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ParScale/
ParScale-4.7B-P4-Python

Name: ParScale-4.7B-P4-Python
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Parallel Scaling Law for Language Model

Yet Another Scaling Law beyond Parameters and Inference Time Scaling

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
ParScale-1.8B-P2	✨ ParScale $P=2$	🤗 ParScale/ParScale-1.8B-P2
ParScale-1.8B-P4	✨ ParScale $P=4$	🤗 ParScale/ParScale-1.8B-P4
ParScale-1.8B-P8	✨ ParScale $P=8$	🤗 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
ParScale-1.8B-P2-Inst	✨ ParScale $P=2$	🤗 ParScale/ParScale-1.8B-P2-Inst
ParScale-1.8B-P4-Inst	✨ ParScale $P=4$	🤗 ParScale/ParScale-1.8B-P4-Inst
ParScale-1.8B-P8-Inst	✨ ParScale $P=8$	🤗 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
ParScale-Qwen-3B-P4-Python	✨ ParScale $P=4$	🤗 ParScale/ParScale-Qwen-3B-P4-Python
ParScale-Qwen-3B-P8-Python	✨ ParScale $P=8$	🤗 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
ParScale-QwenInit-3B-P2-Python	ParScale $P=2$	🤗 ParScale/ParScale-QwenInit-3B-P2-Python
ParScale-QwenInit-3B-P4-Python	ParScale $P=4$	🤗 ParScale/ParScale-QwenInit-3B-P4-Python
ParScale-QwenInit-3B-P8-Python	ParScale $P=8$	🤗 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
ParScale-QwenInit-3B-P2-Pile	ParScale $P=2$	🤗 ParScale/ParScale-QwenInit-3B-P2-Pile
ParScale-QwenInit-3B-P4-Pile	ParScale $P=4$	🤗 ParScale/ParScale-QwenInit-3B-P4-Pile
ParScale-QwenInit-3B-P8-Pile	ParScale $P=8$	🤗 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}

ParScale/ParScale-4.7B-P4-Python