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---
base_model: meta-llama/Llama-3.3-70B-Instruct
language:
- en
- fr
- it
- pt
- hi
- es
- th
- de
library_name: transformers
license: llama3.3
tags:
- facebook
- meta
- pytorch
- llama
- llama-3
- mlx
extra_gated_prompt: "### LLAMA 3.3 COMMUNITY LICENSE AGREEMENT\nLlama 3.3 Version\
\ Release Date: December 6, 2024\n\"Agreement\" means the terms and conditions for\
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Meta is committed to promoting safe and fair use of its tools and features, including\
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\ (“**Policy**”). The most recent copy of this policy can be found at [https://www.llama.com/llama3\\\
_3/use-policy](https://www.llama.com/llama3_3/use-policy).\nProhibited Uses\nWe\
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\ * Reporting risky content generated by the model: [developers.facebook.com/llama\\\
_output\\_feedback](http://developers.facebook.com/llama_output_feedback) * Reporting\
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\ 3.3: LlamaUseReport@meta.com "
extra_gated_fields:
First Name: text
Last Name: text
Date of birth: date_picker
Country: country
Affiliation: text
Job title:
type: select
options:
- Student
- Research Graduate
- AI researcher
- AI developer/engineer
- Reporter
- Other
geo: ip_location
? By clicking Submit below I accept the terms of the license and acknowledge that
the information I provide will be collected stored processed and shared in accordance
with the Meta Privacy Policy
: checkbox
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and shared in accordance with the [Meta Privacy Policy](https://www.facebook.com/privacy/policy/).
extra_gated_button_content: Submit
---
# mlx-community/Llama-3.3-70B-Instruct-6bit
The Model [mlx-community/Llama-3.3-70B-Instruct-6bit](https://huggingface.co/mlx-community/Llama-3.3-70B-Instruct-6bit) was
converted to MLX format from [meta-llama/Llama-3.3-70B-Instruct](https://huggingface.co/meta-llama/Llama-3.3-70B-Instruct)
using mlx-lm version **0.20.1**.
## Use with mlx
```bash
pip install mlx-lm
```
```python
from mlx_lm import load, generate
model, tokenizer = load("mlx-community/Llama-3.3-70B-Instruct-6bit")
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)
```