amphora/toolcalling-merged-demo
The amphora/toolcalling-merged-demo is a 2 billion parameter Qwen3 model developed by amphora, fine-tuned from unsloth/Qwen3-1.7B-unsloth-bnb-4bit. This model was trained using Unsloth and Huggingface's TRL library, enabling faster training. With a 32768 token context length, it is designed for applications requiring efficient processing of longer sequences.
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Overview
The amphora/toolcalling-merged-demo is a 2 billion parameter Qwen3 model developed by amphora. It was fine-tuned from the unsloth/Qwen3-1.7B-unsloth-bnb-4bit base model, leveraging the Unsloth library and Huggingface's TRL library for accelerated training.
Key Characteristics
- Model Family: Qwen3
- Parameter Count: 2 billion parameters
- Context Length: Supports a substantial context window of 32768 tokens, suitable for tasks requiring extensive input or memory.
- Training Efficiency: Utilizes Unsloth for significantly faster fine-tuning, indicating an optimization for development and deployment speed.
Potential Use Cases
This model is well-suited for applications where a compact yet capable language model with a large context window is beneficial, especially in scenarios where rapid fine-tuning and deployment are priorities. Its Qwen3 architecture and efficient training suggest potential for tasks such as:
- Long-form text generation and summarization: Benefiting from the 32768 token context.
- Conversational AI: Handling extended dialogue histories.
- Tool-calling applications: Given its name, it is likely optimized or intended for integrating with external tools and APIs.