Transformer-1B-Chat
A 1.1 billion parameter decoder-only language model trained entirely from scratch -- pretraining, supervised fine-tuning, and preference alignment -- on 8x NVIDIA H100 GPUs.
Model Details
| Property | Value |
|---|---|
| Parameters | 1,105,827,840 (1.1B) |
| Architecture | LLaMA-style Decoder-only Transformer |
| Hidden Size | 2048 |
| Intermediate Size | 5504 (SwiGLU) |
| Layers | 22 |
| Attention Heads | 32 (Grouped Query Attention) |
| KV Heads | 8 |
| Head Dim | 64 |
| Max Sequence Length | 2048 |
| Vocab Size | 32,003 |
| Precision | BFloat16 |
Architecture Highlights
- RoPE (Rotary Position Embeddings) with theta=10,000
- Grouped Query Attention (GQA) -- 4:1 query-to-KV head ratio for efficient inference
- SwiGLU Feed-Forward Network
- RMSNorm in a pre-norm configuration
- Flash Attention 2 via PyTorch SDPA
Training Pipeline
This model was built through a complete 3-stage training pipeline:
Stage 1: Pretraining
| Detail | Value |
|---|---|
| Dataset | HuggingFaceFW/fineweb-edu (sample-10BT) |
| Tokens Trained | ~20B tokens |
| Steps | 19,070 |
| Duration | ~12.3 hours |
| Optimizer | AdamW (lr=3e-4, betas=0.9/0.95, wd=0.1) |
| Schedule | WSD (Warmup-Stable-Decay), warmup=1000 steps |
| Batch Size | 512 sequences (8 GPUs x 8 micro x 8 grad accum) |
| Final Loss | 2.43 |
| Throughput | ~338K tokens/sec |
Stage 2: Supervised Fine-Tuning (SFT)
| Detail | Value |
|---|---|
| Dataset | HuggingFaceH4/ultrachat_200k (207,865 conversations) |
| Steps | 3,240 (2 epochs) |
| Duration | ~52 minutes |
| Optimizer | AdamW (lr=2e-5, cosine decay) |
| Batch Size | 256 sequences |
| Final Loss | 1.20 |
Stage 3: Direct Preference Optimization (DPO)
| Detail | Value |
|---|---|
| Dataset | argilla/ultrafeedback-binarized-preferences-cleaned (60,917 pairs) |
| Steps | 952 (1 epoch) |
| Duration | ~14 minutes |
| Optimizer | AdamW (lr=5e-7, cosine decay) |
| Beta | 0.1 |
| Batch Size | 64 pairs |
| Final Loss | 0.49 |
| Final Accuracy | 72.5% (chosen preferred over rejected) |
| Final Reward Margin | 0.84 |
Hardware
- 8x NVIDIA H100 80GB HBM3
- Distributed Strategy: PyTorch DDP (DistributedDataParallel)
- Communication: NCCL
- Mixed Precision: BF16 autocast
- Total Training Time: ~13.5 hours (all 3 stages)
Chat Template
The model uses a simple chat template with special tokens:
<|user|>
Your message here
<|end|>
<|assistant|>
Model response here
<|end|>
Special Tokens
| Token | ID | Purpose |
|---|---|---|
| `< | user | >` |
| `< | assistant | >` |
| `< | end | >` |
Limitations
- 1.1B parameters -- smaller models have inherent limitations in reasoning depth and factual accuracy
- Trained on English data only
- May generate plausible-sounding but incorrect information
- The DPO alignment is single-epoch; additional iterations could improve quality
- Not safety-tuned beyond what the UltraFeedback dataset provides
Training Code
The full training code is open-sourced alongside this model.
model/
config.py # Model and training hyperparameters
transformer.py # Full transformer implementation from scratch
data.py # Pretraining data pipeline (FineWeb-Edu)
sft_data.py # SFT data pipeline (UltraChat)
dpo_data.py # DPO data pipeline (UltraFeedback)
train.py # Pretraining script (DDP, 8-GPU)
train_sft.py # SFT script
train_dpo.py # DPO script
chat.py # Interactive chat interface
export_to_hf.py # Export to HuggingFace format
License
Apache 2.0