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 Settings

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
```

yuchenglu commited on Aug 18, 2023

Commit

35696b9

1 Parent(s): 4d80166

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@@ -135,7 +135,7 @@ We summarize the results below:
 * Accuracy over MQA
 | Model | 20 docs (Avg 2.9K tokens) | 30 docs (Avg 4.4K tokens) | 50 docs (Avg 7.4K tokens) |
 | -------- | ------- | ------- | ------- |
-| Llama-2-7B-Chat-hf | 0.384 | 0.375 | 0.313 |
 | Longchat-7b-16k | 0.510 | 0.473 | 0.428 |
 | Longchat-7b-v1.5-32k | 0.534 | 0.516 | 0.479 |
 | GPT-3.5-Turbo-16K | 0.622 | 0.609 | 0.577 |

 * Accuracy over MQA
 | Model | 20 docs (Avg 2.9K tokens) | 30 docs (Avg 4.4K tokens) | 50 docs (Avg 7.4K tokens) |
 | -------- | ------- | ------- | ------- |
+| Llama-2-7B-Chat-hf | 0.448 | 0.421 | 0.354 |
 | Longchat-7b-16k | 0.510 | 0.473 | 0.428 |
 | Longchat-7b-v1.5-32k | 0.534 | 0.516 | 0.479 |
 | GPT-3.5-Turbo-16K | 0.622 | 0.609 | 0.577 |