hathucviet/output
The hathucviet/output model is a 0.5 billion parameter instruction-tuned causal language model, fine-tuned from Qwen/Qwen2.5-0.5B-Instruct. Developed by hathucviet, this model leverages the TRL framework for its training procedure. It is designed for general text generation tasks, offering a compact solution for applications requiring instruction-following capabilities.
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Model Overview
The hathucviet/output model is a compact, instruction-tuned language model with 0.5 billion parameters. It is a fine-tuned variant of the Qwen/Qwen2.5-0.5B-Instruct base model, developed by Qwen. The fine-tuning process was conducted using the TRL (Transformers Reinforcement Learning) library, indicating a focus on enhancing its instruction-following capabilities through supervised fine-tuning (SFT).
Key Capabilities
- Instruction Following: Optimized to generate responses based on explicit instructions provided in prompts.
- Text Generation: Capable of producing coherent and contextually relevant text for various prompts.
- Compact Size: With 0.5 billion parameters, it offers a lightweight solution suitable for environments with limited computational resources.
Training Details
The model underwent a supervised fine-tuning (SFT) process. The training utilized specific versions of key frameworks:
- TRL: 1.10.0
- Transformers: 5.15.0
- Pytorch: 2.13.0
- Datasets: 5.0.1
- Tokenizers: 0.22.2
Use Cases
This model is suitable for applications requiring efficient, instruction-based text generation where a smaller model footprint is advantageous. It can be used for tasks such as:
- Answering questions based on provided context.
- Generating creative text or dialogue following specific prompts.
- Simple conversational agents or chatbots.