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mlx-community
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bigcode-starcoder2-15b-8bit

Text Generation
Transformers
Safetensors
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starcoder2
code
Eval Results
text-generation-inference
8-bit precision
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Community
bigcode-starcoder2-15b-8bit
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---
pipeline_tag: text-generation
inference:
  parameters:
    temperature: 0.2
    top_p: 0.95
widget:
- text: 'def print_hello_world():'
  example_title: Hello world
  group: Python
datasets:
- bigcode/the-stack-v2-train
license: bigcode-openrail-m
library_name: transformers
tags:
- code
- mlx
base_model: bigcode/starcoder2-15b
model-index:
- name: starcoder2-15b
  results:
  - task:
      type: text-generation
    dataset:
      name: CruxEval-I
      type: cruxeval-i
    metrics:
    - type: pass@1
      value: 48.1
  - task:
      type: text-generation
    dataset:
      name: DS-1000
      type: ds-1000
    metrics:
    - type: pass@1
      value: 33.8
  - task:
      type: text-generation
    dataset:
      name: GSM8K (PAL)
      type: gsm8k-pal
    metrics:
    - type: accuracy
      value: 65.1
  - task:
      type: text-generation
    dataset:
      name: HumanEval+
      type: humanevalplus
    metrics:
    - type: pass@1
      value: 37.8
  - task:
      type: text-generation
    dataset:
      name: HumanEval
      type: humaneval
    metrics:
    - type: pass@1
      value: 46.3
  - task:
      type: text-generation
    dataset:
      name: RepoBench-v1.1
      type: repobench-v1.1
    metrics:
    - type: edit-smiliarity
      value: 74.08
---

# mlx-community/bigcode-starcoder2-15b-8bit

The Model [mlx-community/bigcode-starcoder2-15b-8bit](https://huggingface.co/mlx-community/bigcode-starcoder2-15b-8bit) was
converted to MLX format from [bigcode/starcoder2-15b](https://huggingface.co/bigcode/starcoder2-15b)
using mlx-lm version **0.21.1**.

## Use with mlx

```bash
pip install mlx-lm
```

```python
from mlx_lm import load, generate

model, tokenizer = load("mlx-community/bigcode-starcoder2-15b-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)
```