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 Settings

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
```

Zack Zhiyuan Li commited on Apr 30, 2024

Commit

10bd713

1 Parent(s): 3e77051

add benchmark with openELM and Phi-3

Browse files

Files changed (2) hide show

OpenELM-benchmark.jpeg +0 -0
README.md +6 -3

OpenELM-benchmark.jpeg ADDED Viewed

README.md CHANGED Viewed

@@ -32,8 +32,11 @@ Check the Octopus V3 demo video for [Android and iOS](https://octopus3.nexa4ai.c
 </p>
-## Octopus V2
-We are a very small team with many work. Please give us more time to prepare the code, and we will **open source** it. We hope Octopus v2 model will be helpful for you. Let's democratize AI agents for everyone. We've received many requests from car industry, health care, financial system etc. Octopus model is able to be applied to **any function**, and you can start to think about it now.
 <p align="center">
 - <a href="https://www.nexa4ai.com/" target="_blank">Nexa AI Product</a>
 - <a href="https://arxiv.org/abs/2404.01744" target="_blank">ArXiv</a>
@@ -97,7 +100,7 @@ print("latency:", time.time() - start_time," s")
 ## Evaluation
-The benchmark result can be viewed in [this excel](android_benchmark.xlsx), which is manually verified. All the queries in the benchmark test are sampled by Gemini.
 <p align="center" width="100%">
 <a><img src="latency_plot.jpg" alt="ondevice" style="width: 80%; min-width: 300px; display: block; margin: auto; margin-bottom: 20px;"></a>

 </p>
+## Octopus V2 Release
+After open-sourcing our model, we got many requests to compare our model with [Apple's OpenELM](https://huggingface.co/apple/OpenELM-3B-Instruct) and [Microsoft's Phi-3](https://huggingface.co/microsoft/Phi-3-mini-128k-instruct). Please see [Evaluation section](#evaluation). From our benchmark dataset, Microsoft's Phi-3 achieves accuracy of 45.7% and the average inference latency is 10.2s. While Apple's OpenELM fails to generate function call, please see [this screenshot](https://huggingface.co/NexaAIDev/Octopus-v2/blob/main/OpenELM-benchmark.jpeg). Our model, Octopus V2, achieves 99.5% accuracy and the average inference latency is 0.38s.
+We are a very small team with many work. Please give us more time to prepare the code, and we will **open source** it. We hope Octopus v2 model will be helpful for you. Let's democratize AI agents for everyone. We've received many requests from car industry, health care, financial system etc. Octopus model is able to be applied to **any function**, and you can start to think about it now.
 <p align="center">
 - <a href="https://www.nexa4ai.com/" target="_blank">Nexa AI Product</a>
 - <a href="https://arxiv.org/abs/2404.01744" target="_blank">ArXiv</a>
 ## Evaluation
+The benchmark result can be viewed in [this excel](android_benchmark.xlsx), which has been manually verified. Microsoft's Phi-3 model achieved an accuracy of 45.7%, with an average inference latency of 10.2 seconds. Meanwhile, Apple's OpenELM was unable to generate a function call, as shown in [this screenshot](https://huggingface.co/NexaAIDev/Octopus-v2/blob/main/OpenELM-benchmark.jpeg). Additionally, OpenELM's score on the MMLU benchmark is quite low at 26.7, compared to Google's Gemma 2B, which scored 42.3.
 <p align="center" width="100%">
 <a><img src="latency_plot.jpg" alt="ondevice" style="width: 80%; min-width: 300px; display: block; margin: auto; margin-bottom: 20px;"></a>