SmolLM2-360M-Grpo-r999
| Property | Value |
|---|---|
| Parameter Count | 360M |
| Training Tokens | 2 trillion |
| Model Type | Language Model (Instruct-tuned) |
| Hugging Face | Link |
What is SmolLM2-360M-Grpo-r999?
SmolLM2-360M-Grpo-r999 is an advanced language model that builds upon the SmolLM2-360M-Instruct foundation. This model represents a significant evolution in compact language models, trained on a diverse 2-trillion token dataset including FineWeb-Edu, DCLM, and The Stack. It's specifically designed to balance performance with efficiency, making it ideal for deployment in resource-constrained environments.
Implementation Details
The model employs supervised fine-tuning (SFT) using a combination of public and specially curated datasets. It's implemented using the Transformers library and can be deployed on both CPU and GPU environments, with special optimization for multi-GPU setups through Accelerate.
- Efficient architecture optimized for 360M parameters
- Comprehensive training on 2 trillion tokens
- Support for both CPU and GPU deployment
- Integration with HuggingFace Transformers ecosystem
Core Capabilities
- Advanced instruction following and reasoning
- Educational content generation and tutoring
- Code assistance and debugging
- Short-form content generation
- Task-based applications
- Edge device deployment
Frequently Asked Questions
Q: What makes this model unique?
The model stands out for its efficient balance of performance and size, making it particularly suitable for edge computing and rapid prototyping while maintaining strong capabilities in instruction following and reasoning tasks. It represents a significant advancement over SmolLM1 while remaining deployable in resource-constrained environments.
Q: What are the recommended use cases?
The model excels in general-purpose conversational AI, educational applications, code assistance, and content generation. It's particularly well-suited for applications requiring quick deployment or running on edge devices where larger models would be impractical.
