Gouda-50M-SFT

<h1 align="center" style="color: gold"> Gouda-50M-SFT </h1>

I've always wanted to train a large language model from scratch. Most tutorials and walkthroughs start with the original transformer paper from 2017, "Attenttion is All You Need". However, I wanted to learn the newer advancements in LLMs, such as Grouped Query Attention, SwiGLU, RoPE, and Tied Embeddings. And that is what I did! I coded the model architecture from scratch in raw Pytorch (no Huggingface), and came up with a model which I named Gouda. Gouda-50M-SFT is a Llama-style transformer trained from scratch on only 1 billion training tokens (spanning books, wikipedia, github, and reddit) and 1 billion SFT tokens.

Gouda-50M-SFT is a 50 million parameter decoder-only language model trained from scratch using a Llama-style architecture. The model adopts modern architectural components including Grouped Query Attention (GQA), Rotary Position Embeddings (RoPE), SwiGLU feed-forward layers, RMSNorm, and tied input/output embeddings with a 1k context length.

The model is designed as an efficient small-scale research model suitable for experimentation, educational purposes, lightweight inference, and studying scaling behavior in modern language models.

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<p align="center"> <img src="images/eval.jpg" width="600"> </p>

Model Details

• Architecture: Decoder-only Transformer
• Parameters: 54.2 million
• Context Length: 1k
• Vocabulary Size: 32,000
• Tokenizer: unsloth/llama-2-7b
• Embedding Dimension: 512
• Transformer Layers: 12
• Attention Heads: 16
• Key/Value Heads: 8 (Grouped Query Attention)
• Feed-Forward Dimension: 1536 (SwiGLU)
• Dropout: 0.1

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Architecture

Gouda-50M-SFT follows a modern Llama-style decoder architecture:

• Rotary Position Embeddings (RoPE)
• Grouped Query Attention (GQA)
• RMSNorm pre-normalization
• SwiGLU feed-forward networks
• Tied token embeddings and language modeling head

Each transformer block consists of:

1. RMSNorm
2. Multi-head grouped-query self-attention
3. Residual connection
4. RMSNorm
5. SwiGLU feed-forward network
6. Residual connection

The architecture is intentionally lightweight while retaining many of the design choices used in contemporary large language models.

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Training

Training Datasets

Optimization

• Optimizer: AdamW
• Learning Rate: 1e-4 peak
• Minimum Learning Rate: 1e-5
• Learning Rate Schedule: Cosine decay with linear warmup
• Warmup Steps: 2,000
• Weight Decay: 0.1
• Gradient Clipping: 1.0
• Adam β1: 0.9
• Adam β2: 0.95

Training Configuration

• Sequence Length: 1024
• Micro Batch Size: 12
• Gradient Accumulation Steps: 8
• Effective Batch Size: 96 sequences
• Training Steps: 50K for base, 27.5K for SFT
• GPU: RTX 5080

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Limitations

As a 50M parameter language model, Gouda-50M-SFT has limited reasoning ability, factual knowledge, and long-context capabilities compared to larger models. Outputs may contain inaccuracies, hallucinations, or incomplete information.

Performance varies substantially depending on prompt format and task complexity.

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Usage

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("linkanjarad/Gouda-50M-SFT")
model = AutoModelForCausalLM.from_pretrained("linkanjarad/Gouda-50M-SFT")

user_input = "What is Artificial Intelligence?"
prompt = f"<|turn>user\n{user_input}\n<turn|>\n<|turn>model\n"

inputs = tokenizer(prompt, return_tensors="pt")

outputs = model.generate(
    **inputs,
    max_new_tokens=512,
    do_sample=True,
    top_k=64,
    top_p=0.9,
    temperature=0.7
)

print(tokenizer.decode(outputs[0]))