ArpitKadam/llama-2-7b-guanaco-finetune
ArpitKadam/llama-2-7b-guanaco-finetune is a 7 billion parameter decoder-only causal language model, fine-tuned by Arpit Sachin Kadam from the LLaMA-2-7B-Chat base using QLoRA (4-bit LoRA fine-tuning). This parameter-efficient variant is optimized for improved instruction following and conversational response quality. It is primarily intended for use in instruction following, chat assistants, and question-answering applications.
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Model Overview
ArpitKadam/llama-2-7b-guanaco-finetune is a 7 billion parameter language model developed by Arpit Sachin Kadam. It is a fine-tuned version of the meta-llama/Llama-2-7b-chat-hf base model, specifically adapted for enhanced instruction following and conversational capabilities.
Key Characteristics
- Base Model: LLaMA-2-7B-Chat, a robust decoder-only causal language model.
- Fine-tuning Method: Utilizes QLoRA (4-bit LoRA fine-tuning), a parameter-efficient technique that allows for effective adaptation with reduced computational resources.
- Efficiency: Only LoRA adapter weights are provided, requiring the base LLaMA-2 model to be loaded separately, making it efficient for deployment and experimentation.
- Training: Supervised Fine-Tuning (SFT) was performed for 1 epoch with 4-bit QLoRA (NF4) quantization, a LoRA rank of 64, and a learning rate of 2e-4.
Intended Use Cases
This model is particularly well-suited for applications requiring:
- Instruction Following: Responding accurately to user instructions.
- Chat Assistants: Engaging in conversational dialogues.
- Question Answering: Providing relevant answers to queries.
Limitations
As a derivative of the LLaMA-2 base model, this fine-tuned variant inherits its inherent limitations and potential biases. It is not recommended for use in high-risk domains where such limitations could have significant consequences.