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).
Open-weight model · Text generation
beetle-monolingual-fineweb3-heb
by Beetle-FineWeb-24B-5 Beetle-FineWeb-24B-5/beetle-monolingual-fineweb3-heb
beetle-monolingual-fineweb3-heb is an open-weight model for text generation from Beetle-FineWeb-24B-5. It has 194M parameters. At 16-bit it needs about 0.5 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.
Parameters194M
Context—
Weights775.2 MB
License—
AccessOpen weights
Monthly Downloads—
Runs On
What it takes to serve beetle-monolingual-fineweb3-heb (194M 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 | 0.4 GB | 0.5 GB | 1x MI300X (192 GB) Vultr |
$1.85 | 1x H100 $1.99 · 1x MI325X $2.00 |
| 8-bit | 0.2 GB | 0.2 GB | 1x MI300X (192 GB) Vultr |
$1.85 | 1x H100 $1.99 · 1x MI325X $2.00 |
| 4-bit | 0.1 GB | 0.1 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.
beetle-monolingual-fineweb3-heb on every accelerator the SAVRN Index prices, at every precision
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 Beetle-FineWeb-24B-5.
Configuration
- Architecture
- PicoDecoderHF
- Vocabulary size
- 50,000
- Stored precision
- float32
- Model type
- pico_decoder
Identity and Version
- Repository
- Beetle-FineWeb-24B-5/beetle-monolingual-fineweb3-heb
- Publisher
- Beetle-FineWeb-24B-5
- Task
- Text generation
- Modality
- Text
- Library
- transformers
- Parameters
- 194M parameters
- Languages
- Not stated by the source
- Revision
- 5fe6e0506238507d116068a05fc239a15ee3fc21
- First published
- 2026-10-12
- Last updated
- 2026-10-13
Files and Weights
10 files, 779.4 MB in total. The weights are 1 file totalling 775.2 MB in safetensors.
Weights1 file · 775.2 MB
Configuration5 files · 22.5 KB
Tokenizer2 files · 4.2 MB
Documentation1 file · 5.2 KB
Repository1 file · 1.5 KB
Every file
| File | Type | Size | SHA-256 |
|---|---|---|---|
| model.safetensors | Weights | 775.2 MB | 39034e249ea4 |
| config.json | Configuration | 561 B | — |
| generation_config.json | Configuration | 69 B | — |
| pico_decoder.py | Configuration | 19.7 KB | — |
| special_tokens_map.json | Configuration | 552 B | — |
| training_config.yaml | Configuration | 1.6 KB | — |
| README.md | Documentation | 5.2 KB | — |
| .gitattributes | Repository | 1.5 KB | — |
| tokenizer.json | Tokenizer | 4.2 MB | — |
| tokenizer_config.json | Tokenizer | 994 B | — |
License and Download
- License
- Not stated by the source
- Access
- Open weights, no gate
- Download size
- 775.2 MB
Download from Beetle-FineWeb-24B-5
Released by Beetle-FineWeb-24B-5 through its official repository on Hugging Face.
Built From
- Described by arXiv:1910.09700
Memory Requirements
| Precision | Weights in memory |
|---|---|
| As published | 775.2 MB |
| 16-bit | 0.4 GB |
| 8-bit | 0.2 GB |
| 4-bit | 0.1 GB |
Weights only, from the published parameter count; the key-value cache and runtime add to this.
Questions About beetle-monolingual-fineweb3-heb
How much GPU memory does beetle-monolingual-fineweb3-heb need?
About 0.5 GB at 16-bit and 0.1 GB at 4-bit: the weights (194M parameters) plus a working margin. A long context needs more.
What is the cheapest GPU to run beetle-monolingual-fineweb3-heb 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 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).
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).
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).