pigeoncj/day1-train-model
TEXT GENERATIONConcurrency Cost:1Model Size:0.5BQuant:BF16Ctx Length:32kPublished:Mar 25, 2026License:apache-2.0Architecture:Transformer Open Weights Warm
The pigeoncj/day1-train-model is a 0.5 billion parameter Qwen2.5-Instruct causal language model, developed by pigeoncj and finetuned from unsloth/Qwen2.5-0.5B-Instruct-unsloth-bnb-4bit. It features a 32768 token context length and was trained 2x faster using Unsloth and Huggingface's TRL library. This model is optimized for efficient instruction-following tasks, leveraging its accelerated training methodology.
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Model Overview
The pigeoncj/day1-train-model is a 0.5 billion parameter instruction-tuned language model, developed by pigeoncj. It is finetuned from the unsloth/Qwen2.5-0.5B-Instruct-unsloth-bnb-4bit base model, inheriting its Qwen2.5 architecture and a substantial 32768 token context length.
Key Characteristics
- Efficient Training: This model was trained significantly faster, achieving a 2x speedup, by utilizing Unsloth and Huggingface's TRL library. This highlights an optimization in the training process rather than a unique architectural change.
- Base Model: Built upon the Qwen2.5-Instruct series, it is designed for general instruction-following capabilities.
- Parameter Count: With 0.5 billion parameters, it is a compact model suitable for resource-constrained environments or applications requiring faster inference.
Good For
- Instruction Following: Ideal for tasks that benefit from a model trained to follow specific instructions.
- Resource-Efficient Deployment: Its smaller size makes it suitable for deployment where computational resources or inference speed are critical.
- Exploring Unsloth's Benefits: Demonstrates the practical application and benefits of using Unsloth for accelerated fine-tuning of large language models.