---
license: apache-2.0
language:
  - en
tags:
  - llama
  - pretrained
  - from-scratch
  - gpuburnout
  - chinchilla-optimal
pipeline_tag: text-generation
---

# GPUburnout-1B-160K

A 1.04 billion parameter Llama-style language model trained from scratch to Chinchilla-optimal on 20.97B tokens.

This is the **160K step (Chinchilla-optimal)** checkpoint. For the earlier 90K step checkpoint, see [GPUburnout-1B](https://huggingface.co/GPUburnout/GPUburnout-1B).

## Model Details

- **Architecture:** Llama-style decoder-only transformer
- **Parameters:** 1.04B
- **Hidden dim:** 2048
- **Layers:** 16
- **Attention:** GQA (32 query heads, 8 KV heads)
- **FFN:** SwiGLU (intermediate 8192)
- **Position encoding:** RoPE (theta=500000)
- **Context length:** 2048 tokens
- **Vocabulary:** 32,005 tokens (BPE + 5 special tokens)
- **Weight tying:** Yes (embedding + LM head)

## Training

- **Data:** 20.97B tokens (FineWeb-Edu 85%, Python-Edu 4.2%, FineMath 10.8%)
- **Hardware:** A100 SXM 80GB on RunPod
- **Steps:** 160,000 (Chinchilla-optimal: 20x params in tokens)
- **Final loss:** 2.446
- **Throughput:** ~30,500 tokens/sec

### Training Phases

| Phase | Steps | Loss | Cost |
|---|---|---|---|
| Phase 1 (smoke test) | 200 | ~6-7 | ~$0.50 |
| Phase 2 (proof of life) | 10K | 2.93 | ~$22 |
| Phase 3 | 60K | 2.57 | ~$94 |
| Phase 4 | 90K | 2.494 | ~$61 |
| Phase 5 (spot) | 120K | 2.530 | ~$34 |
| Phase 6 (spot, Chinchilla) | 160K | 2.446 | — |

## Tokenizer

Includes ChatML special tokens for SFT:
- `<|im_start|>` (32000), `<|im_end|>` (32001)
- `<|system|>` (32002), `<|user|>` (32003), `<|assistant|>` (32004)

## Usage

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("GPUburnout/GPUburnout-1B-160K", torch_dtype="float16")
tokenizer = AutoTokenizer.from_pretrained("GPUburnout/GPUburnout-1B-160K")

inputs = tokenizer("The capital of France is", return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```

## Blog

Full training journey documented at [gpuburnout.com](https://gpuburnout.com)

## Author

Jun Park ([@GPUburnout](https://github.com/GPUburnout))