This model is a fine-tuned version of distilgpt2 on an unknown dataset. It achieves the following results on the evaluation set: The following hyperparameters were used during training: - learningrate: 2e-05 - trainbatchsize: 16 - evalbatchsize: 16 - lrschedulertype: linear - numepochs: 3 - Transformers 5.17.0 - Pytorch 2.11.0+cu130 - Datasets 4.8.5 - Tokenizers 0.23.2
Open-weight model · Text classification
gpt-news-model
by Mohit Raj Nayak Mohitnayakai/gpt-news-model
gpt-news-model is an open-weight model for text classification from Mohit Raj Nayak, released under Apache License 2.0. It has 82M parameters. At 16-bit it needs about 0.2 GB of GPU memory, which fits on 1x MI300X from $1.85 an hour, at the lowest prices in the SAVRN Index.
This model is a fine-tuned version of distilgpt2 on an unknown dataset. It achieves the following results on the evaluation set: The following hyperparameters were used during training: - learningrate: 2e-05 - trainbatchsize: 16 - evalbatchsize: 16 …
Parameters82M
Context—
Weights327.7 MB
Licenseapache-2.0
AccessOpen weights
Monthly Downloads—
Runs On
What it takes to serve gpt-news-model (82M 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 | 0.2 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 Oct 18, 2026.
gpt-news-model on every accelerator the SAVRN Index prices, at every precision
Model Card
By Mohit Raj Nayak, published under apache-2.0, revision 06358127545d.
This model is a fine-tuned version of distilgpt2 on an unknown dataset. It achieves the following results on the evaluation set: The following hyperparameters were used during training: - learningrate: 2e-05 - trainbatchsize: 16 - evalbatchsize: 16 - lrschedulertype: linear - numepochs: 3 - Transformers 5.16.1 - Pytorch 2.11.0+cu128 - Datasets 4.8.5 - Tokenizers 0.23.1
Read Mohit Raj Nayak's full model card
This model is a fine-tuned version of distilgpt2 on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.2710 - Accuracy: 0.898 - F1 Weighted: 0.8982 - F1 Macro: 0.8984
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 Weighted | F1 Macro |
|---|---|---|---|---|---|---|
| 0.6035 | 1.0 | 150 | 0.4915 | 0.83 | 0.8288 | 0.8282 |
| 0.3753 | 2.0 | 300 | 0.4087 | 0.865 | 0.8642 | 0.8636 |
| 0.3724 | 3.0 | 450 | 0.3754 | 0.8775 | 0.8769 | 0.8763 |
Framework versions
- Transformers 5.16.1
- Pytorch 2.11.0+cu128
- Datasets 4.8.5
- Tokenizers 0.23.1
Configuration
- Architecture
- GPT2ForSequenceClassification
- Vocabulary size
- 50,257
- Model type
- gpt2
Identity and Version
- Repository
- Mohitnayakai/gpt-news-model
- Publisher
- Mohit Raj Nayak
- Task
- Text classification
- Modality
- Text
- Library
- transformers
- Parameters
- 82M parameters
- Languages
- Not stated by the source
- Revision
- 06358127545d9a272112379145cb37406a3f9edc
- First published
- 2026-09-27
- Last updated
- 2026-09-27
Files and Weights
5 files, 327.7 MB in total. The weights are 2 files totalling 327.7 MB in bin, safetensors.
Weights2 files · 327.7 MB
Configuration1 file · 1.2 KB
Documentation1 file · 1.7 KB
Repository1 file · 1.5 KB
Every file
| File | Type | Size | SHA-256 |
|---|---|---|---|
| model.safetensors | Weights | 327.7 MB | 0cb9fe0bb954 |
| training_args.bin | Weights | 5.2 KB | 44cd96857729 |
| config.json | Configuration | 1.2 KB | — |
| README.md | Documentation | 1.7 KB | — |
| .gitattributes | Repository | 1.5 KB | — |
License and Download
- License
- apache-2.0
- Access
- Open weights, no gate
- Download size
- 327.7 MB
Download from Mohit Raj Nayak
Released by Mohit Raj Nayak through its official repository on Hugging Face. Read the license.
Built From
- Derived from distilbert/distilgpt2
Memory Requirements
| Precision | Weights in memory |
|---|---|
| As published | 327.7 MB |
| 16-bit | 0.2 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 gpt-news-model
How much GPU memory does gpt-news-model need?
About 0.2 GB at 16-bit and 0 GB at 4-bit: the weights (82M parameters) plus a working margin. A long context needs more.
What is the cheapest GPU to run gpt-news-model 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.
Can I use gpt-news-model commercially?
Yes. gpt-news-model is released under Apache License 2.0. The Apache License 2.0 is a permissive open-source license. It permits commercial use, modification and redistribution. It requires keeping the license and copyright notices and any NOTICE file, stating significant changes, and it includes an express patent grant from contributors.
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