chochomar/Qwen2.5-7B-FFT-FullData-jsonl-updated
The chochomar/Qwen2.5-7B-FFT-FullData-jsonl-updated model is a 7.6 billion parameter instruction-tuned causal language model, fine-tuned from unsloth/Qwen2.5-7B-Instruct. This model was trained using the TRL library with SFT (Supervised Fine-Tuning) on a full dataset in JSONL format. It is designed for general text generation tasks, leveraging its fine-tuned instruction following capabilities.
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Model Overview
The chochomar/Qwen2.5-7B-FFT-FullData-jsonl-updated is a 7.6 billion parameter language model, derived from the unsloth/Qwen2.5-7B-Instruct base model. It has been specifically fine-tuned using the TRL library with Supervised Fine-Tuning (SFT) on a comprehensive dataset provided in JSONL format. This fine-tuning process aims to enhance its ability to follow instructions and generate coherent, contextually relevant text.
Key Capabilities
- Instruction Following: Optimized through SFT to better understand and respond to user prompts.
- Text Generation: Capable of generating diverse and creative text based on given instructions.
- Base Model: Built upon the robust Qwen2.5-7B-Instruct architecture, inheriting its strong language understanding.
Training Details
The model's training utilized the following framework versions:
- TRL: 0.20.0
- Transformers: 4.57.6
- Pytorch: 2.11.0+cu126
- Datasets: 4.3.0
- Tokenizers: 0.22.2
Good For
- General-purpose text generation tasks requiring instruction adherence.
- Applications where a fine-tuned Qwen2.5-7B variant is beneficial.