Instructions to use NexaAI/Octopus-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use NexaAI/Octopus-v2 with Transformers:

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

pipe = pipeline("text-generation", model="NexaAI/Octopus-v2")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("NexaAI/Octopus-v2")
model = AutoModelForCausalLM.from_pretrained("NexaAI/Octopus-v2")
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]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use NexaAI/Octopus-v2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "NexaAI/Octopus-v2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "NexaAI/Octopus-v2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/NexaAI/Octopus-v2

SGLang

How to use NexaAI/Octopus-v2 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 "NexaAI/Octopus-v2" \
    --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": "NexaAI/Octopus-v2",
		"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 "NexaAI/Octopus-v2" \
        --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": "NexaAI/Octopus-v2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use NexaAI/Octopus-v2 with Docker Model Runner:
```
docker model run hf.co/NexaAI/Octopus-v2
```

Function Scalability?

by Starlento - opened Apr 4, 2024

Discussion

Starlento

Apr 4, 2024

It is really great to see certain work which really focus on improving life efficiency without acting as a chatbot.

It seems you are tokenizing the functions (you think this is very important) and train only on those functions to let model learn how to use these tools by model parameters rather than paste them in prompt. This do save the VRAM since it is working in backend.

But I want to know whether you have some "best practice" to add more functions later?
And why you think making it a special token in the tokenizer (256022) is better than just a short special free text while keep the original tokenizer (256000)?

alexchen4ai

Nexa AI org Apr 10, 2024

Hi, Starlento, thanks for your great questions. If you don't use the special token:

To generate a correct function name like take_photo, you will need two token prediction hit to succeed;
If you still use take_photo, these tokens are also present in other natural language containing "take" or "photo". And model wouldn't be able to differentiate them well.

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