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README.md

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 ---
+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
 ---
+<!-- header start -->
+<!-- 200823 -->
+<div style="width: auto; margin-left: auto; margin-right: auto">
+<img src="https://www.astronomer.io/logo/astronomer-logo-RGB-standard-1200px.png" alt="Astronomer" style="width: 60%; min-width: 400px; display: block; margin: auto;">
+</div>
+<div style="margin-top: 1.0em; margin-bottom: 1.0em;"></div>
+
+<div style="text-align:center; margin-top: 0em; margin-bottom: 0em"><p style="margin-top: 0.25em; margin-bottom: 0em;">This model is generously created and made open source by <a href="https://astronomer.io">Astronomer</a>.</p></div>
+<div style="text-align:center; margin-top: 0em; margin-bottom: 0em"><p style="margin-top: 0.25em; margin-bottom: 0em;">Astronomer is the de facto company for <a href="https://airflow.apache.org/">Apache Airflow</a>, the most trusted open-source framework for data orchestration and MLOps.</p></div>
+<hr style="margin-top: 1.0em; margin-bottom: 1.0em;">
+<!-- header end -->
+
+# Llama-3-8B-GPTQ-8-Bit
+- Original Model creator: [Meta Llama from Meta](https://huggingface.co/meta-llama)
+- Original model: [meta-llama/Meta-Llama-3-8B](https://huggingface.co/meta-llama/Meta-Llama-3-8B)
+- Built with Meta Llama 3
+- Quantized by [David Xue](https://www.linkedin.com/in/david-xue-uva/) from [Astronomer](https://astronomer.io)
+
+## MUST READ: Very Important!! Note About Untrained Special Tokens in Llama 3 Base (Non-instruct) Models & Fine-tuning Llama 3 Base
+- Special tokens such as the ones used for instruct are undertrained in Llama 3 base models.
+- Credits: discovered by Daniel Han https://twitter.com/danielhanchen/status/1781395882925343058
+- ![image/png](https://cdn-uploads.huggingface.co/production/uploads/655ad0f8727df37c77a09cb9/1U2rRrx60p1pNeeAZw8Rd.png)
+- A patch function is under way, fine-tuning this model for instruction following may cause `NaN` graidents unless this problem is addressed.
+
+## 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](https://huggingface.co/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.
+
+<!-- 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-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
+```bash
+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
+- Quantized by [David Xue, Machine Learning Engineer from Astronomer](https://www.linkedin.com/in/david-xue-uva/)