Instructions to use togethercomputer/Llama-2-7B-32K-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use togethercomputer/Llama-2-7B-32K-Instruct with Transformers:

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

pipe = pipeline("text-generation", model="togethercomputer/Llama-2-7B-32K-Instruct")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("togethercomputer/Llama-2-7B-32K-Instruct")
model = AutoModelForCausalLM.from_pretrained("togethercomputer/Llama-2-7B-32K-Instruct")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use togethercomputer/Llama-2-7B-32K-Instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "togethercomputer/Llama-2-7B-32K-Instruct"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "togethercomputer/Llama-2-7B-32K-Instruct",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/togethercomputer/Llama-2-7B-32K-Instruct

SGLang

How to use togethercomputer/Llama-2-7B-32K-Instruct 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 "togethercomputer/Llama-2-7B-32K-Instruct" \
    --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": "togethercomputer/Llama-2-7B-32K-Instruct",
		"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 "togethercomputer/Llama-2-7B-32K-Instruct" \
        --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": "togethercomputer/Llama-2-7B-32K-Instruct",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use togethercomputer/Llama-2-7B-32K-Instruct with Docker Model Runner:
```
docker model run hf.co/togethercomputer/Llama-2-7B-32K-Instruct
```

can not install rotary

by CUIGuy - opened Aug 22, 2023

Discussion

CUIGuy

Aug 22, 2023

pip install git+https://github.com/HazyResearch/flash-attention.git#subdirectory=csrc/rotary
says command not found"
Defaulting to user installation because normal site-packages is not writeable
Collecting git+https://github.com/HazyResearch/flash-attention.git#subdirectory=csrc/rotary
Cloning https://github.com/HazyResearch/flash-attention.git to /tmp/pip-req-build-0kyevof9
Running command git clone --filter=blob:none --quiet https://github.com/HazyResearch/flash-attention.git /tmp/pip-req-build-0kyevof9
Resolved https://github.com/HazyResearch/flash-attention.git to commit a8c35b4f576d33fd54be973ac02e725700fba42b
Running command git submodule update --init --recursive -q
Preparing metadata (setup.py) ... done
: command not found

CUIGuy

Aug 22, 2023

found fix, after I changed cuda from 12.1 to 11.8, I need to re install everything.

CUIGuy changed discussion status to closed Aug 22, 2023

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