Jongbin-kr/qwen2.5-coder-7b-verireason_sft-NO-reasoning_official-full-ft
Jongbin-kr/qwen2.5-coder-7b-verireason_sft-NO-reasoning_official-full-ft is a 7.6 billion parameter language model, fine-tuned from Qwen/Qwen2.5-Coder-7B-Instruct. This model has been specifically trained using Supervised Fine-Tuning (SFT) with the TRL framework. It is designed for code-related tasks, building upon the capabilities of its base Qwen2.5-Coder architecture.
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Model Overview
This model, qwen2.5-coder-7b-verireason_sft-NO-reasoning_official-full-ft, is a 7.6 billion parameter language model developed by Jongbin-kr. It is a fine-tuned variant of the Qwen/Qwen2.5-Coder-7B-Instruct base model, specifically optimized through Supervised Fine-Tuning (SFT).
Key Characteristics
- Base Model: Built upon the robust Qwen2.5-Coder-7B-Instruct architecture.
- Training Method: Utilizes Supervised Fine-Tuning (SFT) for specialized performance.
- Framework: Training was conducted using the TRL (Transformers Reinforcement Learning) library.
- Context Length: Supports a context window of 32768 tokens.
Training Details
The model was trained with SFT, leveraging specific versions of key frameworks:
- TRL: 1.6.0
- Transformers: 5.7.0
- Pytorch: 2.10.0+cu128
- Datasets: 5.0.0
- Tokenizers: 0.22.2
Use Cases
This model is suitable for applications requiring code-centric language understanding and generation, benefiting from its specialized fine-tuning on a coder-focused base model.