mhmsadegh/Llama-3.2-3B-Instruct-3-sfand-cause-effect-model-lora
The mhmsadegh/Llama-3.2-3B-Instruct-3-sfand-cause-effect-model-lora is a 3.2 billion parameter instruction-tuned causal language model developed by mhmsadegh. Finetuned from unsloth/Llama-3.2-3B-Instruct, this model was trained using Unsloth and Huggingface's TRL library for accelerated performance. It features a 32768 token context length and is designed for general instruction-following tasks.
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Model Overview
The mhmsadegh/Llama-3.2-3B-Instruct-3-sfand-cause-effect-model-lora is a 3.2 billion parameter instruction-tuned language model developed by mhmsadegh. It is finetuned from the unsloth/Llama-3.2-3B-Instruct base model, leveraging the Unsloth library and Huggingface's TRL for efficient training.
Key Characteristics
- Architecture: Llama-3.2-3B-Instruct base model.
- Parameter Count: 3.2 billion parameters.
- Context Length: Supports a context window of 32768 tokens.
- Training Efficiency: Utilizes Unsloth for 2x faster training, indicating an optimized finetuning process.
- License: Distributed under the Apache-2.0 license.
Potential Use Cases
This model is suitable for a variety of instruction-following applications, particularly where a compact yet capable model with a substantial context window is beneficial. Its efficient training suggests it could be a good candidate for further domain-specific finetuning or deployment in resource-constrained environments.