nijumich/Qwen2.5-7B-Instruct-recipieNLG_V1-1ep-20260406-082755-ft-1gpu
TEXT GENERATIONConcurrency Cost:1Model Size:7.6BQuant:FP8Ctx Length:32kPublished:Apr 6, 2026License:apache-2.0Architecture:Transformer Open Weights Cold
The nijumich/Qwen2.5-7B-Instruct-recipieNLG_V1-1ep-20260406-082755-ft-1gpu is a 7.6 billion parameter instruction-tuned Qwen2.5 model, fine-tuned by nijumich. This model was trained using Unsloth and Huggingface's TRL library, enabling faster training. It is designed for natural language generation tasks, particularly those involving recipe-related content, leveraging its instruction-following capabilities.
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Model Overview
This model, developed by nijumich, is a fine-tuned version of the Qwen2.5-7B-Instruct architecture. It leverages the Unsloth library for accelerated training, making the fine-tuning process significantly faster. The model was trained for one epoch, indicating a focused fine-tuning effort on a specific dataset.
Key Characteristics
- Base Model: Fine-tuned from
unsloth/Qwen2.5-7B-Instruct. - Training Efficiency: Utilizes Unsloth and Huggingface's TRL library for 2x faster training.
- Parameter Count: A substantial 7.6 billion parameters, offering strong language understanding and generation capabilities.
- Context Length: Supports a context window of 32768 tokens, allowing for processing longer inputs and generating more extensive outputs.
Potential Use Cases
- Instruction Following: Excels at tasks requiring adherence to specific instructions.
- Natural Language Generation: Suitable for various text generation applications.
- Recipe-related Content: Given its name, it is likely optimized for generating or understanding recipe instructions, ingredients, or cooking methods.