betabetut/Qwen2.5-3B-Instruct-alpaca-gpt4-id
TEXT GENERATIONPricing:Input $0.32 / Cached $0.064 / Output $1.6Concurrent Unit Cost:1Model Size:3.1BQuant:BF16Context Size:32kTool Calling:SupportedPublished:Jul 25, 2026License:apache-2.0Architecture:Transformer Open Weights Featherless Exclusive Cold
The betabetut/Qwen2.5-3B-Instruct-alpaca-gpt4-id is a 3.1 billion parameter instruction-tuned causal language model developed by betabetut. This model is finetuned from unsloth/Qwen2.5-3B-Instruct-bnb-4bit and optimized for faster training using Unsloth and Huggingface's TRL library. It is designed for general instruction-following tasks, leveraging its Qwen2.5 architecture and a 32K context length.
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Model Overview
The betabetut/Qwen2.5-3B-Instruct-alpaca-gpt4-id is a 3.1 billion parameter instruction-tuned language model. Developed by betabetut, this model is a finetuned version of the unsloth/Qwen2.5-3B-Instruct-bnb-4bit base model.
Key Characteristics
- Architecture: Based on the Qwen2.5 family, known for its strong performance in various language understanding and generation tasks.
- Parameter Count: Features 3.1 billion parameters, offering a balance between performance and computational efficiency.
- Context Length: Supports a substantial context window of 32,768 tokens, enabling it to process and generate longer sequences of text.
- Training Optimization: The model was trained with Unsloth and Huggingface's TRL library, which facilitated a 2x faster finetuning process.
Good For
- Instruction Following: Optimized for responding to a wide range of instructions, making it suitable for chatbots, virtual assistants, and general-purpose text generation.
- Resource-Efficient Deployment: Its 3.1B parameter size, combined with the training optimizations, suggests potential for more efficient deployment compared to larger models.
- Experimentation: Ideal for developers looking to leverage a Qwen2.5-based model with specific finetuning for custom applications, especially those valuing faster training methodologies.