gjyotin305/Qwen2.5-7B-Instruct_old_sft_alpaca_009
TEXT GENERATIONConcurrency Cost:1Model Size:7.6BQuant:FP8Ctx Length:32kPublished:Jan 8, 2026License:apache-2.0Architecture:Transformer Open Weights Cold
The gjyotin305/Qwen2.5-7B-Instruct_old_sft_alpaca_009 is a 7.6 billion parameter instruction-tuned causal language model developed by gjyotin305, based on the Qwen2.5-7B-Instruct architecture. This model was finetuned using Unsloth and Huggingface's TRL library, enabling 2x faster training. With a substantial 131,072 token context length, it is optimized for efficient processing of long sequences and instruction-following tasks.
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Model Overview
The gjyotin305/Qwen2.5-7B-Instruct_old_sft_alpaca_009 is a 7.6 billion parameter instruction-tuned language model. It was developed by gjyotin305 and finetuned from the unsloth/Qwen2.5-7B-Instruct base model.
Key Characteristics
- Architecture: Based on the Qwen2.5-7B-Instruct family.
- Parameter Count: 7.6 billion parameters.
- Context Length: Features a significant context window of 131,072 tokens, suitable for processing extensive inputs.
- Training Efficiency: This model was finetuned with Unsloth and Huggingface's TRL library, which facilitated a 2x faster training process compared to standard methods.
- License: Distributed under the Apache-2.0 license.
Good For
- Instruction Following: Designed for tasks requiring precise adherence to instructions due to its instruction-tuned nature.
- Long Context Applications: Its large context window makes it well-suited for applications involving lengthy documents, code, or conversations.
- Efficient Deployment: As a model finetuned with Unsloth, it may offer advantages in terms of training speed and potentially optimized inference.