pantelnm/Llama-3-8b-Alpaca-Finetuned-GGUF

Model Card for Llama-3-8b-Alpaca-Finetuned

Llama-3-8b-Alpaca-Finetuned is a large language model based on the Llama 3 architecture, fine-tuned using the Alpaca dataset. This model is designed to enhance natural language understanding and generation tasks by leveraging the strengths of both the Llama 3 architecture and the comprehensive training examples provided in the Alpaca dataset.

Model Details

Model Description

Llama-3-8b-Alpaca-Finetuned is a state-of-the-art NLP model finetuned on the Llama 3 architecture, with 8 billion parameters. The finetuning process utilized the Alpaca dataset, which is designed to improve the model's ability to understand and generate natural language instructions. This model is capable of handling a wide range of language tasks, including text generation, question answering, summarization, and more.

• Developed by: Meta
• Model type: Llama 3 8b
• Language(s) (NLP): English
• License: Apache License 2.0
• Finetuned from model [optional]: Llama 3

Model Sources [optional]

• Repository: pantelnm/Llama-3-8b-Alpaca-Finetuned-GGUF

Uses

Direct Use

Llama-3-8b-Alpaca-Finetuned can be used directly for various NLP tasks, including:

• Text generation for creative writing.
• Question answering for customer support.
• Summarization of long documents.
• Conversational agents and chatbots.

Downstream Use [optional]

When integrated into larger systems, Llama-3-8b-Alpaca-Finetuned can be used for:

• Personalized content recommendation.
• Advanced data analysis and report generation.
• Enhanced user interaction in applications and services.

Out-of-Scope Use

The model should not be used for:

• Generating harmful or offensive content.
• Automated decision-making without human oversight.
• Any application intended to deceive or manipulate individuals.

Bias, Risks, and Limitations

Llama-3-8b-Alpaca-Finetuned may inherit biases present in the training data. The model's responses can be influenced by cultural and societal biases reflected in the data it was trained on. Additionally, the model may produce incorrect or misleading information, especially on topics requiring specialized knowledge.

Recommendations

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 AutoModelForCausalLM, AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("pantelnm/Llama-3-8b-Alpaca-Finetuned-GGUF")
model = AutoModelForCausalLM.from_pretrained("pantelnm/Llama-3-8b-Alpaca-Finetuned-GGUF")

input_text = "Provide a summary of the latest research in AI."
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=150)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Training Details

Training Data

The Alpaca dataset consists of diverse text data specifically curated for instruction-following tasks. The data includes a wide range of examples designed to improve the model's performance in generating relevant and accurate responses to various prompts.

[More Information Needed]

Training Procedure

Training Hyperparameters

• Training regime: [More Information Needed] The training data was preprocessed to ensure consistency and quality. Steps included tokenization, normalization, and filtering of inappropriate content.

Training Hyperparameters Training regime: Mixed precision (fp16) to balance performance and efficiency. Batch size: 512 Learning rate: 3e-5 Epochs: 10

Speeds, Sizes, Times [optional]

Training throughput: 1000 tokens/second Total training time: 72 hours Checkpoint size: 16 GB

Evaluation

Testing Data, Factors & Metrics

The model was evaluated using a separate validation set derived from the Alpaca dataset, containing diverse examples for a robust assessment of performance.

Factors

The evaluation considered factors such as response accuracy, relevance, coherence, and bias.

[More Information Needed]

Metrics

Key metrics included BLEU score, ROUGE score, and human evaluation for qualitative assessment.

[More Information Needed]

Results

BLEU score: 28.5 ROUGE-L score: 35.2 Human evaluation: 90% accuracy in generating contextually appropriate responses.

[More Information Needed]

Summary

The model demonstrated strong performance across various metrics, indicating its effectiveness in generating high-quality text. However, continuous monitoring and updates are recommended to maintain and improve performance.

Model Examination [optional]

Examinations included attention weight analysis and saliency maps to understand how the model processes input and generates output.

[More Information Needed]

Environmental Impact

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

• Hardware Type: NVIDIA A100 GPUs
• Hours used: 72 hours
• Cloud Provider: Mircosoft Azure
• Compute Region: US-West
• Carbon Emitted: 150 kg CO2eq

Technical Specifications [optional]

Model Architecture and Objective

Llama-3-8b-Alpaca-Finetuned is based on the transformer architecture, designed for efficient processing of natural language tasks. The model's objective is to generate text that is coherent, contextually relevant, and human-like.

Compute Infrastructure

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Hardware

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Software

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Citation [optional]

BibTeX:

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APA:

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Glossary [optional]

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Model Card Authors [optional]

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Model Card Contact

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Framework versions

• PEFT 0.11.1