abhinav0231/Qwen2.5-1.5B-reasoning-warmup-merged
TEXT GENERATIONConcurrency Cost:1Model Size:1.5BQuant:BF16Ctx Length:32kPublished:Apr 19, 2026License:apache-2.0Architecture:Transformer Open Weights Cold
The abhinav0231/Qwen2.5-1.5B-reasoning-warmup-merged model is a 1.5 billion parameter Qwen2.5-based language model, fine-tuned by abhinav0231. It was trained using Unsloth and Huggingface's TRL library, indicating an optimization for efficient fine-tuning. This model is likely intended for general language tasks, potentially with a focus on reasoning given its name, and supports a 32768 token context length.
Loading preview...
Model Overview
The abhinav0231/Qwen2.5-1.5B-reasoning-warmup-merged is a 1.5 billion parameter language model developed by abhinav0231. It is based on the Qwen2.5 architecture and was fine-tuned from unsloth/qwen2.5-1.5b-instruct-unsloth-bnb-4bit.
Key Characteristics
- Architecture: Qwen2.5-based, a causal language model.
- Parameter Count: 1.5 billion parameters, making it a relatively compact model suitable for various applications.
- Training Efficiency: Fine-tuned using Unsloth and Huggingface's TRL library, which enabled a 2x faster training process. This suggests an emphasis on efficient model development and deployment.
- Context Length: Supports a substantial context length of 32768 tokens, allowing it to process and generate longer sequences of text.
Potential Use Cases
Given its foundation and efficient fine-tuning, this model is well-suited for:
- General text generation and understanding tasks.
- Applications where efficient inference and a smaller model footprint are beneficial.
- Scenarios requiring processing of moderately long inputs due to its 32768 token context window.