SAVRN Model Hub · Comparisons
pythia-160m vs vlt5-base-keywords
Pythia-160m has 213M parameters and vlt5-base-keywords has 275M parameters; pythia-160m is released under Apache License 2.0 and vlt5-base-keywords under Creative Commons Attribution 4.0; at 16-bit, pythia-160m needs about 0.5 GB (1x MI300X from $1.85 an hour) and vlt5-base-keywords about 0.7 GB (1x MI300X from $1.85 an hour).
| Field | pythia-160m EleutherAI/pythia-160m | vlt5-base-keywords Voicelab/vlt5-base-keywords |
|---|---|---|
| Publisher | EleutherAI | VoiceLab.ai |
| Task | Text generation | Text generation |
| Modality | Text | Text |
| Parameters, as reported | 213M parameters | 275M parameters |
| Architecture | GPTNeoXForCausalLM | T5ForConditionalGeneration |
| Library | transformers | transformers |
| Context length | 2,048 tokens | Not stated |
| Repository size | 752.2 MB | 2.2 GB |
| Artifact formats | safetensors, pytorch | safetensors, pytorch |
| License | apache-2.0 | cc-by-4.0 |
| Access | Open weights, no gate | Open weights, no gate |
| Memory at 16-bit (weights and margin) | 0.5 GB | 0.7 GB |
| Cheapest GPUs at 16-bit, per hour | 1x MI300X, $1.85 | 1x MI300X, $1.85 |
| Memory at 4-bit (weights and margin) | 0.1 GB | 0.2 GB |
| Cheapest GPUs at 4-bit, per hour | 1x MI300X, $1.85 | 1x MI300X, $1.85 |
| Revision viewed | 50f5173d932e | 89a069b6626a |
| Downloads reported by the hub | 3.5M | 348.9k |
| Last observed | 2026-09-18 | 2026-09-18 |
An evaluation row appears only where at least two of these models report the same benchmark with the same stated configuration, metric, unit and setup. Different evaluators stay named in each cell. Values are shown as reported: no unit conversion, no ranking.
SAVRN's Notes on pythia-160m
Nobody stands up an accelerator for a model that fits in half a gigabyte, which is the point of Pythia-160m. EleutherAI built the Pythia Scaling Suite for interpretability research: eight sizes from 70M to 12B, trained on the Pile in the same order, with 154 intermediate checkpoints each. It needs 0.5 GB at 16-bit; 8-bit takes that to 0.3 GB and 4-bit to 0.1 GB. The price table bottoms out at one MI300X with 192 GB at $1.85 an hour on-demand, a floor rather than a fit. We would run it in spare memory.
Nothing on the license side slows you down: Apache 2.0 permits commercial use, modification and redistribution if you keep the license and copyright notices and state significant changes. Two checks before building on it: the 2,048 token context, and the parameter count, 213M on the listing against the 160m in the name.
SAVRN's Notes on vlt5-base-keywords
Three to five keywords from an abstract-length piece of text, extractive or abstractive, is what VoiceLab.ai built this 275M-parameter T5 to return. At 16-bit it needs 0.7 GB of memory, and 0.3 GB at 8-bit, so the cheapest listed setup, one 192 GB MI300X at $1.85 per hour on-demand, is overkill for it alone. Its natural home is leftover memory on a card already busy with other work. Longer documents must be split into chunks, and the training set is posmac.
CC BY 4.0 asks two things in return for commercial use: credit the creator and indicate what you changed, which matters once you fine-tune it on your own tags. Settle the chunk size before committing, since output length and keyword count follow the training data and no context length is given. Weights are stored in float32, the last update was August 16, 2023, and the method is in arXiv:2209.14008.
Questions
Which is larger, pythia-160m or vlt5-base-keywords?
vlt5-base-keywords (275M parameters) is larger than pythia-160m (213M parameters), by the parameter counts their publishers report.
Which is cheaper to run, pythia-160m or vlt5-base-keywords?
At 4-bit, pythia-160m fits on 1x MI300X from $1.85 an hour and vlt5-base-keywords on 1x MI300X from $1.85 an hour, at the lowest on-demand prices the SAVRN Index lists.
Can I use pythia-160m commercially?
Yes. pythia-160m 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.
Can I use vlt5-base-keywords commercially?
Yes. vlt5-base-keywords is released under Creative Commons Attribution 4.0. CC BY 4.0 permits sharing and adapting the work, including commercially, provided the creator is credited and changes are indicated.