jpg](http://images.cocodataset.org/val2017/000000039769.jpg)"]
prompt = "Analyze this image and describe the key elements in detail."

# Generate output
formatted_prompt = apply_chat_template(processor, config, prompt, num_images=1)
output = generate(model, processor, formatted_prompt, image, max_tokens=500, temperature=0.2)

print(output)

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2. Command Line Interface (CLI)

python -m mlx_vlm.generate \
  --model zherebetskyy/Qwen3.8-27B-nvfp4-mlx \
  --max-tokens 500 \
  --temperature 0.2 \
  --prompt "Describe this image." \
  --image <path_to_image.jpg>

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3. Running as a Local Server (OpenAI-Compatible)

Start an mlx_lm / mlx_vlm local OpenAI-compatible API server:

# Install tool
uv tool install mlx-lm

# Launch local server
mlx_lm.server --model "zherebetskyy/Qwen3.8-27B-nvfp4-mlx" --port 8080

You can now connect local agent frameworks (e.g., Pi, OpenClaw, Hermes, or LM Studio) to http://localhost:8080/v1.

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📜 License

This model is licensed under the Apache 2.0 License, matching the original base release.

🙏 Acknowledgments & References

• Base Model: Qwen/Qwen3.8-27B
• Quantized with MLX VLM by Apple.
• Check out alternative MLX variants: Qwen3.8-27B 4-bit (Q4) and Qwen3.8-27B 6-bit (Q6) and [Qwen3.8-27B 8-bit (Q8)] (https://huggingface.co/zherebetskyy/Qwen3.8-27B-mxfp8-mlx).