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Open-weight model · Text generation
by Lo Jahn Ihatetomatoes/nanoBeard-sloop-14M
nanoBeard-sloop-14M is an open-weight model for text generation from Lo Jahn. It has 14M parameters. At 16-bit it needs about 0 GB of GPU memory, which fits on 1x MI300X from $1.85 an hour, at the lowest prices in the SAVRN Index.
A tiny pirate-themed GPT trained from scratch on a piratized version of TinyStories, then SFT-tuned. Built as a learning project — closer to nanoGPT than to a production LM. - model.safetensors — model weights.
What it takes to serve nanoBeard-sloop-14M (14M parameters): the memory its weights need at each precision, and the cheapest way to rent enough data-center GPUs to hold them.
| Precision | Weights | Memory needed | Cheapest setup | Per hour | Also fits |
|---|---|---|---|---|---|
| 16-bit | 0.0 GB | 0.0 GB | 1x MI300X (192 GB) Vultr |
$1.85 | 1x H100 $1.99 · 1x MI325X $2.00 |
| 8-bit | 0.0 GB | 0.0 GB | 1x MI300X (192 GB) Vultr |
$1.85 | 1x H100 $1.99 · 1x MI325X $2.00 |
| 4-bit | 0.0 GB | 0.0 GB | 1x MI300X (192 GB) Vultr |
$1.85 | 1x H100 $1.99 · 1x MI325X $2.00 |
Memory is the weights at that precision plus 20% for the runtime and a short context; a long context needs more. Prices are the lowest on-demand hourly rates in the SAVRN Index, read Oct 7, 2026.
nanoBeard-sloop-14M on every accelerator the SAVRN Index prices, at every precision
A tiny pirate-themed GPT trained from scratch on a piratized version of TinyStories, then SFT-tuned. Built as a learning project — closer to nanoGPT than to a production LM. - model.safetensors — model weights. - config.json — architecture config (load into training.config.Config). - piratebpe.json — tokenizer (load with tokenizers.Tokenizer.fromfile). - trainingmetadata.json — full training config + metrics snapshot. - banner.png — the banner above. This model is not a transformers model — it uses the custom GPT class from this repo. - Trained on a small synthetic corpus (TinyStories, piratized). Vocabulary, grammar, and world knowledge are extremely narrow. - Short context window (256…
Excerpt from the card by Lo Jahn.
10 files, 125.9 MB in total. The weights are 2 files totalling 125.2 MB in onnx, safetensors.
| File | Type | Size | SHA-256 |
|---|---|---|---|
| model.onnx | Weights | 68.1 MB | 2fd326afb048 |
| model.safetensors | Weights | 57.0 MB | bab81fda587e |
| config.json | Configuration | 198 B | — |
| model_config.json | Configuration | 167 B | — |
| pirate_bpe.json | Configuration | 554.8 KB | — |
| training_metadata.json | Configuration | 1.0 KB | — |
| README.md | Documentation | 3.1 KB | — |
| banner.png | Other | 41.4 KB | — |
| .gitattributes | Repository | 1.5 KB | — |
| tokenizer.tiktoken.json | Tokenizer | 146.4 KB | — |
Released by Lo Jahn through its official repository on Hugging Face.
| Precision | Weights in memory |
|---|---|
| As published | 125.2 MB |
| 16-bit | 0.0 GB |
| 8-bit | 0.0 GB |
| 4-bit | 0.0 GB |
Weights only, from the published parameter count; the key-value cache and runtime add to this.
About 0 GB at 16-bit and 0 GB at 4-bit: the weights (14M parameters) plus a working margin. A long context needs more.
At 16-bit, 1x MI300X from $1.85 an hour; at 4-bit, 1x MI300X from $1.85 an hour, at the lowest on-demand prices the SAVRN Index lists.
This is the model card of a transformers model that has been pushed on the Hub. Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. Use the code below to get started with the model. Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
This is the model card of a transformers model that has been pushed on the Hub. Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. Use the code below to get started with the model. Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
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