Instructions to use xingyaoww/CodeActAgent-Mistral-7b-v0.1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use xingyaoww/CodeActAgent-Mistral-7b-v0.1 with Transformers:

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

pipe = pipeline("text-generation", model="xingyaoww/CodeActAgent-Mistral-7b-v0.1")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("xingyaoww/CodeActAgent-Mistral-7b-v0.1")
model = AutoModelForMultimodalLM.from_pretrained("xingyaoww/CodeActAgent-Mistral-7b-v0.1")
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]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use xingyaoww/CodeActAgent-Mistral-7b-v0.1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "xingyaoww/CodeActAgent-Mistral-7b-v0.1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "xingyaoww/CodeActAgent-Mistral-7b-v0.1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/xingyaoww/CodeActAgent-Mistral-7b-v0.1

SGLang

How to use xingyaoww/CodeActAgent-Mistral-7b-v0.1 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 "xingyaoww/CodeActAgent-Mistral-7b-v0.1" \
    --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": "xingyaoww/CodeActAgent-Mistral-7b-v0.1",
		"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 "xingyaoww/CodeActAgent-Mistral-7b-v0.1" \
        --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": "xingyaoww/CodeActAgent-Mistral-7b-v0.1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use xingyaoww/CodeActAgent-Mistral-7b-v0.1 with Docker Model Runner:
```
docker model run hf.co/xingyaoww/CodeActAgent-Mistral-7b-v0.1
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Executable Code Actions Elicit Better LLM Agents

💻 Code • 📃 Paper • 🤗 Data (CodeActInstruct) • 🤗 Model (CodeActAgent-Mistral-7b-v0.1) • 🤖 Chat with CodeActAgent!

We propose to use executable Python code to consolidate LLM agents’ actions into a unified action space (CodeAct). Integrated with a Python interpreter, CodeAct can execute code actions and dynamically revise prior actions or emit new actions upon new observations (e.g., code execution results) through multi-turn interactions.

Why CodeAct?

Our extensive analysis of 17 LLMs on API-Bank and a newly curated benchmark M³ToolEval shows that CodeAct outperforms widely used alternatives like Text and JSON (up to 20% higher success rate). Please check our paper for more detailed analysis!

Comparison between CodeAct and Text / JSON as action.

Quantitative results comparing CodeAct and {Text, JSON} on M³ToolEval.

📁 CodeActInstruct

We collect an instruction-tuning dataset CodeActInstruct that consists of 7k multi-turn interactions using CodeAct. Dataset is release at huggingface dataset 🤗. Please refer to the paper and this section for details of data collection.

Dataset Statistics. Token statistics are computed using Llama-2 tokenizer.

🪄 CodeActAgent

Trained on CodeActInstruct and general conversaions, CodeActAgent excels at out-of-domain agent tasks compared to open-source models of the same size, while not sacrificing generic performance (e.g., knowledge, dialog). We release two variants of CodeActAgent:

CodeActAgent-Mistral-7b-v0.1 (recommended, model link): using Mistral-7b-v0.1 as the base model with 32k context window.
CodeActAgent-Llama-7b (model link): using Llama-2-7b as the base model with 4k context window.

Evaluation results for CodeActAgent. ID and OD stand for in-domain and out-of-domain evaluation correspondingly. Overall averaged performance normalizes the MT-Bench score to be consistent with other tasks and excludes in-domain tasks for fair comparison.

Please check out our paper and code for more details about data collection, model training, and evaluation.

📚 Citation

@misc{wang2024executable,
      title={Executable Code Actions Elicit Better LLM Agents}, 
      author={Xingyao Wang and Yangyi Chen and Lifan Yuan and Yizhe Zhang and Yunzhu Li and Hao Peng and Heng Ji},
      year={2024},
      eprint={2402.01030},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

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Paper for xingyaoww/CodeActAgent-Mistral-7b-v0.1

Executable Code Actions Elicit Better LLM Agents

Paper • 2402.01030 • Published Feb 1, 2024 • 195