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
```

Quantizations for llama.cpp

by rozek - opened Aug 31, 2023

Discussion

rozek

Aug 31, 2023

Thank you very much (again) for this marvellous work! Being able to use long contexts for analyzing texts with LLMs is really important!

In order to use your model with llama.cpp, I've (again) generated some quantizations in GGUF format.

Assuming, that the prompt has the format described in the model card, the Q8_0 quantization performs pretty well - on the other side, the Q4_0 quantization hallucinates far too much.

But, with 24GB of RAM, llama.cpp can now handle contexts up to the recommended limit of 32k when using the Q8_0 quantization - that's really cool!

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