Instructions to use ParScale/ParScale-Qwen-3B-P4-Python with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ParScale/ParScale-Qwen-3B-P4-Python with Transformers:

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

pipe = pipeline("text-generation", model="ParScale/ParScale-Qwen-3B-P4-Python", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("ParScale/ParScale-Qwen-3B-P4-Python", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use ParScale/ParScale-Qwen-3B-P4-Python with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ParScale/ParScale-Qwen-3B-P4-Python"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ParScale/ParScale-Qwen-3B-P4-Python",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ParScale/ParScale-Qwen-3B-P4-Python

SGLang

How to use ParScale/ParScale-Qwen-3B-P4-Python 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 "ParScale/ParScale-Qwen-3B-P4-Python" \
    --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": "ParScale/ParScale-Qwen-3B-P4-Python",
		"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 "ParScale/ParScale-Qwen-3B-P4-Python" \
        --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": "ParScale/ParScale-Qwen-3B-P4-Python",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use ParScale/ParScale-Qwen-3B-P4-Python with Docker Model Runner:
```
docker model run hf.co/ParScale/ParScale-Qwen-3B-P4-Python
```

ParScale-Qwen-3B-P4-Python / README.md

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	---
	datasets:
	- bigcode/the-stack-v2
	license: apache-2.0
	base_model:
	- Qwen/Qwen2.5-3B
	library_name: transformers
	---
	<div align="center">


	# Parallel Scaling Law for Language Model


	_Yet Another Scaling Law beyond Parameters and Inference Time Scaling_

	[![Paper](https://img.shields.io/badge/arXiv-2505.10475-red)](https://arxiv.org/abs/2505.10475)
	[![huggingface](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Models-FFD21E)](https://huggingface.co/ParScale)
	[![GitHub](https://img.shields.io/github/stars/QwenLM/ParScale)](https://github.com/QwenLM/ParScale/)

	</div>

	## Checkpoints

	> [!IMPORTANT]
	> All the released checkpoints were trained on public datasets and are for academic use only.

	✨ are our recommendation for strong models.

	### Base models for scaling training data to 1T tokens

	These models demonstrate strong competitiveness among existing small models, including SmolLM, gemma, and Llama-3.2 (see Table 4 for details).

	\|Model\|Description\|Download\|
	\|:-:\|:-:\|:-:\|
	\|ParScale-1.8B-P1\|✨ Baseline $P=1$\|[🤗 ParScale/ParScale-1.8B-P1](https://huggingface.co/ParScale/ParScale-1.8B-P1)\|
	\|ParScale-1.8B-P2\|✨ ParScale $P=2$\|[🤗 ParScale/ParScale-1.8B-P2](https://huggingface.co/ParScale/ParScale-1.8B-P2)\|
	\|ParScale-1.8B-P4\|✨ ParScale $P=4$\|[🤗 ParScale/ParScale-1.8B-P4](https://huggingface.co/ParScale/ParScale-1.8B-P4)\|
	\|ParScale-1.8B-P8\|✨ ParScale $P=8$\|[🤗 ParScale/ParScale-1.8B-P8](https://huggingface.co/ParScale/ParScale-1.8B-P8)\|

	### Instruct models for scaling training data to 1T tokens

	We post-trained the aforementioned base model on SmolTalk-1M to enable conversational capabilities.

	\|Model\|Description\|Download\|
	\|:-:\|:-:\|:-:\|
	\|ParScale-1.8B-P1-Inst\|✨ Baseline $P=1$\|[🤗 ParScale/ParScale-1.8B-P1-Inst](https://huggingface.co/ParScale/ParScale-1.8B-P1-Inst)\|
	\|ParScale-1.8B-P2-Inst\|✨ ParScale $P=2$\|[🤗 ParScale/ParScale-1.8B-P2-Inst](https://huggingface.co/ParScale/ParScale-1.8B-P2-Inst)\|
	\|ParScale-1.8B-P4-Inst\|✨ ParScale $P=4$\|[🤗 ParScale/ParScale-1.8B-P4-Inst](https://huggingface.co/ParScale/ParScale-1.8B-P4-Inst)\|
	\|ParScale-1.8B-P8-Inst\|✨ ParScale $P=8$\|[🤗 ParScale/ParScale-1.8B-P8-Inst](https://huggingface.co/ParScale/ParScale-1.8B-P8-Inst)\|


	### Continual Pretraining Qwen-2.5-3B

	We froze the parameters of Qwen-2.5-3B and only fine-tuned the newly introduced parameters on Stack-V2-Python. Since the following models share the same backbone parameters as Qwen-2.5-3B, they have the potential for dynamic parscale: switching P to adapt model capabilities during inference.

	\|Model\|Description\|Download\|
	\|:-:\|:-:\|:-:\|
	\|ParScale-Qwen-3B-P2-Python\|✨ ParScale $P=2$\|[🤗 ParScale/ParScale-Qwen-3B-P2-Python](https://huggingface.co/ParScale/ParScale-Qwen-3B-P2-Python)\|
	\|ParScale-Qwen-3B-P4-Python\|✨ ParScale $P=4$\|[🤗 ParScale/ParScale-Qwen-3B-P4-Python](https://huggingface.co/ParScale/ParScale-Qwen-3B-P4-Python)\|
	\|ParScale-Qwen-3B-P8-Python\|✨ ParScale $P=8$\|[🤗 ParScale/ParScale-Qwen-3B-P8-Python](https://huggingface.co/ParScale/ParScale-Qwen-3B-P8-Python)\|

	- For full pretraining on Stack-V2-Python

	\|Model\|Description\|Download\|
	\|:-:\|:-:\|:-:\|
	\|ParScale-QwenInit-3B-P1-Python\|Baseline $P=1$\|[🤗 ParScale/ParScale-QwenInit-3B-P1-Python](https://huggingface.co/ParScale/ParScale-QwenInit-3B-P1-Python)\|
	\|ParScale-QwenInit-3B-P2-Python\|ParScale $P=2$\|[🤗 ParScale/ParScale-QwenInit-3B-P2-Python](https://huggingface.co/ParScale/ParScale-QwenInit-3B-P2-Python)\|
	\|ParScale-QwenInit-3B-P4-Python\|ParScale $P=4$\|[🤗 ParScale/ParScale-QwenInit-3B-P4-Python](https://huggingface.co/ParScale/ParScale-QwenInit-3B-P4-Python)\|
	\|ParScale-QwenInit-3B-P8-Python\|ParScale $P=8$\|[🤗 ParScale/ParScale-QwenInit-3B-P8-Python](https://huggingface.co/ParScale/ParScale-QwenInit-3B-P8-Python)\|

	- For full pretraining on Pile

	\|Model\|Description\|Download\|
	\|:-:\|:-:\|:-:\|
	\|ParScale-QwenInit-3B-P1-Pile\|Baseline $P=1$\|[🤗 ParScale/ParScale-QwenInit-3B-P1-Pile](https://huggingface.co/ParScale/ParScale-QwenInit-3B-P1-Pile)\|
	\|ParScale-QwenInit-3B-P2-Pile\|ParScale $P=2$\|[🤗 ParScale/ParScale-QwenInit-3B-P2-Pile](https://huggingface.co/ParScale/ParScale-QwenInit-3B-P2-Pile)\|
	\|ParScale-QwenInit-3B-P4-Pile\|ParScale $P=4$\|[🤗 ParScale/ParScale-QwenInit-3B-P4-Pile](https://huggingface.co/ParScale/ParScale-QwenInit-3B-P4-Pile)\|
	\|ParScale-QwenInit-3B-P8-Pile\|ParScale $P=8$\|[🤗 ParScale/ParScale-QwenInit-3B-P8-Pile](https://huggingface.co/ParScale/ParScale-QwenInit-3B-P8-Pile)\|


	### Checkpoints Used to Fit the Scaling Law

	Download link: https://huggingface.co/ParScale/ParScale-{size}-{P}-{dataset}

	- {size}: model size, from {0.7B, 0.9B, 1.3B, 1.8B, 3B, 4.7B}
	- {P}: number of parallels, from {P1, P2, P4, P8}
	- {dataset}: training dataset, from {Python, Pile}