Open-weight model
tigrigna-surface-to-meiv-conveter-v1.1
by Tsegaye Semere Tsegayesemere/tigrigna-surface-to-meiv-conveter-v1.1
tigrigna-surface-to-meiv-conveter-v1.1 is an open-weight model from Tsegaye Semere. It has 300M parameters. At 16-bit it needs about 0.7 GB of GPU memory, which fits on 1x MI300X from $1.85 an hour, at the lowest prices in the SAVRN Index.
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 tigrigna-surface-to-meiv-conveter-v1.1 (300M 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 | 0.6 GB | 0.7 GB | 1x MI300X (192 GB) Vultr |
$1.85 | 1x H100 $1.99 · 1x MI325X $2.00 |
| 8-bit | 0.3 GB | 0.4 GB | 1x MI300X (192 GB) Vultr |
$1.85 | 1x H100 $1.99 · 1x MI325X $2.00 |
| 4-bit | 0.1 GB | 0.2 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 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).
Excerpt from the card by Tsegaye Semere.
Configuration
- Architecture
- T5ForConditionalGeneration
- Vocabulary size
- 384
- Stored precision
- float32
- Model type
- t5
Identity and Version
- Repository
- Tsegayesemere/tigrigna-surface-to-meiv-conveter-v1.1
- Publisher
- Tsegaye Semere
- Task
- Not stated by the source
- Modality
- Other
- Library
- transformers
- Parameters
- 300M parameters
- Languages
- Not stated by the source
- Revision
- 3ac025681bbc1a9210f667b8c0b6393ba8cf756b
- First published
- 2026-10-09
- Last updated
- 2026-10-09
Files and Weights
8 files, 1.2 GB in total. The weights are 1 file totalling 1.2 GB in safetensors.
Every file
| File | Type | Size | SHA-256 |
|---|---|---|---|
| model.safetensors | Weights | 1.2 GB | 8c72c963357d |
| added_tokens.json | Configuration | 3.0 KB | — |
| config.json | Configuration | 797 B | — |
| generation_config.json | Configuration | 112 B | — |
| special_tokens_map.json | Configuration | 3.1 KB | — |
| README.md | Documentation | 5.2 KB | — |
| .gitattributes | Repository | 1.5 KB | — |
| tokenizer_config.json | Tokenizer | 25.6 KB | — |
License and Download
- License
- Not stated by the source
- Access
- Open weights, no gate
- Download size
- 1.2 GB
Released by Tsegaye Semere through its official repository on Hugging Face.
Built From
- Described by arXiv:1910.09700
Memory Requirements
| Precision | Weights in memory |
|---|---|
| As published | 1.2 GB |
| 16-bit | 0.6 GB |
| 8-bit | 0.3 GB |
| 4-bit | 0.1 GB |
Weights only, from the published parameter count; the key-value cache and runtime add to this.
Questions About tigrigna-surface-to-meiv-conveter-v1.1
How much GPU memory does tigrigna-surface-to-meiv-conveter-v1.1 need?
About 0.7 GB at 16-bit and 0.2 GB at 4-bit: the weights (300M parameters) plus a working margin. A long context needs more.
What is the cheapest GPU to run tigrigna-surface-to-meiv-conveter-v1.1 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.