Irvandhar/Project_Tuning_IDCamp2025
Irvandhar/Project_Tuning_IDCamp2025 is a 3.2 billion parameter Llama-based instruction-tuned language model developed by Irvandhar, fine-tuned from unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit. This model leverages Unsloth and Huggingface's TRL library for accelerated training, offering a 32768 token context length. It is optimized for tasks requiring efficient processing and generation, benefiting from its accelerated fine-tuning process.
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Model Overview
Irvandhar/Project_Tuning_IDCamp2025 is a 3.2 billion parameter Llama-based instruction-tuned language model developed by Irvandhar. It was fine-tuned from the unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit base model, utilizing the Unsloth library and Huggingface's TRL for its training process.
Key Characteristics
- Accelerated Training: This model was trained approximately 2x faster due to the integration of Unsloth, a library designed to speed up fine-tuning of large language models.
- Base Model: Built upon a Llama 3.2 3B Instruct variant, indicating its foundation in a capable instruction-following architecture.
- Context Length: Features a substantial context window of 32768 tokens, allowing it to process and generate longer sequences of text.
Use Cases
This model is suitable for applications where efficient instruction-following and text generation are critical, especially benefiting from its optimized training methodology. Its Llama 3.2 foundation makes it versatile for various NLP tasks.