---
license: apache-2.0
datasets:
- Nikity/Kyoto-Corpus
language:
- en
base_model: mlx-community/lille-130m-instruct-fp16
base_model_relation: finetune
pipeline_tag: text-generation
tags:
- mlx
library_name: mlx
model-index:
- name: lille-130m-instruct
  results:
  - task:
      type: text-generation
    dataset:
      name: arc_challenge
      type: arc_challenge
    metrics:
    - type: Accuracy
      value: 15.05
      name: ARC (Challenge)
  - task:
      type: text-generation
    dataset:
      name: arc_easy
      type: arc_easy
    metrics:
    - type: Accuracy
      value: 21.4
      name: ARC (Easy)
  - task:
      type: text-generation
    dataset:
      name: gpqa
      type: gpqa
    metrics:
    - type: Accuracy
      value: 12.73
      name: GPQA
  - task:
      type: text-generation
    dataset:
      name: gsm8k
      type: gsm8k
    metrics:
    - type: Accuracy
      value: 7.73
      name: GSM8K
  - task:
      type: text-generation
    dataset:
      name: ifeval
      type: ifeval
    metrics:
    - type: Accuracy
      value: 9.01
      name: IFEVAL
  - task:
      type: text-generation
    dataset:
      name: math
      type: math
    metrics:
    - type: Accuracy
      value: 1.91
      name: MATH (Level 5)
  - task:
      type: text-generation
    dataset:
      name: mmlu
      type: mmlu
    metrics:
    - type: Accuracy
      value: 22.76
      name: MMLU
  - task:
      type: text-generation
    dataset:
      name: mt_bench
      type: mt_bench
    metrics:
    - type: Accuracy
      value: 8.2
      name: MT-Bench
  - task:
      type: text-generation
    dataset:
      name: truthful_qa
      type: truthful_qa
    metrics:
    - type: Accuracy
      value: 9.06
      name: TruthfulQA
---

# mlx-community/lille-130m-instruct-6bit

This model [mlx-community/lille-130m-instruct-6bit](https://huggingface.co/mlx-community/lille-130m-instruct-6bit) was
converted to MLX format from [mlx-community/lille-130m-instruct-fp16](https://huggingface.co/mlx-community/lille-130m-instruct-fp16)
using mlx-lm version **0.27.1**.

## Use with mlx

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

```python
from mlx_lm import load, generate

model, tokenizer = load("mlx-community/lille-130m-instruct-6bit")

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)
```