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| This model | FP16 baseline | |
|---|---|---|
| Decode tok/s (avg, long traces) | 59.23 | N/A |
| Peak memory (GB) | 18.271 | N/A |
| Disk size (MB) | 16777 | 58175 |
| Benchmark | This model | FP16 baseline | n |
|---|---|---|---|
| MATH-500 (math reasoning) | 96.7% (answered 30/30) | N/A | 30 |
| IFEval (instruction following) | 38.6% | N/A | 44 |
| LiveCodeBench v6 (code, pass@1) | 56.7% | N/A | 30 |
| HumanEval (code, pass@1) | 83.3% | N/A | 30 |
| Level | This model | FP16 baseline |
|---|---|---|
| level 1 | 100.0% | N/A |
| level 2 | 100.0% | N/A |
| level 3 | 83.3% | N/A |
| level 4 | 100.0% | N/A |
| level 5 | 100.0% | N/A |
| Context length | Decode tok/s |
|---|---|
| ~128 tokens | 62.8 |
| ~256 tokens | 62.4 |
| ~512 tokens | 62.4 |
| ~1024 tokens | 61.6 |
pip install mlx-lm1from mlx_lm import load, generate
2
3model, tokenizer = load("sahilchachra/north-mini-code-mxfp4-mlx")
4response = generate(model, tokenizer, prompt="Your prompt here", max_tokens=256, verbose=True)| Model | Variant |
|---|---|
| sahilchachra/north-mini-code-mxfp4-mlx | Block float MX FP4 ← this model |