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

Libraries

How to use OxxoCodes/Meta-Llama-3-8B-Instruct-GPTQ with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("OxxoCodes/Meta-Llama-3-8B-Instruct-GPTQ")
model = AutoModelForCausalLM.from_pretrained("OxxoCodes/Meta-Llama-3-8B-Instruct-GPTQ")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use OxxoCodes/Meta-Llama-3-8B-Instruct-GPTQ with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/OxxoCodes/Meta-Llama-3-8B-Instruct-GPTQ

SGLang

How to use OxxoCodes/Meta-Llama-3-8B-Instruct-GPTQ 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 "OxxoCodes/Meta-Llama-3-8B-Instruct-GPTQ" \
    --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": "OxxoCodes/Meta-Llama-3-8B-Instruct-GPTQ",
		"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 "OxxoCodes/Meta-Llama-3-8B-Instruct-GPTQ" \
        --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": "OxxoCodes/Meta-Llama-3-8B-Instruct-GPTQ",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use OxxoCodes/Meta-Llama-3-8B-Instruct-GPTQ with Docker Model Runner:
```
docker model run hf.co/OxxoCodes/Meta-Llama-3-8B-Instruct-GPTQ
```

OxxoCodes/Meta-Llama-3-8B-Instruct-GPTQ

Built with Meta Llama 3

Model Description

This is a 4-bit GPTQ quantized version of meta-llama/Meta-Llama-3-8B-Instruct.

This model was quantized using the following quantization config:

quantize_config = BaseQuantizeConfig(
    bits=4,
    group_size=128,
    desc_act=False,
    damp_percent=0.1,
)

To use this model, you need to install AutoGPTQ. For detailed installation instructions, please refer to the AutoGPTQ GitHub repository.

Example Usage

from auto_gptq import AutoGPTQForCausalLM

tokenizer = AutoTokenizer.from_pretrained("meta-llama/Meta-Llama-3-8B-Instruct")
model = AutoGPTQForCausalLM.from_quantized("OxxoCodes/Meta-Llama-3-8B-Instruct-GPTQ")

output = model.generate(**tokenizer("The capitol of France is", return_tensors="pt").to(model.device))[0]
print(tokenizer.decode(output))

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