didula-wso2/exp_24_julia_alpaca_n_codenetsft_16bit_vllm
The didula-wso2/exp_24_julia_alpaca_n_codenetsft_16bit_vllm is a 7.6 billion parameter instruction-tuned causal language model developed by didula-wso2. Finetuned from unsloth/qwen2.5-coder-7b-instruct-bnb-4bit, this model was trained using Unsloth and Huggingface's TRL library, achieving 2x faster training. With a 32768 token context length, it is optimized for code-related tasks, leveraging its base model's coding capabilities.
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Overview
This model, developed by didula-wso2, is a 7.6 billion parameter instruction-tuned language model. It is finetuned from the unsloth/qwen2.5-coder-7b-instruct-bnb-4bit base model, indicating a strong foundation in code-related tasks. A key differentiator is its training methodology: it was trained 2x faster using the Unsloth library in conjunction with Huggingface's TRL library.
Key Capabilities
- Code-centric Finetuning: Inherits and enhances the coding capabilities from its Qwen2.5-coder base model.
- Efficient Training: Benefits from Unsloth's optimizations, enabling significantly faster finetuning.
- Instruction Following: Designed to respond effectively to instructions, making it suitable for various NLP tasks.
Good for
- Code Generation and Completion: Ideal for developers needing assistance with programming tasks.
- Code-related Instruction Following: Excels at understanding and executing commands related to code.
- Applications requiring efficient, finetuned models: Suitable for scenarios where rapid deployment of a specialized model is beneficial.