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

Instructions to use davidxmle/Llama-3-8B-Instruct-GPTQ-4-Bit-Debug with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use davidxmle/Llama-3-8B-Instruct-GPTQ-4-Bit-Debug with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="davidxmle/Llama-3-8B-Instruct-GPTQ-4-Bit-Debug")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("davidxmle/Llama-3-8B-Instruct-GPTQ-4-Bit-Debug")
model = AutoModelForCausalLM.from_pretrained("davidxmle/Llama-3-8B-Instruct-GPTQ-4-Bit-Debug", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use davidxmle/Llama-3-8B-Instruct-GPTQ-4-Bit-Debug with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "davidxmle/Llama-3-8B-Instruct-GPTQ-4-Bit-Debug"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "davidxmle/Llama-3-8B-Instruct-GPTQ-4-Bit-Debug",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/davidxmle/Llama-3-8B-Instruct-GPTQ-4-Bit-Debug

SGLang

How to use davidxmle/Llama-3-8B-Instruct-GPTQ-4-Bit-Debug with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "davidxmle/Llama-3-8B-Instruct-GPTQ-4-Bit-Debug" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "davidxmle/Llama-3-8B-Instruct-GPTQ-4-Bit-Debug",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "davidxmle/Llama-3-8B-Instruct-GPTQ-4-Bit-Debug" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "davidxmle/Llama-3-8B-Instruct-GPTQ-4-Bit-Debug",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use davidxmle/Llama-3-8B-Instruct-GPTQ-4-Bit-Debug with Docker Model Runner:
```
docker model run hf.co/davidxmle/Llama-3-8B-Instruct-GPTQ-4-Bit-Debug
```

Llama-3-8B-Instruct-GPTQ-4-Bit-Debug / README.md

davidxmle

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	---
	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/)