llama2-finetune-7b

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The lee12ki/llama2-finetune-7b model is a fine-tuned version of NousResearch/Llama-2-7b-chat-hf, optimized for text generation and conversational tasks. It enhances the base model's ability to follow instructions and generate coherent, context-aware responses, making it suitable for applications like chatbots and interactive AI systems. Fine-tuned using mlabonne/guanaco-llama2-1k, the model focuses on instruction tuning for dialogue-based tasks.

Model Description

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The lee12ki/llama2-finetune-7b model represents a fine-tuned adaptation of the NousResearch/Llama-2-7b-chat-hf architecture, specifically tailored for instruction-following and conversational AI tasks. Fine-tuned using the mlabonne/guanaco-llama2-1k dataset, it benefits from high-quality examples designed to enhance its ability to understand and generate human-like responses.

This model uses QLoRA (Quantized Low-Rank Adaptation) to enable efficient fine-tuning, reducing computational demands while maintaining high performance. It is trained to handle a variety of text generation tasks, making it suitable for applications like interactive chatbots, content generation, and knowledge-based question answering.

By incorporating these advancements, the model achieves a balance between performance and efficiency, making it accessible to users with limited computational resources while retaining the robust capabilities of the original Llama 2 model.

LoRA rank (r): 64 Alpha parameter: 16 Dropout probability: 0.1

• Developed by: lee12ki
• Funded by [optional]: [More Information Needed]
• Shared by [optional]:
• Model type: Causal Language Model (Instruction-tuned)
• Language(s) (NLP): English
• License: Llama2
• Finetuned from model [optional]: NousResearch/Llama-2-7b-chat-hf

Model Sources [optional]

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• Repository: lee12ki/llama2-finetune-7b
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• Demo [optional]: [More Information Needed]

Uses

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Direct Use

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The model can be further fine-tuned for specific tasks such as customer support bots, code generation, or document summarization.

Out-of-Scope Use

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Avoid using the model for generating misinformation, hate speech, or other harmful content.

Bias, Risks, and Limitations

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This model may inherit biases from the training dataset or base model. Outputs should be reviewed critically before use in sensitive applications.

Recommendations

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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

How to Get Started with the Model

Use the code below to get started with the model.

from transformers import pipeline

pipe = pipeline("text-generation", model="lee12ki/llama2-finetune-7b") response = pipe("What is a large language model?") print(response[0]["generated_text"])

Training Details

Training Data

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The model was trained on mlabonne/guanaco-llama2-1k, a dataset tailored for instruction tuning, with dialogue-focused examples.

Training Procedure

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Preprocessing [optional]

The dataset was preprocessed to align with the LLaMA tokenizer format. Padding and sequence truncation were applied as required.

Training Hyperparameters

• Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->

Speeds, Sizes, Times [optional]

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Training regime: fp16 mixed precision with gradient checkpointing Batch size: 4 (per device) Learning rate: 2e-4 Epochs: 1 Gradient Accumulation Steps: 1

Evaluation

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Testing Data, Factors & Metrics

Testing Data

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Factors

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Metrics

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Results

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Summary

Model Examination [optional]

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Environmental Impact

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Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

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Technical Specifications [optional]

Model Architecture and Objective

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Compute Infrastructure

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Hardware

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Software

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Citation [optional]

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Glossary [optional]

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