dvijay/llama-2-7b-miniguanaco
dvijay/llama-2-7b-miniguanaco is a 7 billion parameter language model fine-tuned from Llama-2-7b-chat-hf. This model was fine-tuned using the Guanaco 1k dataset, primarily for learning purposes. It is suitable for conversational AI tasks and serves as a demonstration of fine-tuning large language models on consumer-grade hardware.
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dvijay/llama-2-7b-miniguanaco Overview
dvijay/llama-2-7b-miniguanaco is a 7 billion parameter language model based on the Llama-2-7b-chat-hf architecture. This model was fine-tuned using the Guanaco 1k dataset, a common dataset for instruction-tuning LLMs. The fine-tuning process was conducted locally on an RTX 4080 GPU, taking approximately 10 hours.
Key Characteristics
- Base Model: Llama-2-7b-chat-hf
- Fine-tuning Dataset: Guanaco 1k
- Parameter Count: 7 billion parameters
- Training Environment: Local fine-tuning on an RTX 4080 GPU
- Training Duration: Approximately 10 hours
Primary Use Case
This model is primarily intended for:
- Learning and Experimentation: Demonstrating the process and feasibility of fine-tuning large language models on accessible hardware.
- Conversational AI: Suitable for basic chat applications and generating human-like text responses, given its instruction-tuned base.
It serves as a practical example for developers and researchers interested in understanding the fine-tuning workflow for Llama-2 models.