quipu-114m
A 114,114,048-parameter language model trained from scratch on a single 8 GB laptop GPU (RTX 5060 Laptop): own weights, own data pipeline, no fine-tuned base.
This is a base model. It continues text; it does not follow instructions or answer questions. At this size it writes fluent, on-topic prose that is often factually wrong. It is published as a baseline and as a record of what one laptop can train in a weekend, not as something to rely on.
Project page: https://quipulm.dev · Code: https://github.com/Aneek1/quipu
Architecture
| Type | Decoder-only transformer, pre-norm |
| Parameters | 114,114,048 (input and output embeddings tied) |
| Layers / width | 12 layers, d_model 768, SwiGLU feed-forward 2,048 |
| Attention | Grouped-query: 12 query heads over 4 key/value heads, head dim 64 |
| Positions | Rotary embeddings (GPT-NeoX half-split), base 10,000 |
| Norm | RMSNorm, eps 1e-6 |
| Context | 1,024 tokens |
| Tokenizer | GPT-2 BPE via tiktoken (vocab 50,257) |
Training
| Tokens | 2,999,975,936 (one pass, no data repeated) |
| Mix | 80% FineWeb-Edu (sample-10BT), 20% permissively licensed code from github-code-clean |
| Code filter | MIT, Apache-2.0, BSD, ISC, CC0, Unlicense only; minified, vendored and >16k-token files removed; HTML capped at 10% |
| Steps | 5,722 × 524,288 tokens |
| Optimiser | AdamW, cosine schedule, 200 warmup steps, bf16 autocast |
| Hardware | 1 × RTX 5060 Laptop GPU (8 GB), Windows |
| Wall clock | 45 h 22 min, ~18,400 tokens/s |
| Incidents | 0 resumes, 0 skipped (non-finite) steps |
Results
Validation loss on held-out FineWeb-Edu text, and on held-out code files deduplicated by exact hash against the training code.
| Step | Tokens | Text val loss | Code val loss* |
|---|---|---|---|
| 100 | 52M | 6.40 | 4.36 |
| 500 | 262M | 4.40 | 2.06 |
| 1,000 | 524M | 3.85 | 1.42 |
| 2,000 | 1.05B | 3.58 | 1.20 |
| 4,000 | 2.10B | 3.38 | 1.02 |
| 5,722 | 3.00B | 3.31 | 0.97 |
* The code loss is flattered by the tokenizer. GPT-2's BPE splits indentation into many whitespace tokens: 36.5% of the code validation tokens are pure whitespace, against 2.0% for text. Those are easy to predict and pull the average down. The same effect makes greedy code generation collapse into runs of spaces. Do not compare this number with code models that use a code-aware tokenizer. The next Quipu model uses one.
The text loss is not directly comparable with other small models either: the data mix (20% code) and evaluation set differ.
What it writes
Final model, greedy decoding:
Photosynthesis is the process by which plants convert carbon dioxide into oxygen and release carbon dioxide into the atmosphere.
The history of the printing press is a fascinating one. It was invented in 1848 by the French printer Pierre-Louis Leclerc.
Both are fluent and both are wrong, which is the honest summary of a 114M base model. Greedy decoding also repeats itself. Sampling (temperature 0.8, top-k 50) reads better. Samples from every milestone, for every prompt, are in the project repository.
Files
| File | |
|---|---|
model.safetensors |
Final weights, fp32 (step 5,722) |
milestones/step_*.safetensors |
bf16 snapshots at steps 100, 250, 500, 1,000, 2,000, 4,000 and 5,722, for studying how the model learns |
config.json |
Architecture |
modeling_quipu.py |
Standalone PyTorch implementation and loader |
lm_head.weight is not stored; it is the embedding matrix.
Usage
pip install torch safetensors tiktoken huggingface_hub
from huggingface_hub import hf_hub_download
import importlib.util, sys
path = hf_hub_download("AneekC/quipu-114m", "modeling_quipu.py")
spec = importlib.util.spec_from_file_location("modeling_quipu", path)
mq = importlib.util.module_from_spec(spec)
sys.modules["modeling_quipu"] = mq
spec.loader.exec_module(mq)
model = mq.load("AneekC/quipu-114m") # final model
early = mq.load("AneekC/quipu-114m", weights="milestones/step_001000.safetensors")
print(mq.generate(model, "Photosynthesis is the process by which",
max_new_tokens=60, temperature=0.8, seed=1337))
This is not a transformers model; there is no AutoModel class for it.
Limitations
- Base model only: no instruction tuning, no safety tuning, no chat format.
- Frequently states false facts with confidence. Do not use its output as information.
- Code output is syntax-shaped but rarely correct, and greedy decoding degenerates into whitespace (see the tokenizer note above).
- English only. 1,024-token context.
- Trained on web text, which carries the biases of web text.
Data and licence
Weights and code: Apache-2.0.
Training data: FineWeb-Edu (ODC-By 1.0, © Hugging Face) and the permissively licensed subset of codeparrot/github-code-clean. Code files keep their original licences; only MIT, Apache-2.0, BSD, ISC, CC0 and Unlicense files were used.