arthurcollet/Mellum-4b-sft-all-mlx-8bit
This model arthurcollet/Mellum-4b-sft-all-mlx-8bit was converted to MLX format from JetBrains/Mellum-4b-sft-all using mlx-lm version 0.28.0.
Use with mlx
pip install mlx-lm
from mlx_lm import load, generate
model, tokenizer = load("arthurcollet/Mellum-4b-sft-all-mlx-8bit")
prompt = "hello"
if tokenizer.chat_template is not None:
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
messages, add_generation_prompt=True
)
response = generate(model, tokenizer, prompt=prompt, verbose=True)
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Model size
1B params
Tensor type
BF16
·
U32 ·
Hardware compatibility
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8-bit
Model tree for arthurcollet/Mellum-4b-sft-all-mlx-8bit
Datasets used to train arthurcollet/Mellum-4b-sft-all-mlx-8bit
Evaluation results
- EM on RepoBench 1.1 (Python)self-reported0.282
- EM ≤ 8k on RepoBench 1.1 (Python)self-reported0.287
- EM on RepoBench 1.1 (Python)self-reported0.264
- EM on RepoBench 1.1 (Python)self-reported0.293
- EM on RepoBench 1.1 (Python)self-reported0.304
- EM on RepoBench 1.1 (Python)self-reported0.269
- EM on RepoBench 1.1 (Python)self-reported0.282
- EM on RepoBench 1.1 (Java)self-reported0.287
- EM ≤ 8k on RepoBench 1.1 (Java)self-reported0.302
- EM on RepoBench 1.1 (Java)self-reported0.288
- EM on RepoBench 1.1 (Java)self-reported0.323
- EM on RepoBench 1.1 (Java)self-reported0.296
- EM on RepoBench 1.1 (Java)self-reported0.245
- EM on RepoBench 1.1 (Java)self-reported0.282
- pass@1 on SAFIMself-reported0.528
- pass@1 on SAFIMself-reported0.655
- pass@1 on SAFIMself-reported0.401
- pass@1 on SAFIMself-reported0.530
- pass@1 on HumanEval Infilling (Single-Line)self-reported0.808
- pass@1 on HumanEval Infilling (Single-Line)self-reported0.482