Instructions to use GAIR/daVinci-Dev-72B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use GAIR/daVinci-Dev-72B with Transformers:

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

pipe = pipeline("text-generation", model="GAIR/daVinci-Dev-72B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("GAIR/daVinci-Dev-72B")
model = AutoModelForMultimodalLM.from_pretrained("GAIR/daVinci-Dev-72B")
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 Settings

vLLM

How to use GAIR/daVinci-Dev-72B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "GAIR/daVinci-Dev-72B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "GAIR/daVinci-Dev-72B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/GAIR/daVinci-Dev-72B

SGLang

How to use GAIR/daVinci-Dev-72B 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 "GAIR/daVinci-Dev-72B" \
    --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": "GAIR/daVinci-Dev-72B",
		"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 "GAIR/daVinci-Dev-72B" \
        --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": "GAIR/daVinci-Dev-72B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use GAIR/daVinci-Dev-72B with Docker Model Runner:
```
docker model run hf.co/GAIR/daVinci-Dev-72B
```

stargazerzj commited on 6 days ago

Commit

a32969d

verified ·

1 Parent(s): 8b8bba7

Upload README.md with huggingface_hub

Browse files

Files changed (1) hide show

README.md +8 -1

README.md CHANGED Viewed

@@ -18,7 +18,8 @@ library_name: transformers
 <div align="center">
-[![Paper](https://img.shields.io/badge/Paper-PDF-1f6feb.svg)](https://github.com/GAIR-NLP/daVinci-Dev/blob/main/daVinci-Dev.pdf)
 [![arXiv](https://img.shields.io/badge/arXiv-2601.18418-b31b1b.svg)](https://arxiv.org/pdf/2601.18418)
 [![GitHub](https://img.shields.io/badge/GitHub-Repository-green)](https://github.com/GAIR-NLP/daVinci-Dev)
 [![Hugging Face](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Dataset-blue)](https://huggingface.co/datasets/GAIR/daVinci-Dev)
@@ -34,6 +35,7 @@ library_name: transformers
 ## Table of Contents
 - [Overview](#overview)
 - [Key Results](#key-results)
 - [Model Zoo](#model-zoo)
@@ -45,6 +47,11 @@ library_name: transformers
 - [License](#license)
 - [Citation](#citation)
 ## Overview
 `daVinci-Dev` is a family of large language models trained for **agentic software engineering**.

 <div align="center">
+<!-- [![Paper](https://img.shields.io/badge/Paper-PDF-1f6feb.svg)](./daVinci-Dev.pdf) -->
+[![ICML 2026](https://img.shields.io/badge/ICML%202026-Oral-8A2BE2.svg)](https://icml.cc/virtual/2026/oral/71032)
 [![arXiv](https://img.shields.io/badge/arXiv-2601.18418-b31b1b.svg)](https://arxiv.org/pdf/2601.18418)
 [![GitHub](https://img.shields.io/badge/GitHub-Repository-green)](https://github.com/GAIR-NLP/daVinci-Dev)
 [![Hugging Face](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Dataset-blue)](https://huggingface.co/datasets/GAIR/daVinci-Dev)
 ## Table of Contents
+- [News](#news)
 - [Overview](#overview)
 - [Key Results](#key-results)
 - [Model Zoo](#model-zoo)
 - [License](#license)
 - [Citation](#citation)
+## News
+- **2026-05:** 🎉 daVinci-Dev was accepted as an **oral presentation** at ICML 2026.
+- **2026-01:** daVinci-Dev paper, code and dataset were released!
 ## Overview
 `daVinci-Dev` is a family of large language models trained for **agentic software engineering**.