ryusangwon/qsaf_best
ryusangwon/qsaf_best is a 1 billion parameter instruction-tuned causal language model, fine-tuned from meta-llama/Llama-3.2-1B-Instruct. Developed by ryusangwon, this model leverages a 32768 token context length and was trained using the TRL framework. It is designed for general text generation tasks, particularly those requiring instruction following.
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Model Overview
ryusangwon/qsaf_best is a 1 billion parameter language model, fine-tuned from the meta-llama/Llama-3.2-1B-Instruct base model. It was developed by ryusangwon and trained using the TRL (Transformer Reinforcement Learning) library, specifically employing a Supervised Fine-Tuning (SFT) procedure.
Key Capabilities
- Instruction Following: The model is instruction-tuned, making it suitable for tasks where a clear prompt or question is provided.
- Text Generation: Capable of generating coherent and contextually relevant text based on input prompts.
- Llama 3.2 Architecture: Benefits from the underlying architecture of the Llama 3.2 series, providing a robust foundation for language understanding and generation.
Training Details
The model underwent Supervised Fine-Tuning (SFT) using the TRL framework. The training environment utilized specific versions of key libraries:
- TRL: 0.12.1
- Transformers: 4.46.3
- Pytorch: 2.5.1
- Datasets: 3.1.0
- Tokenizers: 0.20.4
Use Cases
This model is well-suited for applications requiring a compact, instruction-following language model, such as:
- Answering questions based on provided instructions.
- Generating creative text or responses in conversational agents.
- Prototyping and experimentation with smaller, fine-tuned models.