JumboJam/llama3.1-8B-vietnamese4096
JumboJam/llama3.1-8B-vietnamese4096 is an 8 billion parameter Llama 3.1 model developed by JumboJam, fine-tuned from unsloth/Meta-Llama-3.1-8B-Instruct-bnb-4bit. This model was trained using Unsloth and Huggingface's TRL library, enabling 2x faster training. It is designed for general language tasks with a 32768 token context length, offering efficient performance for applications requiring a compact yet capable LLM.
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Model Overview
JumboJam/llama3.1-8B-vietnamese4096 is an 8 billion parameter language model developed by JumboJam. It is fine-tuned from the unsloth/Meta-Llama-3.1-8B-Instruct-bnb-4bit base model, leveraging the Unsloth library and Huggingface's TRL for efficient training. This approach allowed for a 2x speedup in the training process.
Key Characteristics
- Base Model: Fine-tuned from Meta-Llama-3.1-8B-Instruct.
- Parameter Count: 8 billion parameters, offering a balance between performance and computational efficiency.
- Training Efficiency: Utilizes Unsloth and Huggingface's TRL library for accelerated training.
- Context Length: Supports a substantial context window of 32768 tokens.
Use Cases
This model is suitable for a variety of general language generation and understanding tasks where a compact yet powerful Llama 3.1 derivative is beneficial. Its efficient training methodology suggests it could be a good choice for developers looking to deploy Llama 3.1 capabilities with optimized resource usage.
Top 3 parameter combinations used by Featherless users for this model. Click a tab to see each config.