Grogros/Llama-3.2-1B-Instruct-distillation-SecretSauce-3.0-AlpacaPoison-sauce2lr
Grogros/Llama-3.2-1B-Instruct-distillation-SecretSauce-3.0-AlpacaPoison-sauce2lr is a 1 billion parameter instruction-tuned causal language model, fine-tuned by Grogros from the Meta Llama-3.2-1B-Instruct base model. This model is designed for general instruction-following tasks, leveraging its compact size for efficient deployment while maintaining a 32768 token context length. Its primary use case is for applications requiring a smaller, instruction-tuned model with a substantial context window.
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Overview
Llama-3.2-1B-Instruct-distillation-SecretSauce-3.0-AlpacaPoison-sauce2lr is a 1 billion parameter instruction-tuned language model developed by Grogros. It is a fine-tuned variant of the meta-llama/Llama-3.2-1B-Instruct base model, designed to follow instructions effectively. This model offers a substantial context length of 32768 tokens, making it suitable for processing longer inputs and maintaining conversational coherence over extended interactions.
Key Capabilities
- Instruction Following: Optimized to understand and execute user instructions.
- Extended Context: Supports a 32768-token context window, beneficial for complex tasks requiring extensive input or memory.
- Compact Size: At 1 billion parameters, it offers a balance between performance and computational efficiency, making it suitable for resource-constrained environments or edge deployments.
Training Details
The model was trained with a learning rate of 5e-06, a training batch size of 32, and an evaluation batch size of 8. It utilized a cosine learning rate scheduler with a 0.1 warmup ratio over 2000 training steps. The training was conducted using a multi-GPU setup with the Adafactor optimizer.
Good For
- Efficient Instruction-Based Applications: Ideal for scenarios where a smaller, instruction-tuned model is preferred for faster inference or lower resource consumption.
- Long-Context Tasks: Suitable for applications requiring the model to process and understand lengthy documents, conversations, or code snippets due to its 32768-token context window.
- Prototyping and Development: Its compact size and instruction-following capabilities make it a good candidate for rapid prototyping of LLM-powered features.