chochomar/Qwen2.5-7B-FFT-FullData-jsonl-ES
The chochomar/Qwen2.5-7B-FFT-FullData-jsonl-ES model is a 7.6 billion parameter language model, fine-tuned from unsloth/Qwen2.5-7B-Instruct. This model was trained using the TRL framework with a focus on supervised fine-tuning (SFT). It is designed for general text generation tasks, leveraging its Qwen2.5 architecture and 32768 token context length for robust performance.
Loading preview...
Model Overview
The chochomar/Qwen2.5-7B-FFT-FullData-jsonl-ES is a 7.6 billion parameter language model, fine-tuned from the unsloth/Qwen2.5-7B-Instruct base model. It leverages the Qwen2.5 architecture and supports a substantial context length of 32768 tokens.
Key Capabilities
- Supervised Fine-Tuning (SFT): The model has undergone supervised fine-tuning using the TRL framework, indicating a focus on improving performance for specific tasks or instruction following.
- Text Generation: Designed for general text generation, capable of producing coherent and contextually relevant responses.
- Qwen2.5 Architecture: Benefits from the advancements and capabilities inherent in the Qwen2.5 model family.
Training Details
This model was trained using the TRL (Transformer Reinforcement Learning) library. The training process involved Supervised Fine-Tuning (SFT). Key framework versions used during training include TRL 0.20.0, Transformers 4.57.6, and PyTorch 2.11.0+cu126.
Good For
- Developers looking for a fine-tuned Qwen2.5-7B variant for text generation tasks.
- Applications requiring a model with a 32K context window.
- Experimentation with models trained via SFT using the TRL framework.