---
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
---
This model is generously created and made open source by Astronomer.
Astronomer is the de facto company for Apache Airflow, the most trusted open-source framework for data orchestration and MLOps.
# Llama-3-8B-Instruct-GPTQ-8-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
This repo contains 8 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 just over 10GB of VRAM (compared to 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 8 bit GPTQ quant has minimum quality degradation from the original `bfloat16` model due to its higher bitrate.
## 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-8-Bit/tree/main) | 8 | 32 | Yes | 0.1 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 8192 | 9.09 GB | No | 8-bit, with Act Order and group size 32g. Minimum accuracy loss with decent VRAM usage reduction. |
| More variants to come | TBD | TBD | TBD | TBD | TBD | TBD | TBD | TBD | May upload additional variants of GPTQ 8 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-8-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-8-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/)
