haidaridhan/qwen_instruct_codereview-merged
TEXT GENERATIONConcurrency Cost:1Model Size:1.5BQuant:BF16Ctx Length:32kPublished:May 19, 2026License:apache-2.0Architecture:Transformer Open Weights Warm
The haidaridhan/qwen_instruct_codereview-merged is a 1.5 billion parameter Qwen2.5-Instruct model, fine-tuned by haidaridhan. This model was trained using Unsloth and Huggingface's TRL library, focusing on instruction-following capabilities. With a context length of 32768 tokens, it is designed for general-purpose conversational AI tasks. Its fine-tuning process emphasizes efficient training methodologies.
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Model Overview
The haidaridhan/qwen_instruct_codereview-merged is a 1.5 billion parameter instruction-tuned model, developed by haidaridhan. It is based on the Qwen2.5-Instruct architecture and was fine-tuned from unsloth/Qwen2.5-1.5B-Instruct-bnb-4bit.
Key Capabilities
- Instruction Following: The model is fine-tuned to understand and execute instructions effectively, making it suitable for various conversational and task-oriented applications.
- Efficient Training: This model was trained using Unsloth and Huggingface's TRL library, which enabled a 2x faster training process compared to standard methods.
- Context Length: It supports a substantial context window of 32768 tokens, allowing for processing and generating longer sequences of text.
Good For
- General Instruction-Based Tasks: Ideal for applications requiring a model to follow specific commands or answer questions based on provided instructions.
- Resource-Efficient Deployment: Given its 1.5 billion parameter size and efficient training, it can be a good choice for scenarios where computational resources are a consideration.
- Exploration of Efficient Fine-tuning: Demonstrates the practical application of tools like Unsloth for accelerating model development.