ryusangwon/qsaf_text 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 was trained using Supervised Fine-Tuning (SFT) with the TRL framework. It is designed for general text generation tasks, leveraging its Llama-3.2 base for conversational and instructional applications within a 32768 token context window.
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Model Overview
ryusangwon/qsaf_text is a 1 billion parameter language model, fine-tuned from the meta-llama/Llama-3.2-1B-Instruct base model. This model was developed by ryusangwon and trained using Supervised Fine-Tuning (SFT) with the Hugging Face TRL (Transformer Reinforcement Learning) library.
Key Capabilities
- Instruction Following: Inherits and enhances instruction-following capabilities from its Llama-3.2-1B-Instruct base.
- Text Generation: Capable of generating coherent and contextually relevant text based on user prompts.
- Context Handling: Supports a substantial context length of 32768 tokens, allowing for more extensive input and output.
Training Details
The model underwent Supervised Fine-Tuning (SFT) using the TRL framework. The specific versions of the libraries used during training include:
- 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 suitable for a variety of text-based applications where a compact yet capable instruction-tuned model is required. Its strengths lie in:
- Conversational AI: Generating responses in dialogue systems.
- Content Creation: Assisting with drafting short-form text, summaries, or creative writing prompts.
- General Language Understanding: Processing and responding to diverse natural language queries.