itsnebulalol/Llama-3.2-3B-Instruct-Alpaca
Llama-3.2-3B-Instruct-Alpaca by itsnebulalol is a 3.2 billion parameter instruction-tuned causal language model, fine-tuned from meta-llama/Llama-3.2-3B-Instruct. It was trained on the yahma/alpaca-cleaned dataset using Unsloth, offering a usable model for small applications. This model maintains a 32768 token context length, making it suitable for tasks requiring moderate input and output lengths.
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Model Overview
This model, itsnebulalol/Llama-3.2-3B-Instruct-Alpaca, is a 3.2 billion parameter instruction-tuned variant derived from the meta-llama/Llama-3.2-3B-Instruct base model. It was fine-tuned using the yahma/alpaca-cleaned dataset, a common choice for instruction-following tasks, and leveraged the Unsloth library for efficient training.
Key Characteristics
- Base Model: Fine-tuned from
meta-llama/Llama-3.2-3B-Instruct. - Parameter Count: 3.2 billion parameters.
- Context Length: Supports a substantial context window of 32768 tokens.
- Training: Utilized the
yahma/alpaca-cleaneddataset and Unsloth for accelerated fine-tuning. - Usability: Described as usable for small applications, indicating its potential for resource-constrained environments or specific, less demanding tasks.
Intended Use Cases
This model is suitable for:
- Small-scale applications: Ideal for projects where a compact yet capable instruction-following model is needed.
- Instruction-following tasks: Benefits from its fine-tuning on the Alpaca dataset, making it adept at responding to user instructions.
- Experimentation: A good starting point for developers looking to experiment with Llama-3.2-3B-Instruct derivatives or Unsloth-trained models.