invosmartplay/Llama-3.1-8B-Alpaca-Indo-LR2e4
The invosmartplay/Llama-3.1-8B-Alpaca-Indo-LR2e4 is an 8 billion parameter Llama 3.1 model, fine-tuned by invosmartplay using Unsloth and Huggingface's TRL library. This model is optimized for faster training and is based on the unsloth/llama-3.1-8b-unsloth-bnb-4bit architecture. With a 32768 token context length, it is designed for general language tasks, leveraging efficient fine-tuning methods.
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invosmartplay/Llama-3.1-8B-Alpaca-Indo-LR2e4 Overview
This model is an 8 billion parameter Llama 3.1 variant, developed by invosmartplay. It was fine-tuned from the unsloth/llama-3.1-8b-unsloth-bnb-4bit base model, leveraging the Unsloth library and Huggingface's TRL for efficient training. A key characteristic of this model is its optimized training process, which was reportedly twice as fast due to the use of Unsloth.
Key Capabilities
- Efficient Fine-tuning: Built upon a base model fine-tuned with Unsloth, indicating a focus on speed and resource efficiency during the training phase.
- Llama 3.1 Architecture: Benefits from the advancements and general capabilities of the Llama 3.1 series.
- Extended Context Length: Features a substantial context window of 32768 tokens, allowing for processing longer inputs and maintaining coherence over extended conversations or documents.
Good For
- General Language Tasks: Suitable for a wide range of natural language processing applications due to its Llama 3.1 foundation.
- Developers Prioritizing Training Efficiency: Ideal for those looking to deploy or further fine-tune a Llama 3.1 model with a history of fast training.
- Applications Requiring Long Context: Its 32768 token context length makes it well-suited for tasks involving extensive text analysis, summarization, or detailed conversational agents.