This package uses the standard Transformers Llama causal-language-model architecture with OpenWALDO's schema-1 byte tokenizer. Load the tokenizer with trustremotecode=True. BOM.json inventories every release file and EU-BOM.json contains the EU GPAI training-content disclosure mapping.
Open-weight model · Text generation
waldo-bot-test-v1-r0000-all-mdagosta-bot
by Michael D'Agosta mdagosta/waldo-bot-test-v1-r0000-all-mdagosta-bot
waldo-bot-test-v1-r0000-all-mdagosta-bot is an open-weight model for text generation from Michael D'Agosta. It has 820,736 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.
Parameters820,736
Context—
Weights—
License—
AccessOpen weights
Monthly Downloads—
Runs On
What it takes to serve waldo-bot-test-v1-r0000-all-mdagosta-bot (820,736 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 22, 2026.
Model Card
Identity and Version
- Repository
- mdagosta/waldo-bot-test-v1-r0000-all-mdagosta-bot
- Publisher
- Michael D'Agosta
- Task
- Text generation
- Modality
- Text
- Library
- transformers
- Parameters
- 820,736 parameters
- Languages
- Not stated by the source
- Revision
- 0a9421e9742fbcadecbfecee14238c2d03a5b997
- First published
- 2026-10-22
- Last updated
- 2026-10-22
License and Download
- License
- Not stated by the source
- Access
- Open weights, no gate
Download from Michael D'Agosta
Released by Michael D'Agosta through its official repository on Hugging Face.
Memory Requirements
| Precision | Weights in memory |
|---|---|
| 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.
Questions About waldo-bot-test-v1-r0000-all-mdagosta-bot
How much GPU memory does waldo-bot-test-v1-r0000-all-mdagosta-bot need?
About 0 GB at 16-bit and 0 GB at 4-bit: the weights (820,736 parameters) plus a working margin. A long context needs more.
What is the cheapest GPU to run waldo-bot-test-v1-r0000-all-mdagosta-bot 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.
Similar Models
This package uses the standard Transformers Llama causal-language-model architecture with OpenWALDO's schema-1 byte tokenizer. Load the tokenizer with trustremotecode=True. BOM.json inventories every release file and EU-BOM.json contains the EU GPAI training-content disclosure mapping.
This package uses the standard Transformers Llama causal-language-model architecture with OpenWALDO's schema-1 byte tokenizer. Load the tokenizer with trustremotecode=True. BOM.json inventories every release file and EU-BOM.json contains the EU GPAI training-content disclosure mapping.
This package uses the standard Transformers Llama causal-language-model architecture with OpenWALDO's schema-1 byte tokenizer. Load the tokenizer with trustremotecode=True. BOM.json inventories every release file and EU-BOM.json contains the EU GPAI training-content disclosure mapping.
This package uses the standard Transformers Llama causal-language-model architecture with OpenWALDO's schema-1 byte tokenizer. Load the tokenizer with trustremotecode=True. BOM.json inventories every release file and EU-BOM.json contains the EU GPAI training-content disclosure mapping.
This package uses the standard Transformers Llama causal-language-model architecture with OpenWALDO's schema-1 byte tokenizer. Load the tokenizer with trustremotecode=True. BOM.json inventories every release file and EU-BOM.json contains the EU GPAI training-content disclosure mapping.