ryusangwon/ko_en_Llama-3.2-1B-Instruct
The ryusangwon/ko_en_Llama-3.2-1B-Instruct model is a 1 billion parameter instruction-tuned causal language model, fine-tuned by ryusangwon from the meta-llama/Llama-3.2-1B-Instruct base model. It was trained using the TRL framework. This model is designed for general text generation tasks, leveraging its instruction-following capabilities.
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Overview
This model, ko_en_Llama-3.2-1B-Instruct, is a 1 billion parameter instruction-tuned language model developed by ryusangwon. It is a fine-tuned variant of the meta-llama/Llama-3.2-1B-Instruct base model, indicating an enhancement for instruction-following tasks. The fine-tuning process utilized the TRL (Transformer Reinforcement Learning) framework, a library often employed for training large language models.
Key Capabilities
- Instruction Following: Designed to respond to user instructions effectively, building upon its base Llama 3.2 architecture.
- Text Generation: Capable of generating coherent and contextually relevant text based on prompts.
Training Details
The model underwent a Supervised Fine-Tuning (SFT) procedure. The training environment included specific versions of key frameworks:
- TRL: 0.13.0
- Transformers: 4.47.0
- Pytorch: 2.5.1
- Datasets: 3.1.0
- Tokenizers: 0.21.0
Good For
- General-purpose text generation where instruction adherence is important.
- Applications requiring a compact, instruction-tuned model based on the Llama 3.2 architecture.