---
base_model: tokyotech-llm/Llama-3.1-Swallow-70B-Instruct-v0.1
datasets:
- lmsys/lmsys-chat-1m
- argilla/magpie-ultra-v0.1
language:
- en
- ja
library_name: transformers
license: llama3.1
pipeline_tag: text-generation
tags:
- mlx
model_type: llama
---

# mlx-community/Llama-3.1-Swallow-70B-Instruct-v0.1-4bit

The Model [mlx-community/Llama-3.1-Swallow-70B-Instruct-v0.1-4bit](https://huggingface.co/mlx-community/Llama-3.1-Swallow-70B-Instruct-v0.1-4bit) was converted to MLX format from [tokyotech-llm/Llama-3.1-Swallow-70B-Instruct-v0.1](https://huggingface.co/tokyotech-llm/Llama-3.1-Swallow-70B-Instruct-v0.1) using mlx-lm version **0.19.0**.

## Use with mlx

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

```python
from mlx_lm import load, generate

model, tokenizer = load("mlx-community/Llama-3.1-Swallow-70B-Instruct-v0.1-4bit")

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)
```