mrsanskar19/my_first_model is a 0.5 billion parameter instruction-tuned causal language model, fine-tuned from Qwen/Qwen2.5-0.5B-Instruct. Developed by mrsanskar19, this model leverages a 32768-token context length and was trained using the TRL framework. It is designed for general text generation tasks, building upon the capabilities of its base Qwen2.5 architecture.
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Overview
mrsanskar19/my_first_model is a 0.5 billion parameter instruction-tuned language model, derived from the Qwen/Qwen2.5-0.5B-Instruct architecture. This model was fine-tuned using the TRL (Transformers Reinforcement Learning) framework, indicating a focus on improving its instruction-following capabilities and response quality through supervised fine-tuning (SFT).
Key Capabilities
- Instruction Following: Enhanced through SFT, allowing it to generate responses based on given prompts and instructions.
- Text Generation: Capable of generating coherent and contextually relevant text for various prompts.
- Context Handling: Supports a substantial context length of 32768 tokens, enabling it to process and generate longer sequences of text while maintaining context.
Training Details
The model was trained using Supervised Fine-Tuning (SFT) with the TRL library (version 1.0.0). The development environment included Transformers version 5.0.0, Pytorch 2.10.0+cu128, Datasets 4.8.4, and Tokenizers 0.22.2.
Good For
- Prototyping: Its smaller size (0.5B parameters) makes it suitable for rapid experimentation and development where computational resources might be limited.
- General Text Generation Tasks: Ideal for applications requiring basic instruction-following and text completion, such as chatbots, content generation, or summarization of short texts.