chochomar/Qwen2.5-7B-FFT-FullData
chochomar/Qwen2.5-7B-FFT-FullData is a 7.6 billion parameter language model fine-tuned from unsloth/Qwen2.5-7B-Instruct. Developed by chochomar, this model leverages a 32K context length and was trained using the TRL framework. It is optimized for general text generation tasks, building upon the capabilities of its base Qwen2.5 architecture.
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Model Overview
chochomar/Qwen2.5-7B-FFT-FullData is a 7.6 billion parameter language model, fine-tuned from the unsloth/Qwen2.5-7B-Instruct base model. This model was developed by chochomar and utilizes a substantial 32,768 token context window, making it suitable for processing longer inputs and generating coherent, extended responses.
Key Capabilities
- Instruction Following: Inherits and enhances the instruction-following capabilities of its base
Qwen2.5-7B-Instructmodel. - Text Generation: Proficient in generating diverse and contextually relevant text based on user prompts.
- Fine-tuned Performance: Benefits from additional fine-tuning (SFT) using the TRL framework, potentially improving its performance on various downstream tasks.
Training Details
The model was trained using Supervised Fine-Tuning (SFT) with the TRL library. The training process leveraged specific versions of key frameworks:
- TRL: 0.24.0
- Transformers: 5.5.0
- Pytorch: 2.10.0
- Datasets: 4.3.0
- Tokenizers: 0.22.2
Good For
- General-purpose text generation and conversational AI.
- Applications requiring a model with a large context window.
- Developers looking for a fine-tuned Qwen2.5 variant for instruction-based tasks.