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johnlockejrr/Qwen2.5-14B-Instruct-mxfp4johnlockejrr| Setting | Value |
|---|---|
| Quantization mode | mxfp4 |
| Bits per weight | 4 |
| Group size | 64 |
| Activation dtype | bfloat16 |
| Framework | MLX |
| Quantization tool | EricFillion/quantize |
1python3 quantize.py \
2 --model_name Qwen/Qwen2.5-14B-Instruct \
3 --save_model_path models/Qwen2.5-14B-Instruct-mxfp4 \
4 --q_mode mxfp4 \
5 --q_bits 4 \
6 --q_group_size 641mlx_lm.generate \
2 --model johnlockejrr/Qwen2.5-14B-Instruct-mxfp4 \
3 --prompt "Explain the concept of recursion with an example." \
4 --max-tokens 5121from mlx_lm import load, generate
2
3model, tokenizer = load("johnlockejrr/Qwen2.5-14B-Instruct-mxfp4")
4
5prompt = "Give me a short explanation of transformers in machine learning."
6
7output = generate(model, tokenizer, prompt, max_tokens=200)
8print(output)1from mlx_lm import load, chat
2
3model, tokenizer = load("johnlockejrr/Qwen2.5-14B-Instruct-mxfp4")
4
5messages = [
6 {"role": "user", "content": "What is a monad in functional programming?"}
7]
8
9response = chat(model, tokenizer, messages)
10print(response)| Metric | Value |
|---|---|
| Generation speed | approximately 8-12 tokens/sec |
| Peak memory usage | approximately 10.5 G B |
| GPU | Apple M4 GPU |
| Framework | MLX |
1model-00001-of-00002.safetensors
2model-00002-of-00002.safetensors
3model.safetensors.index.json
4config.json
5tokenizer.json
6tokenizer_config.json
7chat_template.jinja
8generation_config.json
9README.md