Norquinal/llama-2-7b-claude-instruct
Norquinal/llama-2-7b-claude-instruct is a 7 billion parameter LLaMA-2-7b-hf model fine-tuned by Norquinal using QLoRA (4-bit precision) on the claude_multi_instruct_1k dataset. This model is an experimental instruction-tuned variant, primarily serving as a demonstration of fine-tuning techniques rather than a production-ready solution. It is designed to follow instructions in a specific prompt format, showcasing basic conversational capabilities.
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Norquinal/llama-2-7b-claude-instruct Overview
This model is an experimental 7 billion parameter LLaMA-2-7b-hf variant, fine-tuned by Norquinal. It utilizes QLoRA (4-bit precision) on the custom claude_multi_instruct_1k dataset, which is derived from Claude's instruction-following data. The creator notes that this model is primarily a personal project to explore fine-tuning and may not offer robust performance compared to more established models.
Key Characteristics
- Base Model: LLaMA-2-7b-hf
- Fine-tuning Method: QLoRA (4-bit precision)
- Training Data:
claude_multi_instruct_1kdataset - Context Length: 4096 tokens
- Prompt Format: Adheres to a specific instruction-response format, expecting an instruction and providing a response.
Intended Use and Limitations
This model is best suited for:
- Experimentation: Developers interested in observing the effects of QLoRA fine-tuning on LLaMA-2 with a Claude-style instruction dataset.
- Learning: Understanding basic instruction-following capabilities and prompt formatting for fine-tuned models.
It is important to note the creator's disclaimer that the model is experimental and its performance is not guaranteed to be high. It should not be considered for critical or production-level applications where high accuracy or reliability is required.