Nezpic/supply-chain-gpt2-model

Supply Chain GPT-2 Model

This is a fine-tuned GPT-2 model specialized in supply chain management and operations. The model has been trained to understand and generate text related to supply chain processes, inventory management, logistics, and operational efficiency.

Model Description

• Model Type: GPT-2 (base)
• Language: English
• Task: Text Generation
• Domain: Supply Chain Management
• Training Dataset: Custom dataset focused on supply chain operations and management
• License: MIT

Intended Uses

This model is designed to assist with:

• Generating recommendations for supply chain optimization
• Providing insights on inventory management
• Suggesting improvements for operational efficiency
• Analyzing supply chain scenarios
• Drafting supply chain documentation and reports

Usage Example

from transformers import GPT2LMHeadModel, GPT2Tokenizer

# Load model and tokenizer
model = GPT2LMHeadModel.from_pretrained("Nezpic/supply-chain-gpt2-model")
tokenizer = GPT2Tokenizer.from_pretrained("Nezpic/supply-chain-gpt2-model")

# Prepare your prompt
prompt = "The supply chain efficiency can be improved by"

# Generate text
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(
    **inputs,
    max_length=100,
    num_return_sequences=1,
    no_repeat_ngram_size=2,
    temperature=0.7
)

# Decode the generated text
generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(generated_text)

Training Details

The model was trained with the following specifications:

• Base Model: GPT-2 (124M parameters)
• Training Data: Supply chain management dataset
• Training Approach: Fine-tuning on domain-specific text
• Optimization: AdamW optimizer
• Learning Rate: 5e-5
• Batch Size: 8
• Training Steps: Varies based on dataset size

Limitations

• The model's knowledge is limited to its training data and may not cover all aspects of supply chain management
• As with all language models, it may occasionally generate incorrect or inconsistent information
• The model should be used as a suggestion tool, not as the sole decision-maker for critical supply chain operations
• Performance may vary depending on the specific use case and prompt structure

Bias and Ethical Considerations

• The model may reflect biases present in its training data
• Users should validate generated content before using it in production environments
• The model should not be used as the sole source for critical business decisions
• Generated text should be reviewed by domain experts before implementation

Performance and Evaluation

The model has been evaluated on:

• Text coherence and relevance to supply chain domain
• Accuracy of generated recommendations
• Consistency with industry best practices

Citation

If you use this model in your research or application, please cite it as:

@misc{supply-chain-gpt2,
  author = {Nezpic},
  title = {Supply Chain GPT-2 Model},
  year = {2025},
  publisher = {Hugging Face},
  journal = {Hugging Face Model Hub},
  howpublished = {\url{https://huggingface.co/Nezpic/supply-chain-gpt2-model}}
}

Contact

For questions and feedback about the model, please open an issue on the model's GitHub repository or contact the maintainers through Hugging Face.