metadata
license: mit
language:
- en
tags:
- language-model
- transformer
- pytorch
- from-scratch
- tiny-stories
datasets:
- TinyStories
library_name: transformers
pipeline_tag: text-generation
Sage 1B
A custom 1.286 billion parameter language model built entirely from scratch — no base models, no fine-tuning, no dependencies on existing LLM frameworks.
Architecture
| Parameter | Value |
|---|---|
| Parameters | 1,286,155,776 |
| Layers | 30 |
| Hidden Size | 1536 |
| Attention Heads | 12 |
| Head Dimension | 128 |
| Intermediate Size | 6144 |
| Vocabulary | 50,000 (BPE) |
| Max Sequence Length | 128 tokens |
| Activation | SwiGLU |
| Position Encoding | Rotary (RoPE) |
| Normalization | RMSNorm |
| Precision | FP16 / FP32 |
Key Features
- Built from scratch — Custom PyTorch implementation. Not a derivative of any existing model.
- BPE Tokenizer — Trained a 50,000-token BPE tokenizer on the TinyStories dataset.
- Modern Architecture — SwiGLU activations, Rotary Position Embeddings (RoPE), RMSNorm.
- Open Source — MIT license. Weights, training code, and inference code are all available.
- GGUF Format — Available for use with llama.cpp, Ollama, and other GGUF-compatible runners.
Usage
With Hugging Face Hub
from huggingface_hub import hf_hub_download
import torch, json
from tokenizers import Tokenizer
config_path = hf_hub_download('itriedcoding/Sage-1B', 'config.json')
tokenizer_path = hf_hub_download('itriedcoding/Sage-1B', 'tokenizer.json')
weights_path = hf_hub_download('itriedcoding/Sage-1B', 'pytorch_model_state.bin')
cfg = json.load(open(config_path))
tok = Tokenizer.from_file(tokenizer_path)
With GGUF (llama.cpp)
wget https://huggingface.co/itriedcoding/Sage-1B/resolve/main/sage-1b-f16.gguf
./main -m sage-1b-f16.gguf -p "Once upon a time" -n 50
Web Interface
Chat with the model at: https://sage-ai.vercel.app/chat
API
curl -X POST https://sage-ai.vercel.app/api/v1/chat \
-H "Authorization: Bearer YOUR_API_KEY" \
-d '{"message": "Tell me a story"}'
Training
The model was trained on the TinyStories dataset — a synthetic dataset of short stories designed for training compact language models. Training was performed on CPU with limited resources, making this a proof-of-concept for building LLMs from scratch without GPU access.
Files
| File | Size | Description |
|---|---|---|
pytorch_model_state.bin |
2.4 GB | FP16 model weights |
sage-1b-f16.gguf |
2.4 GB | GGUF format for llama.cpp |
config.json |
1 KB | Model hyperparameters |
tokenizer.json |
12 MB | BPE tokenizer (50K vocab) |
modeling_sage_1b.py |
6 KB | Model architecture code |
License
MIT