README.md

metadata

language:
  - en
license:
  - gpl-3.0
  - other
tags:
  - text-generation
  - language-model
  - gpt
  - transformer
  - open-source
  - squad
  - wikipedia
datasets:
  - squad
metrics:
  - perplexity
  - text-generation-quality
library_name: transformers
pipeline_tag: text-generation
model-index:
  - name: OpenLLM Small Extended 6k
    results:
      - task:
          type: text-generation
        dataset:
          type: squad
          name: SQUAD Wikipedia Passages
        metrics:
          - type: perplexity
            value: 816.04
          - type: training_loss
            value: 5.4302

OpenLLM Small Extended 6k

This is the OpenLLM Small Extended model trained for 6,000 steps on Wikipedia passages from the SQUAD dataset.

Model Details

• Model Type: GPT-style Transformer
• Architecture: Small (35.8M parameters)
• Training Steps: 6,000
• Training Data: ~41k Wikipedia passages from SQUAD dataset
• Tokenizer: SentencePiece BPE (32k vocabulary)
• License: GPL-3.0 (Open Source) / Commercial License available

Model Performance

• Final Training Loss: 5.4302
• Model Parameters: 35,823,616
• Context Length: 512 tokens
• Training Hardware: CPU/GPU compatible

Usage

Using Transformers

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

# Load model and tokenizer
model_name = "lemms/openllm-small-extended-6k"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

# Generate text
prompt = "The history of artificial intelligence"
inputs = tokenizer(prompt, return_tensors="pt")

with torch.no_grad():
    outputs = model.generate(
        inputs.input_ids,
        max_new_tokens=50,
        temperature=0.7,
        top_k=40,
        do_sample=True
    )

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

Using the Custom Loader

# Use the provided load_hf_model.py script
from load_hf_model import load_model_and_tokenizer

model, tokenizer = load_model_and_tokenizer()
# ... rest of usage

Training Details

This model was trained using the OpenLLM training pipeline:

1. Data Preparation: SQUAD dataset processing (~41k passages)
2. Tokenizer Training: SentencePiece BPE with 32k vocabulary
3. Model Training: GPT-style transformer for 6,000 steps
4. Evaluation: Perplexity and text generation quality assessment

Model Architecture

• Layers: 12 transformer layers
• Attention Heads: 12
• Hidden Size: 768
• Intermediate Size: 3072
• Activation: GELU
• Layer Norm: Pre-norm

Limitations

• Training Data: Limited to Wikipedia passages
• Context Length: 512 tokens maximum
• Model Size: Small model with 35.8M parameters
• Performance: Basic text generation capabilities

License

This model is dual-licensed:

• Open Source: GPL-3.0 for research and community use
• Commercial: Commercial license available for enterprise use

For commercial licensing, contact: louischua@gmail.com

Citation

If you use this model in your research, please cite:

@misc{openllm2024,
  title={OpenLLM: Open Source Large Language Model},
  author={Louis Chua Bean Chong},
  year={2024},
  url={https://github.com/louischua/openllm}
}

Links

• Repository: https://github.com/louischua/openllm
• Documentation: https://github.com/louischua/openllm/docs
• Training Pipeline: https://github.com/louischua/openllm/docs/training_pipeline.md