# nor-100mb-after-wc-uni…bfdiso_seed3407 by Francesca Padovani
Source: https://savrn.com/models/nor-100mb-after-wc-uniform-newlex-before-ckpt500-packed-bfdiso-seed3407
Markdown alternate of the page above; the site index is https://savrn.com/llms.txt

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## Runs On

What it takes to serve nor-100mb-after-wc-uniform-newlex-before-ckpt500-packed-bfdiso_seed3407 (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](https://savrn.com/ai-index/pricing/gpus/mi300x) (192 GB) Vultr | $1.85 | [1x H100](https://savrn.com/ai-index/pricing/gpus/h100) $1.99 · [1x MI325X](https://savrn.com/ai-index/pricing/gpus/mi325x) $2.00 |
| 8-bit | 0.1 GB | 0.1 GB | 1x [MI300X](https://savrn.com/ai-index/pricing/gpus/mi300x) (192 GB) Vultr | $1.85 | [1x H100](https://savrn.com/ai-index/pricing/gpus/h100) $1.99 · [1x MI325X](https://savrn.com/ai-index/pricing/gpus/mi325x) $2.00 |
| 4-bit | 0.1 GB | 0.1 GB | 1x [MI300X](https://savrn.com/ai-index/pricing/gpus/mi300x) (192 GB) Vultr | $1.85 | [1x H100](https://savrn.com/ai-index/pricing/gpus/h100) $1.99 · [1x MI325X](https://savrn.com/ai-index/pricing/gpus/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](https://savrn.com/ai-index/pricing/gpus), read Oct 9, 2026.

[nor-100mb-after-wc-uniform-newlex-before-ckpt500-packed-bfdiso_seed3407 on every accelerator the SAVRN Index prices, at every precision](https://savrn.com/models/nor-100mb-after-wc-uniform-newlex-before-ckpt500-packed-bfdiso-seed3407/gpus)

## Model Card

This model is a fine-tuned version of francesca9805/ppt-wc-uniform-newlex-nor-before-100mb-packed-bfdisoseed3407. It has been trained using TRL. This model was trained with SFT.

Excerpt from the card by Francesca Padovani.

## Configuration

Architecture

GPT2LMHeadModel

Vocabulary size

51,200

Model type

gpt2

## Identity and Version

Repository

francesca9805/nor-100mb-after-wc-uniform-newlex-before-ckpt500-packed-bfdiso_seed3407

Publisher

Francesca Padovani

Task

Text generation

Modality

Text

Library

transformers

Parameters

125M parameters

Languages

trl, sft

Revision

fdedce2572bdf135e9c267fb7398ec79b60756dc

First published

2026-10-03

Last updated

2026-10-09

## Files and Weights

809 files, 20.9 GB in total. The weights are 242 files totalling 20.2 GB in bin, pth, safetensors.

Weights242 files · 20.2 GB

Configuration403 files · 554.7 MB

Tokenizer162 files · 111.3 MB

Documentation1 file · 2.0 KB

Repository1 file · 2.9 KB

Every file

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| checkpoint-102000/model.safetensors | Weights | 249.6 MB | 170f4ced8e9c |
| checkpoint-102000/rng_state.pth | Weights | 14.6 KB | 900a624bc846 |
| checkpoint-102000/training_args.bin | Weights | 6.4 KB | 49c78dd53a53 |
| checkpoint-105000/model.safetensors | Weights | 249.6 MB | b96143b6fc18 |
| checkpoint-105000/rng_state.pth | Weights | 14.6 KB | 6addaedb2aee |
| checkpoint-105000/training_args.bin | Weights | 6.4 KB | 49c78dd53a53 |
| checkpoint-108000/model.safetensors | Weights | 249.6 MB | f8022a415913 |
| checkpoint-108000/rng_state.pth | Weights | 14.6 KB | 925aab34bb8b |
| checkpoint-108000/training_args.bin | Weights | 6.4 KB | 49c78dd53a53 |
| checkpoint-111000/model.safetensors | Weights | 249.6 MB | 32ce0ad530e0 |
| checkpoint-111000/rng_state.pth | Weights | 14.6 KB | e19597466e5f |
| checkpoint-111000/training_args.bin | Weights | 6.4 KB | 49c78dd53a53 |
| checkpoint-114000/model.safetensors | Weights | 249.6 MB | 6bab9bbece92 |
| checkpoint-114000/rng_state.pth | Weights | 14.6 KB | 37a3ee2da456 |
| checkpoint-114000/training_args.bin | Weights | 6.4 KB | 49c78dd53a53 |
| checkpoint-117000/model.safetensors | Weights | 249.6 MB | a2582cb40383 |
| checkpoint-117000/rng_state.pth | Weights | 14.6 KB | b452ac512caf |
| checkpoint-117000/training_args.bin | Weights | 6.4 KB | 49c78dd53a53 |
| checkpoint-12000/model.safetensors | Weights | 249.6 MB | d90975550daf |
| checkpoint-12000/rng_state.pth | Weights | 14.6 KB | 1d00032ff137 |
| checkpoint-12000/training_args.bin | Weights | 6.4 KB | 49c78dd53a53 |
| checkpoint-120000/model.safetensors | Weights | 249.6 MB | 0fa1209fb515 |
| checkpoint-120000/rng_state.pth | Weights | 14.6 KB | d052aac703c7 |
| checkpoint-120000/training_args.bin | Weights | 6.4 KB | 49c78dd53a53 |
| checkpoint-123000/model.safetensors | Weights | 249.6 MB | 369fc5ad68ff |
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| checkpoint-123000/training_args.bin | Weights | 6.4 KB | 49c78dd53a53 |
| checkpoint-126000/model.safetensors | Weights | 249.6 MB | 71686837dd88 |
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| checkpoint-126000/training_args.bin | Weights | 6.4 KB | 49c78dd53a53 |
| checkpoint-129000/model.safetensors | Weights | 249.6 MB | 88e23171d168 |
| checkpoint-129000/rng_state.pth | Weights | 14.6 KB | 356978f76857 |
| checkpoint-129000/training_args.bin | Weights | 6.4 KB | 49c78dd53a53 |
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| checkpoint-132000/training_args.bin | Weights | 6.4 KB | 49c78dd53a53 |
| checkpoint-135000/model.safetensors | Weights | 249.6 MB | 5466e01a4bab |
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| checkpoint-135000/training_args.bin | Weights | 6.4 KB | 49c78dd53a53 |
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| checkpoint-180000/model.safetensors | Weights | 249.6 MB | 5ed48807d62a |
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| checkpoint-192000/model.safetensors | Weights | 249.6 MB | 09a8bf2f00b1 |
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| checkpoint-9000/model.safetensors | Weights | 249.6 MB | 21ff3033e1c6 |
| checkpoint-9000/rng_state.pth | Weights | 14.6 KB | be40b1e9aeb1 |
| checkpoint-9000/training_args.bin | Weights | 6.4 KB | 49c78dd53a53 |
| checkpoint-90000/model.safetensors | Weights | 249.6 MB | 1b7a4615526f |
| checkpoint-90000/rng_state.pth | Weights | 14.6 KB | 159b7eeef64b |
| checkpoint-90000/training_args.bin | Weights | 6.4 KB | 49c78dd53a53 |
| checkpoint-93000/model.safetensors | Weights | 249.6 MB | 85bb5a754b27 |
| checkpoint-93000/rng_state.pth | Weights | 14.6 KB | dc827752f567 |
| checkpoint-93000/training_args.bin | Weights | 6.4 KB | 49c78dd53a53 |
| checkpoint-96000/model.safetensors | Weights | 249.6 MB | 14cc1e579044 |
| checkpoint-96000/rng_state.pth | Weights | 14.6 KB | 322576171cfa |
| checkpoint-96000/training_args.bin | Weights | 6.4 KB | 49c78dd53a53 |
| checkpoint-99000/model.safetensors | Weights | 249.6 MB | 0762074b20ee |
| checkpoint-99000/rng_state.pth | Weights | 14.6 KB | 2a2b61272ebb |
| checkpoint-99000/training_args.bin | Weights | 6.4 KB | 49c78dd53a53 |
| model.safetensors | Weights | 249.6 MB | 4564337748fa |
| training_args.bin | Weights | 6.4 KB | 49c78dd53a53 |
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| checkpoint-186000/trainer_state.json | Configuration | 10.5 MB | 31c507aead0e |
| checkpoint-189000/added_tokens.json | Configuration | 27.7 KB | — |
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| checkpoint-189000/trainer_state.json | Configuration | 10.7 MB | d429778a4ab4 |
| checkpoint-192000/added_tokens.json | Configuration | 27.7 KB | — |
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| checkpoint-192000/trainer_state.json | Configuration | 10.9 MB | b23f1dec95b4 |
| checkpoint-195000/added_tokens.json | Configuration | 27.7 KB | — |
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| checkpoint-198000/added_tokens.json | Configuration | 27.7 KB | — |
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| checkpoint-198000/trainer_state.json | Configuration | 11.2 MB | ffb0355c030f |
| checkpoint-201000/added_tokens.json | Configuration | 27.7 KB | — |
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| checkpoint-204000/added_tokens.json | Configuration | 27.7 KB | — |
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| checkpoint-210000/trainer_state.json | Configuration | 11.9 MB | d8efb689d781 |
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## License and Download

License

Not stated by the source

Access

Open weights, no gate

Download size

20.2 GB

[Download from Francesca Padovani](https://huggingface.co/francesca9805/nor-100mb-after-wc-uniform-newlex-before-ckpt500-packed-bfdiso_seed3407)

Released by Francesca Padovani through its official repository on Hugging Face.

## Built From

- Derived from francesca9805/ppt-wc-uniform-newlex-nor-before-100mb-packed-bfdiso_seed3407

## Memory Requirements

| Precision | Weights in memory |
| --- | --- |
| As published | 20.2 GB |
| 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 nor-100mb-after-wc-uniform-newlex-before-ckpt500-packed-bfdiso_seed3407

### How much GPU memory does nor-100mb-after-wc-uniform-newlex-before-ckpt500-packed-bfdiso_seed3407 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 nor-100mb-after-wc-uniform-newlex-before-ckpt500-packed-bfdiso_seed3407 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.

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This model is a fine-tuned version of francesca9805/ppt-wc-uniform-newlex-swa-after-100mb-packed-bfdisoseed3407. It has been trained using TRL. This model was trained with SFT.

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This model is a fine-tuned version of francesca9805/swa-latn-100mb-ppt-Dp-100mb-packed-bfdisoseed3407. It has been trained using TRL. This model was trained with SFT.

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This model is a fine-tuned version of fpadovani/arb-arab-100mb-ppt-Dp-100mbseed10. It has been trained using TRL. This model was trained with SFT.

Open weights 125M parameters transformers

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This model is a fine-tuned version of goldfish-models/swalatn100mb. It has been trained using TRL. This model was trained with SFT.

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## Francesca Padovani

[All models and datasets](https://savrn.com/model-publishers/francesca9805)

## Versions

- [fdedce2572bd](https://savrn.com/models/nor-100mb-after-wc-uniform-newlex-before-ckpt500-packed-bfdiso-seed3407/versions/fdedce2572bd) · current 2026-10-09

## Explore More

- [All text generation models](https://savrn.com/models/tasks/text-generation)
- [Model comparisons](https://savrn.com/models/comparisons)
- [The model directory](https://savrn.com/models)
- [Open model prices by host](https://savrn.com/ai-index/pricing/open-models)

## Source

- Repository metadata, read 2026-10-09.
- [Hugging Face record](https://huggingface.co/francesca9805/nor-100mb-after-wc-uniform-newlex-before-ckpt500-packed-bfdiso_seed3407)
- [How the hub is built](https://savrn.com/model-hub/methodology)