---
language:
- en
pipeline_tag: text-generation
tags:
- facebook
- meta
- pytorch
- llama
- llama-3
license: other
license_name: llama3
license_link: LICENSE
extra_gated_prompt: >-
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extra_gated_fields:
  First Name: text
  Last Name: text
  Date of birth: date_picker
  Country: country
  Affiliation: text
  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
extra_gated_description: The information you provide will be collected, stored, processed and shared in accordance with the [Meta Privacy Policy](https://www.facebook.com/privacy/policy/).
extra_gated_button_content: Submit
widget:
  - example_title: Hello
    messages:
    - role: user
      content: Hey my name is Julien! How are you?
  - example_title: Winter holidays
    messages:
    - role: system
      content: You are a helpful and honest assistant. Please, respond concisely and truthfully.
    - role: user
      content: Can you recommend a good destination for Winter holidays?
  - example_title: Programming assistant
    messages:
    - role: system
      content: You are a helpful and honest code and programming assistant. Please, respond concisely and truthfully.
    - role: user
      content: Write a function that computes the nth fibonacci number.
inference:
  parameters:
    max_new_tokens: 300
    stop:
    - <|end_of_text|>
    - <|eot_id|>
quantized_by: bartowski
---

## 4-bit GEMM AWQ  Quantizations of Meta-Llama-3-8B-Instruct

Using <a href="https://github.com/casper-hansen/AutoAWQ/">AutoAWQ</a> release <a href="https://github.com/casper-hansen/AutoAWQ/releases/tag/v0.2.4">v0.2.4</a> for quantization.

Original model: https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct

## Prompt format

```
<|begin_of_text|><|start_header_id|>system<|end_header_id|>

{system_prompt}<|eot_id|><|start_header_id|>user<|end_header_id|>

{prompt}<|eot_id|><|start_header_id|>assistant<|end_header_id|>


```

## AWQ Parameters

 - q_group_size: 128
 - w_bit: 4
 - zero_point: True
 - version: GEMM

## How to run

From the AutoAWQ repo [here](https://github.com/casper-hansen/AutoAWQ/blob/main/examples/generate.py)

First install autoawq pypi package:

```
pip install autoawq
```

Then run the following:

```
from awq import AutoAWQForCausalLM
from transformers import AutoTokenizer, TextStreamer


quant_path = "models/Meta-Llama-3-8B-Instruct-AWQ"

# Load model
model = AutoAWQForCausalLM.from_quantized(quant_path, fuse_layers=True)
tokenizer = AutoTokenizer.from_pretrained(quant_path, trust_remote_code=True)
streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)

prompt = "You're standing on the surface of the Earth. "\
        "You walk one mile south, one mile west and one mile north. "\
        "You end up exactly where you started. Where are you?"

chat = [
    {"role": "system", "content": "You are a concise assistant that helps answer questions."},
    {"role": "user", "content": prompt},
]

# <|eot_id|> used for llama 3 models
terminators = [
    tokenizer.eos_token_id,
    tokenizer.convert_tokens_to_ids("<|eot_id|>")
]

tokens = tokenizer.apply_chat_template(
    chat,
    return_tensors="pt"
).cuda()

# Generate output
generation_output = model.generate(
    tokens, 
    streamer=streamer,
    max_new_tokens=64,
    eos_token_id=terminators
)
```

Want to support my work? Visit my ko-fi page here: https://ko-fi.com/bartowski
