johnlockejrr/Qwen2.5-Coder-14b-mxfp4

Qwen2.5­Code-14B - MLX mxfp4 Quantized

• Repository: johnlockejrr/qwen2.5-coder-14b-mxfp4
• Base model: https://huggingface.co/Qwen/Qwen2.5-Coder-14B
• Quantization: MLX mxfp4 (4-bit)
• Quantized by: johnlockejrr
• Framework: MLX + mlx-lm
• Quantization tool: https://github.com/EricFillion/quantize

---

Model Summary

This repository contains an MLX-quantized version of Qwen2.5-Coder-14B, optimized for Apple Silicon (M1/M2/M3/M4) devices. The model was quantized to mxfp4 (4-bit) using the MLX-based quantization tool by Eric Fillion, reducing memory usage from approximately 30 G B (FP16) to approximately 10.6 G B while maintaining strong coding performance.

This quantized model is suitable for:

• local coding assistants
• offline development workflows
• VS Code integration
• fast inference on Apple GUPs
• running large models on 16 G B or 24 G B Apple Silicon machines

---

Quantization Details

Setting	Value
Quantization mode	mxfp4
Bits per weight	4
Group size	64
Activation dtype	bfloat16
Framework	MLX
Quantization tool	EricFillion/quantize

Command used:

python3 quantize.py  \
  --model_name Qwen/Qwen2.5-Coder-14B \
  --save_model_path models/qwen2.5-coder-14b-mxfp4 \
  --q_mode mxfp4 \
  --q_bits 4 \
  --q_group_size 64

Resulting model size: approximately 10.6 G B

---

Running the Model (MLX)

CLI (mx-lm)

mlx_lm.generate \
  --model johnlockejrr/qwen2.5-coder-14b-mxfp4 \
  --prompt "Write a Python function to compute Fibonacci numbers."

Python API

from mlx_lm import load, generate

model, tokenizer = load("johnlockejrr/qwen2.5-coder-14b-mxfp4")

prompt = "Write a Python function to compute Fibonacci numbers."

output = generate(model, tokenizer, prompt, max_tokens=200)
print(output)

Chat Mode

from mx_lm import load, chat

model, tokenizer = load("johnlockejrr/qwen2.5-coder-14b-mxfp4")

messages = [
  {"role": "user", "content": "Explain what a binary search tree is."}
 ]

response = chat(model, tokenizer, messages)
print(response)

---

Performance (Mac Mini M4, 16 GB)

Metric	Value
Generation speed	approximately 9-12 tokens/sec
Peak memory usage	approximately 10.7 G B
GPU	Apple M4 GPU
Framework	MLX

---

Repository Contents

model-00001-of-00002.safetensors
model-00002-of-00002.safetensors
model.safetensors.index.json
config.json
tokenizer.json
tokenizer_config.json
chat_template.jinja
generation_config.json
README.md

---

License

This model inherits the license of the original model:

Qwen2.5-Coder-14B License: https://huggingface.co/Qwen/Qwen2.5-Coder-14B#license

Please review the license before using this model in commercial applications.

---

Limitations and Bias

• The model may generate incorrect or insecure code.
• It may hallucinate APIs or functions.
• It may produce biased or harmful statements if prompted.
• It should not be used for production-critical code without human review.

---

Acknowledgements

• Qwen Team for the original Qwen2.5-Coder-14B model
• Apple MLX Team for the MLX framework
• Eric Fillion for the MLX quantization tool
• Hugging Face for hosting the model