cnfusion/Mellum-4b-sft-python-mlx-8Bit
The Model cnfusion/Mellum-4b-sft-python-mlx-8Bit was converted to MLX format from JetBrains/Mellum-4b-sft-python using mlx-lm version 0.22.3.
Use with mlx
pip install mlx-lm
from mlx_lm import load, generate
model, tokenizer = load("cnfusion/Mellum-4b-sft-python-mlx-8Bit")
prompt="hello"
if hasattr(tokenizer, "apply_chat_template") and tokenizer.chat_template is not None:
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
messages, tokenize=False, add_generation_prompt=True
)
response = generate(model, tokenizer, prompt=prompt, verbose=True)
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Model tree for cnfusion/Mellum-4b-sft-python-mlx-8Bit
Base model
JetBrains/Mellum-4b-base
Finetuned
JetBrains/Mellum-4b-sft-python
Datasets used to train cnfusion/Mellum-4b-sft-python-mlx-8Bit
Evaluation results
- EM on RepoBench 1.1 (Python)self-reported0.284
- EM ≤ 8k on RepoBench 1.1 (Python)self-reported0.299
- EM on RepoBench 1.1 (Python)self-reported0.292
- EM on RepoBench 1.1 (Python)self-reported0.306
- EM on RepoBench 1.1 (Python)self-reported0.298
- EM on RepoBench 1.1 (Python)self-reported0.268
- EM on RepoBench 1.1 (Python)self-reported0.254
- pass@1 on SAFIMself-reported0.421
- pass@1 on SAFIMself-reported0.332
- pass@1 on SAFIMself-reported0.361