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

Libraries

How to use gorilla-llm/gorilla-openfunctions-v2 with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("gorilla-llm/gorilla-openfunctions-v2")
model = AutoModelForCausalLM.from_pretrained("gorilla-llm/gorilla-openfunctions-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]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use gorilla-llm/gorilla-openfunctions-v2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "gorilla-llm/gorilla-openfunctions-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": "gorilla-llm/gorilla-openfunctions-v2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/gorilla-llm/gorilla-openfunctions-v2

SGLang

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

Docker Model Runner
How to use gorilla-llm/gorilla-openfunctions-v2 with Docker Model Runner:
```
docker model run hf.co/gorilla-llm/gorilla-openfunctions-v2
```

cannot report error when missing mandatory params

#14

by tiger55cn - opened May 9, 2024

Discussion

tiger55cn

May 9, 2024

Sometimes it just fills a random value which is not expected behavior.
Is there recommendation to overcome it?

CharlieJi

Gorilla LLM (UC Berkeley) org May 9, 2024

•

edited May 9, 2024

Hi @tiger55cn ! Thanks for flagging the issue. This behavior isn't ideal. It would be helpful if you could link some examples in the discussion so we can understand the behavior more concretely!

During our fine-tuning, we set out to perform data augmentation on function relevance detection, as listed below. The missing required parameters prompts you are referring to is a subset of all scenarios of the function relevance detection category. We would love to know the specific use case you're carrying out, so that openfunctions can improve model robustness to those use cases. Thanks!

https://gorilla.cs.berkeley.edu/blogs/7_open_functions_v2.html#data_composition

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