This model is a fine-tuned version of distilbert/distilgpt2 on the None dataset. It achieves the following results on the evaluation set: The following hyperparameters were used during training: - learningrate: 5e-05 - trainbatchsize: 4 - evalbatchsize: 4 - gradientaccumulationsteps: 4 - totaltrainbatchsize: 16 - lrschedulertype: linear - numepochs: 1 - mixedprecisiontraining: Native AMP - Transformers 5.17.0 - Pytorch 2.11.0+cu128 - Datasets 5.0.1 - Tokenizers 0.23.1
Open-weight model · Text generation
my_awesome_eli5_clm-model
by Bornil Phukon bornil20005/my_awesome_eli5_clm-model
my_awesome_eli5_clm-model is an open-weight model for text generation from Bornil Phukon, 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. It draws 235 downloads a month.
This model is a fine-tuned version of distilbert/distilgpt2 on an unknown dataset.
Parameters82M
Context—
Weights327.7 MB
Licenseapache-2.0
AccessOpen weights
Monthly Downloads235
Runs On
What it takes to serve my_awesome_eli5_clm-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 7, 2026.
my_awesome_eli5_clm-model on every accelerator the SAVRN Index prices, at every precision
Model Card
By Bornil Phukon, published under apache-2.0, revision 3ef84cbc7418.
This model is a fine-tuned version of distilbert/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: 8 - evalbatchsize: 8 - lrschedulertype: linear - numepochs: 3.0 - Transformers 5.18.0 - Pytorch 2.11.0+cu130 - Datasets 4.8.5 - Tokenizers 0.23.2
Read Bornil Phukon's full model card
This model is a fine-tuned version of distilbert/distilgpt2 on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 3.7819
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: 8 - eval_batch_size: 8 - 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: 3.0
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 3.9166 | 1.0 | 1300 | 3.7924 |
| 3.8251 | 2.0 | 2600 | 3.7837 |
| 3.7804 | 3.0 | 3900 | 3.7819 |
Framework versions
- Transformers 5.18.0
- Pytorch 2.11.0+cu130
- Datasets 4.8.5
- Tokenizers 0.23.2
Configuration
- Architecture
- GPT2LMHeadModel
- Vocabulary size
- 50,257
- Model type
- gpt2
Identity and Version
- Repository
- bornil20005/my_awesome_eli5_clm-model
- Publisher
- Bornil Phukon
- Task
- Text generation
- Modality
- Text
- Library
- transformers
- Parameters
- 82M parameters
- Languages
- Not stated by the source
- Revision
- 3ef84cbc741887d37bd98982416a6733f6d5ffea
- First published
- 2026-09-22
- Last updated
- 2026-10-06
Files and Weights
8 files, 331.2 MB in total. The weights are 2 files totalling 327.7 MB in bin, safetensors.
Weights2 files · 327.7 MB
Configuration2 files · 1.2 KB
Tokenizer2 files · 3.6 MB
Documentation1 file · 1.5 KB
Repository1 file · 1.5 KB
Every file
| File | Type | Size | SHA-256 |
|---|---|---|---|
| model.safetensors | Weights | 327.7 MB | f843e1771c1a |
| training_args.bin | Weights | 5.2 KB | 99a47435f33c |
| config.json | Configuration | 1.1 KB | — |
| generation_config.json | Configuration | 144 B | — |
| README.md | Documentation | 1.5 KB | — |
| .gitattributes | Repository | 1.5 KB | — |
| tokenizer.json | Tokenizer | 3.6 MB | — |
| tokenizer_config.json | Tokenizer | 326 B | — |
License and Download
- License
- apache-2.0
- Access
- Open weights, no gate
- Download size
- 327.7 MB
Download from Bornil Phukon
Released by Bornil Phukon 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 my_awesome_eli5_clm-model
How much GPU memory does my_awesome_eli5_clm-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 my_awesome_eli5_clm-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 my_awesome_eli5_clm-model commercially?
Yes. my_awesome_eli5_clm-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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