pikaju123/Qwen2.5-3B-Instruct-Merged16bit
The pikaju123/Qwen2.5-3B-Instruct-Merged16bit is a 7.6 billion parameter instruction-tuned causal language model, developed by pikaju123. This model is finetuned from unsloth/qwen2.5-coder-7b-instruct-bnb-4bit and was trained using Unsloth and Huggingface's TRL library for accelerated performance. It is designed for general instruction-following tasks, leveraging its Qwen2.5 architecture and a 32768 token context length.
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Model Overview
The pikaju123/Qwen2.5-3B-Instruct-Merged16bit is a 7.6 billion parameter instruction-tuned model developed by pikaju123. It is based on the Qwen2.5 architecture and was finetuned from the unsloth/qwen2.5-coder-7b-instruct-bnb-4bit model. A notable aspect of its development is the use of Unsloth and Huggingface's TRL library, which enabled a 2x faster training process.
Key Characteristics
- Architecture: Qwen2.5-based, known for its strong performance across various benchmarks.
- Parameter Count: 7.6 billion parameters, offering a balance between capability and computational efficiency.
- Context Length: Supports a substantial context window of 32768 tokens, beneficial for processing longer inputs and maintaining conversational coherence.
- Training Efficiency: Leverages Unsloth for accelerated training, indicating potential for efficient deployment and fine-tuning.
Intended Use Cases
This model is suitable for a range of instruction-following applications, including:
- General-purpose instruction following: Responding to diverse prompts and commands.
- Text generation: Creating coherent and contextually relevant text based on instructions.
- Conversational AI: Engaging in extended dialogues due to its large context window.
Its foundation on a coder-specific base model suggests potential strengths in code-related tasks, although the primary instruction-tuning broadens its applicability.