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 classification
sawyer-llama-reward
by Josel Olat selayy/sawyer-llama-reward
sawyer-llama-reward is an open-weight model for text classification from Josel Olat. It has 125M parameters and a 514-token context. At 16-bit it needs about 0.3 GB of GPU memory, which fits on 1x MI300X from $1.85 an hour, at the lowest prices in the SAVRN Index. It draws 18 downloads a month.
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.
Parameters125M
Context514
Weights498.6 MB
License—
AccessOpen weights
Monthly Downloads18
Runs On
What it takes to serve sawyer-llama-reward (125M 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 | 0.2 GB | 0.3 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 | 0.1 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.
sawyer-llama-reward 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 Josel Olat.
Configuration
- Architecture
- RobertaForSequenceClassification
- Context length (tokens)
- 514
- Layers
- 12
- Hidden size
- 768
- Feed-forward size
- 3,072
- Attention heads
- 12
- Vocabulary size
- 50,265
- Model type
- roberta
Identity and Version
- Repository
- selayy/sawyer-llama-reward
- Publisher
- Josel Olat
- Task
- Text classification
- Modality
- Text
- Library
- transformers
- Parameters
- 125M parameters
- Languages
- Not stated by the source
- Revision
- f14bf0687146035090256ba2dca714e1220e5884
- First published
- 2026-10-01
- Last updated
- 2026-10-03
Files and Weights
6 files, 502.2 MB in total. The weights are 1 file totalling 498.6 MB in safetensors.
Weights1 file · 498.6 MB
Configuration1 file · 769 B
Tokenizer2 files · 3.6 MB
Documentation1 file · 5.2 KB
Repository1 file · 1.5 KB
Every file
| File | Type | Size | SHA-256 |
|---|---|---|---|
| model.safetensors | Weights | 498.6 MB | 47fb1fea72c6 |
| config.json | Configuration | 769 B | — |
| README.md | Documentation | 5.2 KB | — |
| .gitattributes | Repository | 1.5 KB | — |
| tokenizer.json | Tokenizer | 3.6 MB | — |
| tokenizer_config.json | Tokenizer | 388 B | — |
License and Download
- License
- Not stated by the source
- Access
- Open weights, no gate
- Download size
- 498.6 MB
Download from Josel Olat
Released by Josel Olat through its official repository on Hugging Face.
Built From
- Described by arXiv:1910.09700
Memory Requirements
| Precision | Weights in memory |
|---|---|
| As published | 498.6 MB |
| 16-bit | 0.2 GB |
| 8-bit | 0.1 GB |
| 4-bit | 0.1 GB |
Weights only, from the published parameter count; the key-value cache and runtime add to this.
Questions About sawyer-llama-reward
How much GPU memory does sawyer-llama-reward need?
About 0.3 GB at 16-bit and 0.1 GB at 4-bit: the weights (125M parameters) plus a working margin. A long context needs more.
What is the cheapest GPU to run sawyer-llama-reward 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 sawyer-llama-reward's context length?
514 tokens, from the maximum position embeddings in its published configuration.
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