Instructions to use SII-GAIR-NLP/davinci-llm-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use SII-GAIR-NLP/davinci-llm-model with Transformers:

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

pipe = pipeline("text-generation", model="SII-GAIR-NLP/davinci-llm-model", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("SII-GAIR-NLP/davinci-llm-model")
model = AutoModelForCausalLM.from_pretrained("SII-GAIR-NLP/davinci-llm-model", device_map="auto")
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 SII-GAIR-NLP/davinci-llm-model with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/SII-GAIR-NLP/davinci-llm-model

SGLang

How to use SII-GAIR-NLP/davinci-llm-model 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 "SII-GAIR-NLP/davinci-llm-model" \
    --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": "SII-GAIR-NLP/davinci-llm-model",
		"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 "SII-GAIR-NLP/davinci-llm-model" \
        --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": "SII-GAIR-NLP/davinci-llm-model",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use SII-GAIR-NLP/davinci-llm-model with Docker Model Runner:
```
docker model run hf.co/SII-GAIR-NLP/davinci-llm-model
```

Add metadata and link to code

by nielsr HF Staff - opened Apr 2

base: refs/heads/main

←

from: refs/pr/1

Discussion Files changed

+26

-17

Files changed (1) hide show

README.md +26 -17

README.md CHANGED Viewed

@@ -1,9 +1,22 @@
 ## Overview
-daVinci-LLM-3B is a 3B-parameter base language model aimed at making pretraining a transparent and reproducible scientific process. We release not only the final weights but also training trajectories, intermediate checkpoints, data processing decisions, and 200+ ablation studies covering data quality, mixture design, training dynamics, and evaluation validity. The model reaches an overall score of 51.72 across 19 benchmarks, approaching or matching larger 7B-scale models such as OLMo-3 7B.
-![score-daVinci-llm](score-daVinci-llm.png)
 The model follows a two-stage curriculum over ~8T tokens:
 - **Stage 1 (6T tokens):** broad pretraining over diverse web-scale corpora.
 - **Stage 2 (2T tokens):** structured QA and reasoning-heavy data to amplify math and code reasoning.
@@ -43,24 +56,21 @@ Major categories:
 - **Science/Math:** MegaMath, Nemotron-CC-Math, and Darwin-Science series (L3–L5).
 - **QA:** multi-source QA data with rejection sampling (L5).
-## Training Recipe (Summary)
-- **Stage 1:** 6T tokens with progressively adjusted mixtures (shifting weight from web text to code/science).
-- **Stage 2:** 2T tokens with structured QA (30% → 70%) for stronger reasoning and problem-solving.
-## Evaluation (Summary)
-- **Benchmarks:** 19 tasks spanning General, Code, and Science/Math.
-- **Tooling:** lm-eval-harness.
-- **Result:** Overall average 51.72, comparable to OLMo-3 7B.
-## License
-Apache-2.0
 ## Citation
-```
 @misc{qin2026davincillmtowardssciencepretraining,
       title={daVinci-LLM:Towards the Science of Pretraining},
       author={Yiwei Qin and Yixiu Liu and Tiantian Mi and Muhang Xie and Zhen Huang and Weiye Si and Pengrui Lu and Siyuan Feng and Xia Wu and Liming Liu and Ye Luo and Jinlong Hou and Qipeng Guo and Yu Qiao and Pengfei Liu},
@@ -70,5 +80,4 @@ Apache-2.0
       primaryClass={cs.AI},
       url={https://arxiv.org/abs/2603.27164},
 }
-```

+---
+license: apache-2.0
+library_name: transformers
+pipeline_tag: text-generation
+tags:
+- qwen2
+- pretraining-science
+---
 ## Overview
+**daVinci-LLM-3B** is a 3B-parameter base language model presented in [daVinci-LLM: Towards the Science of Pretraining](https://huggingface.co/papers/2603.27164). This project aims to make the pretraining process a transparent and reproducible scientific endeavor.
+We release not only the final weights but also training trajectories, intermediate checkpoints, data processing decisions, and 200+ ablation studies covering data quality, mixture design, training dynamics, and evaluation validity.
+- **GitHub:** [GAIR-NLP/daVinci-LLM](https://github.com/GAIR-NLP/daVinci-LLM)
+- **Paper:** [arXiv:2603.27164](https://arxiv.org/abs/2603.27164)
+- **Dataset:** [davinci-llm-data](https://huggingface.co/datasets/SII-GAIR-NLP/davinci-llm-data)
 The model follows a two-stage curriculum over ~8T tokens:
 - **Stage 1 (6T tokens):** broad pretraining over diverse web-scale corpora.
 - **Stage 2 (2T tokens):** structured QA and reasoning-heavy data to amplify math and code reasoning.
 - **Science/Math:** MegaMath, Nemotron-CC-Math, and Darwin-Science series (L3–L5).
 - **QA:** multi-source QA data with rejection sampling (L5).
+## Evaluation
+The model reaches an overall average score of **51.72** across 19 benchmarks, matching or exceeding the performance of larger 7B-scale models like OLMo-3 7B.
+| Capability Dimension | daVinci-3B | OLMo-3 7B | LLaMA-3.2-3B | Qwen-2.5-3B |
+| -------------------- | ---------: | --------: | -----------: | ----------: |
+| **Overall Performance** | **51.72** | 51.65 | 37.58 | 51.44 |
+| General Knowledge | 52.96 | 55.13 | 51.08 | 55.16 |
+| Code Generation | 55.99 | 54.42 | 32.40 | 56.13 |
+| Scientific Reasoning | 48.30 | 45.98 | 22.45 | 44.65 |
+| MATH | 62.80 | 39.60 | 9.00 | 37.20 |
 ## Citation
+```bibtex
 @misc{qin2026davincillmtowardssciencepretraining,
       title={daVinci-LLM:Towards the Science of Pretraining},
       author={Yiwei Qin and Yixiu Liu and Tiantian Mi and Muhang Xie and Zhen Huang and Weiye Si and Pengrui Lu and Siyuan Feng and Xia Wu and Liming Liu and Ye Luo and Jinlong Hou and Qipeng Guo and Yu Qiao and Pengfei Liu},
       primaryClass={cs.AI},
       url={https://arxiv.org/abs/2603.27164},
 }
+```