Instructions to use aws-neuron/Llama-2-7b-chat-hf-seqlen-2048-bs-1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use aws-neuron/Llama-2-7b-chat-hf-seqlen-2048-bs-1 with Transformers:

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

pipe = pipeline("text-generation", model="aws-neuron/Llama-2-7b-chat-hf-seqlen-2048-bs-1")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("aws-neuron/Llama-2-7b-chat-hf-seqlen-2048-bs-1")
model = AutoModelForCausalLM.from_pretrained("aws-neuron/Llama-2-7b-chat-hf-seqlen-2048-bs-1")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use aws-neuron/Llama-2-7b-chat-hf-seqlen-2048-bs-1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "aws-neuron/Llama-2-7b-chat-hf-seqlen-2048-bs-1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "aws-neuron/Llama-2-7b-chat-hf-seqlen-2048-bs-1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/aws-neuron/Llama-2-7b-chat-hf-seqlen-2048-bs-1

SGLang

How to use aws-neuron/Llama-2-7b-chat-hf-seqlen-2048-bs-1 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 "aws-neuron/Llama-2-7b-chat-hf-seqlen-2048-bs-1" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "aws-neuron/Llama-2-7b-chat-hf-seqlen-2048-bs-1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "aws-neuron/Llama-2-7b-chat-hf-seqlen-2048-bs-1" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "aws-neuron/Llama-2-7b-chat-hf-seqlen-2048-bs-1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use aws-neuron/Llama-2-7b-chat-hf-seqlen-2048-bs-1 with Docker Model Runner:
```
docker model run hf.co/aws-neuron/Llama-2-7b-chat-hf-seqlen-2048-bs-1
```

Neuronx model for meta-llama/Llama-2-7b-chat-hf

This repository contains are AWS Inferentia2 and neuronx compatible checkpoint for meta-llama/Llama-2-7b-chat-hf. You can find detailed information about the base model on its Model Card.

Usage on Amazon SageMaker

coming soon

Usage with optimum-neuron


from optimum.neuron import pipeline

# Load pipeline from Hugging Face repository
pipe = pipeline("text-generation", "aws-neuron/Llama-2-7b-chat-hf-seqlen-2048-bs-1")

# We use the tokenizer's chat template to format each message - see https://huggingface.co/docs/transformers/main/en/chat_templating
messages = [
    {"role": "user", "content": "What is 2+2?"},
]
prompt = pipe.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
# Run generation
outputs = pipe(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])

Compilation Arguments

compilation arguments

{
  "num_cores": 2,
  "auto_cast_type": "fp16"
}

input_shapes

{
  "sequence_length": 2048,
  "batch_size": 1
}

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