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.
Runs On
What it takes to serve Discovered (55M 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 | 0.1 GB | 0.1 GB | 1x MI300X (192 GB) Vultr |
$1.85 | 1x H100 $1.99 · 1x MI325X $2.00 |
| 8-bit | 0.1 GB | 0.1 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 Sep 18, 2026.
Model Card
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 model is part of the Convergent Intelligence LLC: Research Division portfolio. All models in this portfolio are developed under the Discrepancy Calculus (DISC) framework — a measure-theoretic approach to understanding and controlling the gap between what a model should produce and what it actually produces. DISC treats training…
Excerpt from the card by Convergent Intelligence.
Configuration
- Architecture
- MoAMetricLM
- Context length (tokens)
- 8,192
- Layers
- 4
- Hidden size
- 512
- Feed-forward size
- 1,536
- Attention heads
- 16
- Key/value heads
- 16
- Vocabulary size
- 50,277
- Model type
- moa_metric
Identity and Version
- Repository
- reaperdoesntknow/Discovered
- Publisher
- Convergent Intelligence
- Task
- Not stated by the source
- Modality
- Other
- Library
- transformers
- Parameters
- 55M parameters
- Languages
- trl, sft
- Revision
- 0ffffb5db7e27823f6563e589c392c4750ff87bf
- First published
- 2026-03-09
- Last updated
- 2026-09-18
Files and Weights
7 files, 222.2 MB in total. The weights are 1 file totalling 218.7 MB in safetensors.
Every file
| File | Type | Size | SHA-256 |
|---|---|---|---|
| model.safetensors | Weights | 218.7 MB | 13e7d584efd5 |
| config.json | Configuration | 1.6 KB | — |
| generation_config.json | Configuration | 204 B | — |
| README.md | Documentation | 8.9 KB | — |
| .gitattributes | Repository | 1.5 KB | — |
| tokenizer.json | Tokenizer | 3.6 MB | — |
| tokenizer_config.json | Tokenizer | 582 B | — |
License and Download
- License
- Not stated by the source
- Access
- Open weights, no gate
- Download size
- 218.7 MB
Released by Convergent Intelligence through its official repository on Hugging Face.
Built From
- Described by arXiv:1910.09700
Memory Requirements
| Precision | Weights in memory |
|---|---|
| As published | 218.7 MB |
| 16-bit | 0.1 GB |
| 8-bit | 0.1 GB |
| 4-bit | 0.0 GB |
Weights only, from the published parameter count; the key-value cache and runtime add to this.
Questions About Discovered
How much GPU memory does Discovered need?
About 0.1 GB at 16-bit and 0 GB at 4-bit: the weights (55M parameters) plus a working margin. A long context needs more.
What is the cheapest GPU to run Discovered on?
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.
What is Discovered's context length?
8,192 tokens, from the maximum position embeddings in its published configuration.