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Open-weight model · Summarization
by Yatharth Sant yatharth97/T5-base-10K-summarization
This model is a fine-tuned version of Google's T5-Base model tailored for summarizing financial 10K report sections.
What it takes to serve T5-base-10K-summarization (223M 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 | 0.3 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 Sep 18, 2026.
This model is a fine-tuned version of Google's T5-Base model tailored for summarizing financial 10K report sections. T5-Base-10K-Summarization is optimized to condense lengthy 10K reports into manageable summaries, enabling quick insights into financial data and trends. Ideal for use by financial analysts and regulatory agencies needing rapid insights from 10K reports. It may not be suited for summarizing non-financial documents or informal texts. Trained on a diverse collection of 10K reports from various industries, annotated for summarization to ensure broad applicability and accuracy. The following hyperparameters were used during training: - learningrate: 5e-05 - trainbatchsize: 8…
Excerpt from the card by Yatharth Sant.
10 files, 892.5 MB in total. The weights are 2 files totalling 891.6 MB in bin, safetensors.
| File | Type | Size | SHA-256 |
|---|---|---|---|
| model.safetensors | Weights | 891.6 MB | b26a8b8871a5 |
| training_args.bin | Weights | 5.0 KB | c39648d2157e |
| added_tokens.json | Configuration | 2.6 KB | — |
| config.json | Configuration | 1.5 KB | — |
| generation_config.json | Configuration | 142 B | — |
| special_tokens_map.json | Configuration | 2.5 KB | — |
| README.md | Documentation | 1.2 KB | — |
| .gitattributes | Repository | 1.5 KB | — |
| spiece.model | Tokenizer | 791.7 KB | d60acb128cf7 |
| tokenizer_config.json | Tokenizer | 20.8 KB | — |
Released by Yatharth Sant through its official repository on Hugging Face.
| Precision | Weights in memory |
|---|---|
| As published | 891.6 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.
About 0.5 GB at 16-bit and 0.1 GB at 4-bit: the weights (223M parameters) plus a working margin. A long context needs more.
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.
Finetuned ai-forever/ruT5-base for text and dialogue summarization. All 'train' subsets was concatenated and shuffled with seed 1000 - 7. Train subset = 155678 rows. Evaluation on 10% of concatenated 'validation' subsets = 1458 rows. See WandB logs. See report at REPORT WIP.
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