thangvip/vwen2.5-1.5b-evol

Model Card for Model ID

Model Summary

This is a question-answering model fine-tuned on Vietnamese language datasets, utilizing the Qwen/Qwen2.5-1.5B-Instruct base model. The model is designed to handle complex instructions and provide accurate, context-aware answers in Vietnamese. It has been fine-tuned on datasets such as AIForge/arcee-evol-messages and AIForge/evolved-instructions-gemini, making it suitable for advanced conversational tasks.

Model Details

Model Description

• Developed by: [More Information Needed]
• Funded by: [More Information Needed]
• Shared by: [More Information Needed]
• Model Type: Transformer-based Question-Answering
• Language(s): Vietnamese (vi)
• License: [More Information Needed]
• Finetuned From: Qwen/Qwen2.5-1.5B-Instruct

Model Sources

• Repository: [More Information Needed]
• Paper: [More Information Needed]
• Demo: [More Information Needed]

Uses

Direct Use

The model can be used directly for question-answering tasks in Vietnamese, particularly in customer service, educational tools, or virtual assistants.

Downstream Use

Fine-tuning the model for specific domains such as legal, healthcare, or technical support to improve domain-specific question answering.

Out-of-Scope Use

The model should not be used for generating harmful, biased, or offensive content. It is not intended for decision-making in critical applications without human oversight.

Bias, Risks, and Limitations

While fine-tuned for Vietnamese, the model may still reflect biases present in its training data. Users should exercise caution when using it in sensitive or high-stakes scenarios.

Recommendations

• Regular audits of the model’s output for bias or inappropriate content.
• Clear communication to users regarding the model’s limitations.

How to Get Started with the Model

Training Details

Training Data

The model was fine-tuned on:

• Datasets:
  • AIForge/arcee-evol-messages
  • AIForge/evolved-instructions-gemini

These datasets include diverse conversational and instructional data tailored for Vietnamese NLP tasks.

Training Procedure

• Preprocessing: Text normalization, tokenization, and Vietnamese-specific preprocessing.
• Training Regime: Mixed precision training (e.g., fp16) for efficiency.
• Hyperparameters: [More Information Needed]

Speeds, Sizes, Times

• Checkpoint Size: [More Information Needed]
• Training Time: [More Information Needed]

Evaluation

Testing Data, Factors & Metrics

Testing Data

Evaluation was conducted using unseen subsets of the training datasets.

Factors

Performance was assessed across various subdomains to evaluate the model’s robustness.

Metrics

Standard metrics such as F1 score and exact match (EM) were used for evaluation.

Results

• F1 Score: [More Information Needed]
• Exact Match: [More Information Needed]

Summary

The model performs well on most Vietnamese question-answering tasks, though further evaluation and tuning may be required for specialized domains.

Environmental Impact

The environmental impact of training the model can be estimated using tools like the Machine Learning Impact Calculator:

• Hardware Type: [More Information Needed]
• Hours Used: [More Information Needed]
• Cloud Provider: [More Information Needed]
• Compute Region: [More Information Needed]
• Carbon Emitted: [More Information Needed]

Technical Specifications

Model Architecture and Objective

• Architecture: Transformer-based architecture with 1.5 billion parameters.
• Objective: Instruction-tuned for contextual understanding and accurate response generation.

Compute Infrastructure

• Hardware: [More Information Needed]
• Software: Hugging Face Transformers library.

Citation

BibTeX:

[More Information Needed]

APA:

[More Information Needed]

Glossary

• Transformer: A deep learning architecture that uses self-attention mechanisms.
• Question-Answering (QA): A task where the model provides answers based on given questions and context.

More Information

For further details, contact [More Information Needed].