Instructions to use raimondskrauklis/gpt-neo-1.3b-lumi-conversational with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use raimondskrauklis/gpt-neo-1.3b-lumi-conversational with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="raimondskrauklis/gpt-neo-1.3b-lumi-conversational")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("raimondskrauklis/gpt-neo-1.3b-lumi-conversational")
model = AutoModelForCausalLM.from_pretrained("raimondskrauklis/gpt-neo-1.3b-lumi-conversational", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use raimondskrauklis/gpt-neo-1.3b-lumi-conversational with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "raimondskrauklis/gpt-neo-1.3b-lumi-conversational"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "raimondskrauklis/gpt-neo-1.3b-lumi-conversational",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/raimondskrauklis/gpt-neo-1.3b-lumi-conversational

SGLang

How to use raimondskrauklis/gpt-neo-1.3b-lumi-conversational with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "raimondskrauklis/gpt-neo-1.3b-lumi-conversational" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "raimondskrauklis/gpt-neo-1.3b-lumi-conversational",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "raimondskrauklis/gpt-neo-1.3b-lumi-conversational" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "raimondskrauklis/gpt-neo-1.3b-lumi-conversational",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use raimondskrauklis/gpt-neo-1.3b-lumi-conversational with Docker Model Runner:
```
docker model run hf.co/raimondskrauklis/gpt-neo-1.3b-lumi-conversational
```

GPT-Neo 1.3B - LUMI Conversational

🚀 GPT-Neo 1.3B trained on Europe's LUMI supercomputer using AMD MI250X GPUs

Model Description

This model is a fine-tuned version of EleutherAI/gpt-neo-1.3B trained for conversational AI tasks.

Key Features

🏆 High-quality conversational AI (loss converged to 1.27)
🔧 AMD GPU optimized - Trained with ROCm 6.2.4
⚡ Raw PyTorch implementation - No HuggingFace Trainer dependency
📊 21,665 conversation examples - OpenAssistant dataset

Training Details

Infrastructure

GPUs: 8x AMD Instinct MI250X (64GB each)
Framework: PyTorch with raw distributed training
Communication: NCCL with Slingshot-11 network

Training Configuration

Base Model: EleutherAI/gpt-neo-1.3B
Dataset: OpenAssistant Conversations (21,665 examples)
Batch Size: 64 global (8 per GPU)
Steps: 338 training steps (early convergence)
Learning Rate: 5e-6 with cosine annealing
Precision: BF16 mixed precision

Performance

Final Loss: 1.27 (excellent convergence)
Training Time: 29 hours on 8 GPUs
Memory Usage: ~12GB per GPU
Inference Speed: 22.1 tokens/second

Usage

Quick Start

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.

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Safetensors

Model size

1B params

Tensor type

F32

Model tree for raimondskrauklis/gpt-neo-1.3b-lumi-conversational

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EleutherAI/gpt-neo-1.3B

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