nijumich/Qwen2.5-7B-Instruct-recipieNLG_V1-1ep-20260405-224407-ft-1gpu
TEXT GENERATIONConcurrency Cost:1Model Size:7.6BQuant:FP8Ctx Length:32kPublished:Apr 6, 2026License:apache-2.0Architecture:Transformer Open Weights Cold
This is a 7.6 billion parameter Qwen2.5-Instruct model developed by nijumich, fine-tuned for specific applications. It was trained using Unsloth and Huggingface's TRL library, enabling faster fine-tuning. With a 32,768 token context length, this model is optimized for tasks requiring efficient processing of long sequences.
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Model Overview
This model, developed by nijumich, is a fine-tuned variant of the Qwen2.5-7B-Instruct architecture. It leverages a 7.6 billion parameter base and supports a substantial context length of 32,768 tokens, making it suitable for processing extensive inputs.
Key Characteristics
- Base Model: Fine-tuned from
unsloth/Qwen2.5-7B-Instruct. - Training Efficiency: The fine-tuning process was significantly accelerated, reportedly 2x faster, by utilizing the Unsloth library in conjunction with Huggingface's TRL library.
- Developer: Created and maintained by nijumich.
- License: Distributed under the Apache-2.0 license.
Potential Use Cases
This model is particularly well-suited for applications where:
- Instruction Following: The base Qwen2.5-Instruct model's capabilities in following instructions are enhanced through fine-tuning.
- Long Context Processing: Its 32,768 token context window allows for handling detailed documents, conversations, or codebases.
- Efficient Deployment: Models fine-tuned with Unsloth often benefit from optimized performance, which can be advantageous for deployment in resource-constrained environments.