gshasiri/SmolLM3-Mid
SmolLM3-Mid by gshasiri is a 1 billion parameter causal language model, fine-tuned from gshasiri/llama3.2-1B-chatml using TRL. This model is designed for general text generation tasks, leveraging its compact size for efficient deployment while maintaining conversational capabilities. It offers a balance of performance and resource efficiency for various natural language processing applications.
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Model Overview
gshasiri/SmolLM3-Mid is a 1 billion parameter language model developed by gshasiri. It is a fine-tuned variant of the gshasiri/llama3.2-1B-chatml base model, specifically trained using the Transformer Reinforcement Learning (TRL) framework. This model is optimized for conversational text generation and general natural language understanding tasks.
Key Capabilities
- Conversational Text Generation: Excels at generating human-like responses to prompts, suitable for chatbots and interactive applications.
- Efficient Deployment: With 1 billion parameters, it offers a compact footprint, making it suitable for environments with limited computational resources.
- Fine-tuned Performance: Benefits from supervised fine-tuning (SFT) using TRL, enhancing its ability to follow instructions and generate coherent text.
Training Details
The model underwent a supervised fine-tuning (SFT) process. The training utilized TRL version 0.25.0, Transformers 4.57.1, PyTorch 2.6.0+cu126, Datasets 4.4.1, and Tokenizers 0.22.1. Further details on the training run can be visualized via Weights & Biases.
Good For
- Chatbot Development: Ideal for creating responsive and context-aware conversational agents.
- Text Summarization: Can be adapted for generating concise summaries of longer texts.
- Content Creation: Useful for generating creative text, answering questions, and assisting with writing tasks where a smaller, efficient model is preferred.