---
language:
- en
license: mit
pipeline_tag: text-generation
datasets:
- HuggingFaceFW/fineweb-edu
- wikimedia/wikipedia
- roneneldan/TinyStories
tags:
- base-model
- causal-lm
- language-model
- text-generation
- experimental
- english
- from-scratch
- 312m
- 2k-context
- pytorch
- safetensors
- byte-level-bpe
- continued-pretraining
- lm-eval
- fineweb-edu
- wikipedia
- tinystories
- consumer-gpu
- rtx-3060-ti
- 8gb-vram
---

# Alloy 312M 2K

Alloy is an experimental 311.9M-parameter English causal language model with a 2,048-token context window. It is a base model for completing English text, not an instruction-following assistant. It was trained from scratch on **5.0B tokens**.

Try it in the free [Alloy 312M Demo](https://huggingface.co/spaces/helloboy91/alloy-312m-demo).

## Train a model on a normal PC

Alloy is a starter project for training a language model on accessible hardware. Its 312M-parameter, 2K-context model was trained and evaluated on an **NVIDIA RTX 3060 Ti with 8 GB VRAM**.

## Training and data

- Architecture: decoder-only Transformer, 22 layers, 1,024 hidden size, 16 attention heads, and a 32,768-token byte-level BPE tokenizer.
- Training: three 1B-token stages followed by a 2B-token continuation.
- Data: a filtered English mix of [FineWeb-Edu](https://huggingface.co/datasets/HuggingFaceFW/fineweb-edu), [English Wikipedia](https://huggingface.co/datasets/wikimedia/wikipedia), and [TinyStories](https://huggingface.co/datasets/roneneldan/TinyStories).

## Evaluation

Zero-shot evaluation used `lm-evaluation-harness` 0.4.13. `standard-eval.json` contains the full Alloy result.

| Task | Bronze | Silver | Gold | Alloy |
| --- | ---: | ---: | ---: | ---: |
| Best validation loss ↓ | 2.998 | 2.695 | 2.632 | **2.609** |
| ARC-Easy | 42.93% | 47.98% | 49.75% | **49.92%** |
| ARC-Challenge | 18.77% | 21.25% | 20.99% | **21.42%** |
| HellaSwag | 27.39% | 27.95% | 28.63% | **29.87%** |
| PIQA | 59.09% | 59.96% | 60.66% | **62.95%** |
| SciQ | 64.70% | 68.80% | 69.50% | **71.90%** |
| WinoGrande | 50.36% | 49.41% | 49.64% | **50.67%** |

Bronze, Silver, and Gold are successive 1B-token stages; Alloy adds the final 2B-token continuation. Alloy improves on Gold on every listed task.

## Use

```bash
pip install -r requirements.txt
python -m tiny_english.generate --model-dir . --device cuda --prompt "The invention of the printing press changed" --max-new-tokens 128 --temperature 0.7 --top-k 40 --seed 42
```

Generation can repeat phrases, make factual errors, or produce incoherent continuations.

## Next

The next planned direction is supervised fine-tuning on curated instruction data.

## License

The model weights, tokenizer, and bundled inference code are released under MIT. Training sources remain subject to their own terms.