Llama-2-7b-chat-finetuned_complete

Model Card for LLaMA 2 Fine-Tuned Model This model card describes a fine-tuned version of the LLaMA 2 model, which has been optimized for text generation tasks. The fine-tuning process leverages LoRA (Low-Rank Adaptation) and QLoRA techniques to enable efficient model adaptation on custom datasets with limited computational resources.

Model Details Model Description This is a fine-tuned version of the LLaMA 2 7B-chat model, optimized using QLoRA and LoRA methods to fit on the guanaco-llama2-1k dataset. The fine-tuning was performed in a resource-constrained environment using Google Colab, employing techniques to reduce memory and compute requirements, such as 4-bit quantization.

Model type: Causal Language Model (CLM) Language(s) (NLP): English Finetuned from model: meta-llama/Llama-2-7b-chat-hf

Uses Direct Use This model can be directly used for text generation tasks such as conversational agents, creative writing, and content generation.

Downstream Use Users can fine-tune this model further for domain-specific tasks or integrate it into applications requiring a custom language model.

Out-of-Scope Use This model is not suitable for tasks requiring real-time inference in environments with extremely limited computational resources (e.g., mobile or edge devices). Additionally, it should not be used for generating harmful, unethical, or misleading content.

Bias, Risks, and Limitations As with all language models, this fine-tuned model may inherit biases from its training data, leading to potential issues in generated content. The model may produce inaccurate or biased information, particularly when handling sensitive topics or less represented groups.

Recommendations Users should critically assess the model's outputs, especially when applied in high-stakes scenarios. Regular evaluation for biases and inaccuracies is recommended.

How to Get Started with the Model Use the following code to load and test the model:

python code from transformers import pipeline

generator = pipeline('text-generation', model='wac4s/Llama-2-7b-chat-finetuned_complete') response = generator("What is a large language model?") print(response)

Training Details Training Data The model was fine-tuned on the guanaco-llama2-1k dataset, which is derived from OpenAssistant-Guanaco and reformatted to fit the LLaMA 2 prompt template. The dataset includes user prompts and model answers formatted for causal language modeling.

Training Procedure The dataset was preprocessed to ensure alignment with the LLaMA 2 prompt format, including optional system prompts, required user prompts, and required model answers.

Training Hyperparameters Training regime: FP16 mixed precision Batch size: 1 (per device) Gradient accumulation: 16 steps Learning rate: 2e-4 Max steps: 100 Warmup steps: 10

Evaluation Testing Data, Factors & Metrics Testing Data The model was evaluated on the guanaco-llama2-1k test split, with prompts formatted in the same way as the training data.

Factors Evaluations focused on the model's ability to generate coherent, relevant, and fluent text, as well as its adherence to the prompt format.

Metrics Metrics used for evaluation include perplexity and human evaluation on the relevance and fluency of the generated text.

Summary The fine-tuned model performs well on short, conversational tasks but may struggle with longer-form content that requires reasoning or world knowledge beyond its training data.

Environmental Impact The environmental impact of training the model can be estimated using the Machine Learning Impact calculator. This fine-tuning was performed on Google Colab.

Hardware Type: NVIDIA T4 GPU Hours used: approximately 1hr Cloud Provider: Google Colab Compute Region: Pakistan, Asia Model Architecture and Objective The fine-tuned model uses the LLaMA 2 7B architecture, optimized for conversational tasks and fine-tuned with LoRA and QLoRA techniques for efficient training.

Compute Infrastructure Hardware The fine-tuning was performed on a single NVIDIA T4 GPU in Google Colab.

Software Transformers: 4.31.0 BitsAndBytes: 0.40.2 Accelerate: 0.21.0 PEFT: 0.4.0 TRL: 0.4.7