Instructions to use TheBloke/Llama-2-7B-Chat-GGML with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use TheBloke/Llama-2-7B-Chat-GGML with Transformers:

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

pipe = pipeline("text-generation", model="TheBloke/Llama-2-7B-Chat-GGML")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("TheBloke/Llama-2-7B-Chat-GGML", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use TheBloke/Llama-2-7B-Chat-GGML with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "TheBloke/Llama-2-7B-Chat-GGML"
# 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-2-7B-Chat-GGML",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/TheBloke/Llama-2-7B-Chat-GGML

SGLang

How to use TheBloke/Llama-2-7B-Chat-GGML 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-2-7B-Chat-GGML" \
    --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-2-7B-Chat-GGML",
		"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-2-7B-Chat-GGML" \
        --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-2-7B-Chat-GGML",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use TheBloke/Llama-2-7B-Chat-GGML with Docker Model Runner:
```
docker model run hf.co/TheBloke/Llama-2-7B-Chat-GGML
```

Benchmark of different GGML version

by aiapprentice101 - opened Jul 18, 2023

Discussion

aiapprentice101

Jul 18, 2023

Hi,

Awesome work! Do you happen to have any benchmark of different versions of the GGML model? It will be great to see how much performance deterioration we get from these quantization techniques.

mike-ravkine

Jul 20, 2023

@aiapprentice101 Did you see https://oobabooga.github.io/blog/posts/perplexities/ ? Its not about this model specifically but covers the relative performance of different GGML and GPTQ quants.

aiapprentice101

Jul 23, 2023

Thank you @mike-ravkine . The post seems to claim GPTQ is the best in terms of quality. However, when I test out the GPTQ version of Llama-2 (also from TheBloke), I get very bad performance. The model doesn't capture the one-shot instructions, and generates random stuffs.

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