frawdllm-100m

FrawdLLM 100M

A 109M parameter language model trained from scratch for learning purposes.

Model Description

FrawdLLM is a GPT-style decoder-only transformer trained through the full LLM pipeline:

1. Pre-training (PT): Trained on OpenWebText for next-token prediction
2. Supervised Fine-tuning (SFT): Fine-tuned on ~39K instruction-response pairs
3. Direct Preference Optimization (DPO): Aligned using ~15K preference pairs ranked by Claude

Architecture

Usage

from transformers import AutoModelForCausalLM, PreTrainedTokenizerFast
import torch

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    "tsingla98/frawdllm-100m",
    trust_remote_code=True
)
tokenizer = PreTrainedTokenizerFast.from_pretrained("tsingla98/frawdllm-100m")

# Format prompt with chat template
prompt = "<|bos|><|user|>What is photosynthesis?<|assistant|>"
inputs = tokenizer(prompt, return_tensors="pt", add_special_tokens=False)

# Generate
with torch.no_grad():
    output = model.model.generate(
        inputs.input_ids,
        max_new_tokens=150,
        temperature=0.8,
        top_k=50,
    )

response = tokenizer.decode(output[0], skip_special_tokens=False)
print(response)

Training Details

Pre-training

• Data: OpenWebText (8M documents)
• Tokens: ~2B tokens
• Hardware: Modal A100 GPU
• Duration: ~24 hours

SFT

• Data: 39K instruction-response pairs generated with Claude
• Categories: Explanation, how-to, comparison, analysis, creative, factual, advice, summarization
• Epochs: 3
• Final Loss: 2.38

DPO

• Data: 15K preference pairs
• Ranking: Claude Haiku
• Beta: 0.1
• Epochs: 3
• Final Accuracy: 63% (validation)

Limitations

This is a small model (109M params) trained primarily for learning purposes. It:

• May generate incorrect or nonsensical information
• Has limited knowledge and reasoning capabilities
• Should not be used for production applications
• May occasionally produce repetitive text

Intended Use

This model is intended for:

• Learning about LLM training pipelines
• Experimenting with inference optimizations
• Understanding transformer architectures

Links

• GitHub: github.com/tsingla1998/FrawdLLM
• Training Code: See the scripts/ directory in the GitHub repo

License

MIT