gshasiri/SmolLM3-Mid-Second-Round
gshasiri/SmolLM3-Mid-Second-Round is a 1 billion parameter causal language model developed by gshasiri, fine-tuned from SmolLM3-Mid. This model has been trained using TRL for improved performance and is designed for general text generation tasks with a context length of 32768 tokens.
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Overview
gshasiri/SmolLM3-Mid-Second-Round is a 1 billion parameter language model, representing a fine-tuned iteration of the original gshasiri/SmolLM3-Mid model. It leverages the Transformer Reinforcement Learning (TRL) library for its training process, indicating a focus on enhancing its conversational or instruction-following capabilities through techniques like Supervised Fine-Tuning (SFT).
Key Capabilities
- Text Generation: Capable of generating coherent and contextually relevant text based on given prompts.
- Fine-tuned Performance: Benefits from SFT training using TRL, suggesting improved response quality and adherence to instructions compared to its base model.
- Extended Context: Supports a substantial context length of 32768 tokens, allowing for processing and generating longer sequences of text.
Training Details
The model underwent Supervised Fine-Tuning (SFT) using the TRL framework (version 0.25.1) and Transformers (version 4.57.1). The training process was tracked and can be visualized via Weights & Biases, providing transparency into its development.
Good For
- General Text Generation: Suitable for various applications requiring text completion, question answering, or creative writing.
- Conversational AI: The SFT training implies potential for better performance in dialogue systems and interactive applications.
- Research and Experimentation: Provides a fine-tuned base for further experimentation with smaller language models.