UX4567/Kartik-Qwen-3B-Instruct
Kartik-Qwen-3B-Instruct is a 3.1 billion parameter instruction-tuned causal language model, fine-tuned from Qwen/Qwen2.5-3B-Instruct. Developed by Kartik, this model leverages a 32768 token context length and is optimized for conversational AI and instruction-following tasks. It was trained using the TRL library, making it suitable for applications requiring robust text generation based on specific prompts.
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Model Overview
Kartik-Qwen-3B-Instruct is a 3.1 billion parameter instruction-tuned language model, building upon the foundational Qwen2.5-3B-Instruct architecture. This model has been specifically fine-tuned using the TRL (Transformers Reinforcement Learning) library, indicating an optimization for instruction-following and conversational capabilities. It supports a substantial context length of 32768 tokens, allowing it to process and generate longer, more coherent responses based on complex prompts.
Key Capabilities
- Instruction Following: Designed to accurately interpret and respond to user instructions.
- Conversational AI: Optimized for generating human-like text in dialogue scenarios.
- Extended Context: Benefits from a 32768 token context window, enabling processing of detailed inputs.
- TRL Fine-tuning: Leverages advanced training techniques for improved performance in interactive applications.
Training Details
The model underwent Supervised Fine-Tuning (SFT) as part of its training procedure. The development utilized several key frameworks:
- PEFT: 0.19.1
- TRL: 1.9.2
- Transformers: 5.13.1
- Pytorch: 2.11.0+cu128
- Datasets: 5.0.1
- Tokenizers: 0.22.2
Good For
- Developing chatbots and virtual assistants.
- Generating creative content based on specific instructions.
- Applications requiring detailed responses from extensive prompts.