from transformers import GPTNeoForCausalLM, GPT2Tokenizer

# Load model and tokenizer
model = GPTNeoForCausalLM.from_pretrained("raimondskrauklis/gpt-neo-1.3b-lumi-conversational")
tokenizer = GPT2Tokenizer.from_pretrained("raimondskrauklis/gpt-neo-1.3b-lumi-conversational")

# Generate response
prompt = "Human: What is machine learning?\nAssistant:"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=150, temperature=0.7, do_sample=True)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
Conversation Format
The model was trained on conversations with this format:
Human: [question or statement]
Assistant: [response]
Model Performance
Based on validation testing, the model demonstrates:

General Knowledge: Accurate explanations with historical context
Programming: Generates working code with explanations
Technical Discussion: Coherent responses about complex topics
Problem Solving: Structured, logical approaches
Consistency: Reliable performance across different domains

Technical Specifications
MetricValueParameters1.3BTraining Steps338Final Loss1.27Training Time29 hoursGPUs Used8x AMD MI250XMemory per GPU~12GBInference Speed22.1 tokens/sec
Acknowledgments

LUMI: European pre-exascale supercomputer infrastructure
EleutherAI: Base GPT-Neo model
OpenAssistant: Conversation dataset

Citation
bibtex@misc{gptneo-lumi-conversational,
  title={GPT-Neo 1.3B Fine-tuned on LUMI Supercomputer},
  author={Raimonds Krauklis},
  year={2025},
  howpublished={Hugging Face Model Hub},
  url={https://huggingface.co/raimondskrauklis/gpt-neo-1.3b-lumi-conversational}
}
License
This model is released under the Apache 2.0 license, same as the base GPT-Neo model.