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README.md
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---
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license: apache-2.0
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datasets:
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- bigcode/the-stack
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- bigcode/the-stack-v2
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- bigcode/starcoderdata
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- bigcode/commitpack
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library_name: transformers
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tags:
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- code
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- mlx
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- mlx-my-repo
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base_model: JetBrains/Mellum-4b-sft-python
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model-index:
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- name: Mellum-4b-sft-python
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results:
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- task:
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type: text-generation
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dataset:
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name: RepoBench 1.1 (Python)
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type: tianyang/repobench_python_v1.1
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metrics:
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- type: exact_match
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value: 0.2837
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name: EM
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verified: false
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- type: exact_match
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value: 0.2987
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name: EM ≤ 8k
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verified: false
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- type: exact_match
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value: 0.2924
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name: EM
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verified: false
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- type: exact_match
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value: 0.306
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name: EM
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verified: false
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- type: exact_match
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value: 0.2977
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name: EM
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verified: false
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- type: exact_match
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value: 0.268
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name: EM
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verified: false
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- type: exact_match
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value: 0.2543
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name: EM
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verified: false
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- task:
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type: text-generation
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dataset:
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name: SAFIM
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type: gonglinyuan/safim
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metrics:
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- type: pass@1
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value: 0.4212
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name: pass@1
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verified: false
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- type: pass@1
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value: 0.3316
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name: pass@1
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verified: false
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- type: pass@1
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value: 0.3611
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name: pass@1
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verified: false
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- type: pass@1
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value: 0.571
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name: pass@1
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verified: false
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- task:
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type: text-generation
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dataset:
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name: HumanEval Infilling (Single-Line)
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type: loubnabnl/humaneval_infilling
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metrics:
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- type: pass@1
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value: 0.8045
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name: pass@1
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verified: false
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- type: pass@1
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value: 0.4819
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name: pass@1
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verified: false
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- type: pass@1
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value: 0.3768
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name: pass@1
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verified: false
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---
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# cnfusion/Mellum-4b-sft-python-mlx-fp16
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The Model [cnfusion/Mellum-4b-sft-python-mlx-fp16](https://huggingface.co/cnfusion/Mellum-4b-sft-python-mlx-fp16) was converted to MLX format from [JetBrains/Mellum-4b-sft-python](https://huggingface.co/JetBrains/Mellum-4b-sft-python) using mlx-lm version **0.22.3**.
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## Use with mlx
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```bash
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pip install mlx-lm
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```
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```python
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from mlx_lm import load, generate
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model, tokenizer = load("cnfusion/Mellum-4b-sft-python-mlx-fp16")
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prompt="hello"
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if hasattr(tokenizer, "apply_chat_template") and tokenizer.chat_template is not None:
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messages = [{"role": "user", "content": prompt}]
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prompt = tokenizer.apply_chat_template(
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messages, tokenize=False, add_generation_prompt=True
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)
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response = generate(model, tokenizer, prompt=prompt, verbose=True)
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```
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