---
base_model: meta-llama/Meta-Llama-3-8B-Instruct
inference: false
model_creator: astronomer-io
model_name: Meta-Llama-3-8B-Instruct
model_type: llama
pipeline_tag: text-generation
prompt_template: "{% set loop_messages = messages %}{% for message in loop_messages %}{% set content = '<|start_header_id|>' + message['role'] + '<|end_header_id|>\n\n'+ message['content'] | trim + '<|eot_id|>' %}{% if loop.index0 == 0 %}{% set content = bos_token + content %}{% endif %}{{ content }}{% endfor %}{% if add_generation_prompt %}{{ '<|start_header_id|>assistant<|end_header_id|>\n\n' }}{% endif %}"
quantized_by: davidxmle
license: other
license_name: llama-3-community-license
license_link: https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct/blob/main/LICENSE
tags:
- llama
- llama-3
- facebook
- meta
- astronomer
- gptq
- pretrained
- quantized
- finetuned
- autotrain_compatible
- endpoints_compatible
datasets:
- wikitext
---

# Llama-3-8B-Instruct-GPTQ-4-Bit
- Original Model creator: [Meta Llama from Meta](https://huggingface.co/meta-llama)
- Original model: [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct)
- Built with Meta Llama 3
- Quantized by [Astronomer](https://astronomer.io)

# Important Note About Serving with vLLM & oobabooga/text-generation-webui
- For loading this model onto vLLM, make sure all requests have `"stop_token_ids":[128001, 128009]` to temporarily address the non-stop generation issue.
   - vLLM does not yet respect `generation_config.json`.
   - vLLM team is working on a a fix for this https://github.com/vllm-project/vllm/issues/4180
- For oobabooga/text-generation-webui
  - Load the model via AutoGPTQ, with `no_inject_fused_attention` enabled. This is a bug with AutoGPTQ library.
  - Under `Parameters` -> `Generation` -> `Skip special tokens`: turn this off (deselect)
  - Under `Parameters` -> `Generation` -> `Custom stopping strings`: add `"<|end_of_text|>","<|eot_id|>"` to the field

<!-- description start -->
## Description

This repo contains 4 Bit quantized GPTQ model files for [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct).

This model can be loaded with less than 6 GB of VRAM (huge reduction from the original 16.07GB model) and can be served lightning fast with the cheapest Nvidia GPUs possible (Nvidia T4, Nvidia K80, RTX 4070, etc).

The 4 bit GPTQ quant has small quality degradation from the original `bfloat16` model but can be served on much smaller GPUs with maximum improvement in latency and throughput.

<!-- description end -->

## GPTQ Quantization Method
- This model is quantized by utilizing the AutoGPTQ library, following best practices noted by [GPTQ paper](https://arxiv.org/abs/2210.17323)
- Quantization is calibrated and aligned with random samples from the specified dataset (wikitext for now) for minimum accuracy loss.

| Branch | Bits | Group Size | Act Order | Damp % | GPTQ Dataset | Sequence Length | VRAM Size | ExLlama | Description |
| ------ | ---- | -- | --------- | ------ | ------------ | ------- | ---- | ------- | ---- |
| [main](https://huggingface.co/astronomer-io/Llama-3-8B-Instruct-GPTQ-4-Bit/tree/main) | 4 | 128 | Yes | 0.1 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 8192 | 5.74 GB | Yes | 4-bit, with Act Order and group size 128g. Smallest model possible with small accuracy loss | 
| More variants to come | TBD | TBD | TBD | TBD | TBD | TBD | TBD | TBD | May upload additional variants of GPTQ 4 bit models in the future using different parameters such as 128g group size and etc. |

## Serving this GPTQ model using vLLM
Tested serving this model via vLLM using an Nvidia T4 (16GB VRAM).

Tested with the below command
```
python -m vllm.entrypoints.openai.api_server --model astronomer-io/Llama-3-8B-Instruct-GPTQ-4-Bit --max-model-len 8192 --dtype float16
```
For the non-stop token generation bug, make sure to send requests with `stop_token_ids":[128001, 128009]` to vLLM endpoint
Example:
```json
{
    "model": "astronomer-io/Llama-3-8B-Instruct-GPTQ-4-Bit",
    "messages": [
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "Who created Llama 3?"}
        ],
    "max_tokens": 2000,
    "stop_token_ids":[128001,128009]
}
```
### Prompt Template
```
<|begin_of_text|><|start_header_id|>user<|end_header_id|>
{{prompt}}<|eot_id|>
<|start_header_id|>assistant<|end_header_id|>
```
### Contributors
- Quantized by [David Xue, Machine Learning Engineer from Astronomer](https://www.linkedin.com/in/david-xue-uva/)