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Llama-3-8B-GPTQ-4-Bit

Text Generation
Transformers
Safetensors
llama
llama-3
facebook
meta
astronomer
gptq
pretrained
quantized
finetuned
Inference Endpoints
text-generation-inference
4-bit precision
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Llama-3-8B-GPTQ-4-Bit / README.md
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metadata
base_model: meta-llama/Meta-Llama-3-8B
inference: false
model_creator: astronomer-io
model_name: Meta-Llama-3-8B
model_type: llama
pipeline_tag: text-generation
quantized_by: davidxmle
license: other
license_name: llama-3
license_link: https://huggingface.co/meta-llama/Meta-Llama-3-8B/blob/main/LICENSE
tags:
  - llama
  - llama-3
  - facebook
  - meta
  - astronomer
  - gptq
  - pretrained
  - quantized
  - finetuned
  - autotrain_compatible
  - endpoints_compatible
datasets:
  - wikitext
Astronomer

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-GPTQ-8-Bit

MUST READ: Very Important!! Note About Untrained Special Tokens in Llama 3 Base (Non-instruct) Models & Fine-tuning Llama 3 Base

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.
  • 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 4 Bit quantized GPTQ model files for meta-llama/Meta-Llama-3-8B.

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.

The untrained-special-tokens-fixed branch is the same model as the main branch but has special tokens and tokens untrained (by finding the tokens where max embedding value of each token in input_embeddings and output_embeddings is 0) and setting them to the average of all trained tokens for each feature. Using this branch is recommended if you plan to do any fine-tuning with your own tokens added or with instruction following.

GPTQ Quantization Method

  • This model is quantized by utilizing the AutoGPTQ library, following best practices noted by GPTQ paper
  • 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 Special Tokens Fixed Description
main 4 128 Yes 0.1 wikitext 8192 5.74 GB Yes No 4-bit, with Act Order and group size 128g. Smallest model possible with small accuracy loss
untrained-special-tokens-fixed 4 128 Yes 0.1 wikitext 8192 5.74 GB Yes Yes Same as the main branch. The special tokens that were untrained causing exploding gradients/NaN gradients have had their embedding values set to the average of trained tokens for each feature
More variants to come TBD 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-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

Contributors