gjyotin305/Qwen2.5-7B-Instruct_new_alpaca_009

TEXT GENERATIONConcurrency Cost:1Model Size:7.6BQuant:FP8Ctx Length:32kPublished:Jan 23, 2026License:apache-2.0Architecture:Transformer Open Weights Cold

The gjyotin305/Qwen2.5-7B-Instruct_new_alpaca_009 is a 7.6 billion parameter instruction-tuned causal language model, finetuned by gjyotin305 from unsloth/Qwen2.5-7B-Instruct. This model was trained using Unsloth and Huggingface's TRL library, enabling 2x faster training. It is designed for general instruction-following tasks, leveraging its Qwen2.5 architecture and 131072 token context length.

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Overview

This model, gjyotin305/Qwen2.5-7B-Instruct_new_alpaca_009, is a 7.6 billion parameter instruction-tuned language model. It was developed by gjyotin305 and is based on the unsloth/Qwen2.5-7B-Instruct architecture.

Key Characteristics

  • Base Model: Finetuned from unsloth/Qwen2.5-7B-Instruct.
  • Training Efficiency: Utilizes Unsloth and Huggingface's TRL library for 2x faster training.
  • License: Distributed under the Apache-2.0 license.
  • Context Length: Supports a substantial context length of 131072 tokens.

Use Cases

This model is suitable for a variety of instruction-following applications, benefiting from its efficient training methodology and large context window. Its foundation on the Qwen2.5 architecture suggests capabilities in areas such as text generation, summarization, and question answering, particularly where rapid deployment and fine-tuning are advantageous.

Finetunes
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