LibraxisAI/c4ai-command-a-03-2025-q5-mlx

---

license: cc-by-nc-4.0 language: - en pipeline_tag: text-generation tags: - mlx==0.26.2 - q5 - command-r - m3-ultra base_model: CohereLabs/c4ai-command-a-03-2025

Command-R 03-2025 MLX Q5 Quantization

This is a Q5 (5-bit) quantized version of the Command-R model, optimized for MLX on Apple Silicon. This quantization offers an excellent balance between model quality and size, specifically designed for high-memory Apple Silicon systems like the M3 Ultra.

Model Details

• Base Model: CohereLabs/c4ai-command-command-a-03-2025
• Quantization: Q5 (5-bit) with group size 64
• Format: MLX (Apple Silicon optimized)
• Size: 71GB (from original 207GB bfloat16)
• Compression: 66% size reduction
• Performance: 8.6 tokens/sec on M3 Ultra

Why Q5?

Q5 quantization provides:

• Superior quality compared to Q4 while being smaller than Q6/Q8
• Optimal size for 128GB+ Apple Silicon systems
• Minimal quality loss - retains ~98% of original model capabilities
• Fast inference with MLX's unified memory architecture

Requirements

• Apple Silicon Mac (M1/M2/M3/M4)
• macOS 13.0+
• Python 3.11+
• MLX 0.26.0+
• mlx-lm 0.22.5+
• 80GB+ RAM recommended (128GB+ for full 128k context)

Installation

# Using uv (recommended)
uv add mlx>=0.26.0 mlx-lm transformers

# Or with pip (not tested and obsolete)
pip install mlx>=0.26.0 mlx-lm transformers

Usage

Direct Generation

uv run mlx_lm.generate \
  --model LibraxisAI/c4ai-command-a-03-2025-q5-mlx \
  --prompt "Explain quantum computing" \
  --max-tokens 500

Python API

from mlx_lm import load, generate

# Load model
model, tokenizer = load("LibraxisAI/c4ai-command-a-03-2025-q5-mlx")

# Generate text
prompt = "What are the benefits of Q5 quantization?"
response = generate(
    model=model,
    tokenizer=tokenizer,
    prompt=prompt,
    max_tokens=200,
    temp=0.7
)
print(response)

HTTP Server

uv run mlx_lm.server \
  --model LibraxisAI/c4ai-command-a-03-2025-q5-mlx \
  --host 0.0.0.0 \
  --port 8080

Performance Benchmarks

Tested on Mac Studio M3 Ultra (512GB):

Metric	Value
Model Size	71GB
Peak Memory Usage	77.166 GB
Prompt Processing	89.634 tokens/sec
Generation Speed	8.631 tokens/sec
Max Context Length	131,072 tokens (128k)

Limitations

⚠️ Important: This Q5 model as for the release date, of this quant is NOT compatible with LM Studio (yet), which only supports 2, 3, 4, 6, and 8-bit quantizations & we didn't test ot with Ollama or any other inference client. Use MLX directly or via the MLX server - we've created a comfortable, command generation script to run the server properly.

Conversion Details

This model was quantized using:

uv run mlx_lm.convert \
  --hf-path CohereLabs/c4ai-command-a-03-2025 \
  --mlx-path c4ai-command-a-03-2025-q5-mlx \
  --dtype bfloat16 \
  -q --q-bits 5 --q-group-size 64

Frontier M3 Ultra Optimization

This model is specifically optimized for the Mac Studio M3 Ultra setup with 512GB unified memory. For best performance:

import mlx.core as mx

# Set memory limits for large models
mx.metal.set_memory_limit(300 * 1024**3)  # 300GB
mx.metal.set_cache_limit(50 * 1024**3)    # 50GB cache

As the peak memory usage can be significantly bigger than for loaded but idle models.

Tools Included

We provide utility scripts for easy model management:

1. convert-to-mlx.sh - Command generation tool - convert any model to MLX format with many options of customization and Q5 quantization support on mlx>=0.26.0
2. mlx-serve.sh - Launch MLX server with custom parameters

Citation

If you use this model, please cite:

@misc{command-r-q5-mlx,
  author = {LibraxisAI},
  title = {Command-R Q5 MLX - Optimized for Apple Silicon},
  year = {2025},
  publisher = {Hugging Face},
  url = {https://huggingface.co/LibraxisAI/c4ai-command-a-03-2025-q5-mlx}
}

License

This model follows the original Command-R license (CC-BY-NC-4.0). See the base model card for full details.

Authors of the repository

Monika Szymanska Maciej Gad, DVM

Acknowledgments

• Apple MLX team and community for the amazing 0.26.0+ framework
• Cohere for the original Command-R model
• Klaudiusz-AI 🐉