Instructions to use TheBloke/Llama-2-70B-Chat-GPTQ with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use TheBloke/Llama-2-70B-Chat-GPTQ with Transformers:

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

pipe = pipeline("text-generation", model="TheBloke/Llama-2-70B-Chat-GPTQ")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("TheBloke/Llama-2-70B-Chat-GPTQ")
model = AutoModelForCausalLM.from_pretrained("TheBloke/Llama-2-70B-Chat-GPTQ", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use TheBloke/Llama-2-70B-Chat-GPTQ with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "TheBloke/Llama-2-70B-Chat-GPTQ"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "TheBloke/Llama-2-70B-Chat-GPTQ",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/TheBloke/Llama-2-70B-Chat-GPTQ

SGLang

How to use TheBloke/Llama-2-70B-Chat-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 "TheBloke/Llama-2-70B-Chat-GPTQ" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "TheBloke/Llama-2-70B-Chat-GPTQ",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "TheBloke/Llama-2-70B-Chat-GPTQ" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "TheBloke/Llama-2-70B-Chat-GPTQ",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use TheBloke/Llama-2-70B-Chat-GPTQ with Docker Model Runner:
```
docker model run hf.co/TheBloke/Llama-2-70B-Chat-GPTQ
```

max_position_embeddings = 2048?

#29

by zzzac - opened Jul 31, 2023

Discussion

zzzac

Jul 31, 2023

I saw from the config that max_position_embeddings is set to 2048, but the original llama2 model has 4096 maximum input length. Is there a particular reason to reduce the input length of these quantized model?

Thanks for this great work!

TheBloke

Owner Jul 31, 2023

No sorry that's just a mistake. Or rather, the original Llama 2 config.json's had that set to 2048 so that's what mine were set to. Then they updated theirs to 4096.

I did update mine too, but I see now I only did that for the main branch config.json, not the additional branch alternative GPTQs. I'll fix that now.

To be honest it doesn't matter for most clients, which set the length independently. The max_position_embeddings is more a default, not a maximum. But anyway, I'll fix it.

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