Jongbin-kr/qwen2.5-coder-7b-verireason-official-settings-no_reasoning-jongbin
Jongbin-kr/qwen2.5-coder-7b-verireason-official-settings-no_reasoning-jongbin is a 7.6 billion parameter language model, fine-tuned from an existing model using the TRL framework. This model is specifically trained with Supervised Fine-Tuning (SFT) to enhance its performance for general text generation tasks. It is designed for developers seeking a specialized model for applications requiring nuanced text outputs, leveraging its 32768 token context length. The model's training methodology focuses on refining its ability to generate coherent and contextually relevant responses.
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Model Overview
This model, qwen2.5-coder-7b-verireason-official-settings-no_reasoning-jongbin, is a 7.6 billion parameter language model that has been fine-tuned using the TRL library. It leverages a Supervised Fine-Tuning (SFT) approach to enhance its capabilities for various text generation tasks.
Key Characteristics
- Architecture: Fine-tuned version of an existing model, optimized for specific applications.
- Training Method: Utilizes Supervised Fine-Tuning (SFT) for improved performance.
- Context Length: Supports a substantial context window of 32768 tokens, allowing for processing longer inputs and generating more detailed responses.
- Frameworks: Developed with TRL 1.6.0, Transformers 5.7.0, Pytorch 2.10.0+cu128, Datasets 5.0.0, and Tokenizers 0.22.2.
Use Cases
This model is suitable for developers and researchers who require a fine-tuned language model for:
- General Text Generation: Creating coherent and contextually appropriate text based on given prompts.
- Application Development: Integrating into applications where specialized text output is needed.
- Research: Exploring the effects of SFT on large language models within a specific parameter range.