metadata
base_model: meta-llama/Meta-Llama-3.1-8B-Instruct
language:
- en
- de
- fr
- it
- pt
- hi
- es
- th
license: llama3.1
pipeline_tag: text-generation
tags:
- facebook
- meta
- pytorch
- llama
- llama-3
- mlx
extra_gated_prompt: >-
### LLAMA 3.1 COMMUNITY LICENSE AGREEMENT
Llama 3.1 Version Release Date: July 23, 2024
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### Llama 3.1 Acceptable Use Policy
Meta is committed to promoting safe and fair use of its tools and features,
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[https://llama.meta.com/llama3_1/use-policy](https://llama.meta.com/llama3_1/use-policy)
#### Prohibited Uses
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Please report any violation of this Policy, software “bug,” or other problems
that could lead to a violation of this Policy through one of the following
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* Reporting issues with the model: [https://github.com/meta-llama/llama-models/issues](https://github.com/meta-llama/llama-models/issues)
* Reporting risky content generated by the model:
developers.facebook.com/llama_output_feedback
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* Reporting violations of the Acceptable Use Policy or unlicensed uses of Meta Llama 3: [email protected]
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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mlx-community/Meta-Llama-3.1-8B-Instruct-bf16
The Model mlx-community/Meta-Llama-3.1-8B-Instruct-bf16 was converted to MLX format from meta-llama/Meta-Llama-3.1-8B-Instruct using mlx-lm version 0.19.0.
Use with mlx
pip install mlx-lm
from mlx_lm import load, generate
model, tokenizer = load("mlx-community/Meta-Llama-3.1-8B-Instruct-bf16")
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)