souradeepmukhopadhyay99/qwen3-4b-apigenmt-5k-trl-fullft
The souradeepmukhopadhyay99/qwen3-4b-apigenmt-5k-trl-fullft model is a 4 billion parameter causal language model, fine-tuned from Qwen/Qwen3-4B-Instruct-2507. It was trained using the TRL framework with Supervised Fine-Tuning (SFT) methods. This model is designed for general text generation tasks, leveraging its Qwen3 architecture and fine-tuning for improved instruction following. It offers a context length of 40960 tokens, making it suitable for applications requiring extensive input processing.
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Model Overview
The souradeepmukhopadhyay99/qwen3-4b-apigenmt-5k-trl-fullft is a 4 billion parameter language model, derived from the Qwen/Qwen3-4B-Instruct-2507 base model. It has undergone further fine-tuning using the TRL (Transformer Reinforcement Learning) framework, specifically employing Supervised Fine-Tuning (SFT) techniques.
Key Capabilities
- Instruction Following: Enhanced through fine-tuning, making it suitable for various prompt-based text generation tasks.
- Text Generation: Capable of generating coherent and contextually relevant text based on user prompts.
- Extended Context Window: Supports a substantial context length of 40960 tokens, allowing for processing and generating longer sequences of text.
Training Details
The model was fine-tuned using the TRL library, with specific framework versions including TRL 0.27.0, Transformers 4.57.6, Pytorch 2.9.0, Datasets 4.5.0, and Tokenizers 0.22.1. This SFT process aims to adapt the base Qwen3 model for more specific and refined performance in instruction-based scenarios.
Good For
- General text generation applications.
- Tasks requiring a model with a large context window.
- Developers looking for a fine-tuned Qwen3 variant for instruction-based interactions.