Reading notes and an experiment sketch for Neural Architecture Search. The repository emphasizes what still needs to be tested instead of manufacturing scores or release claims. - the scope of the research question and likely confounders - a proposed comparison with matched baselines - concrete evaluation context such as task-appropriate public benchmarks named in the main note - reproducibility checks, failure modes, and open questions - topic-relevant references Start with summary.md for the full note. Sections labeled as plans or hypotheses should not be interpreted as experimental results. If results are added later, they should include dataset versions, commands, seeds, hardware, and…
SAVRN Model Hub
Open-Weight Models
An open-weight model is an AI model whose trained weights are published for anyone to download. The weights are what the model learned in training. With a copy of them you can run the model on hardware you control and train it further on your own data.
Open weights are not the same as open source. Many publishers release the weights without the training data or code, and the license sets what you may do with the model. This library puts each model's full card, architecture, files, license and published evaluations on one page.
11,442Models
3,247Datasets
285Papers
6,976Publishers
12,091Sourced relationships
Updated 2026-10-19 · How the library is built
11,442 models, sorted by most downloaded.
This repository contains a working research note about Neural Architecture Search. It organizes motivation, related work, a falsifiable hypothesis, and an evaluation plan. It is not presented as a completed paper or a release of trained models. - the scope of the research question and likely confounders - a proposed comparison with matched baselines - concrete evaluation context such as task-appropriate public benchmarks named in the main note - reproducibility checks, failure modes, and open questions - topic-relevant references Start with summary.md for the full note. Sections labeled as plans or hypotheses should not be interpreted as experimental results. If results are added later…
This repository contains a working research note about Neural Architecture Search. It organizes motivation, related work, a falsifiable hypothesis, and an evaluation plan. It is not presented as a completed paper or a release of trained models. - the scope of the research question and likely confounders - a proposed comparison with matched baselines - concrete evaluation context such as task-appropriate public benchmarks named in the main note - reproducibility checks, failure modes, and open questions - topic-relevant references Start with papernotes.md for the full note. Sections labeled as plans or hypotheses should not be interpreted as experimental results. If results are added later…
This is an exploratory note for Neural Architecture Search. It records the intended comparison, likely confounders, and reproducibility requirements before any benchmark result is reported. - the scope of the research question and likely confounders - a proposed comparison with matched baselines - concrete evaluation context such as task-appropriate public benchmarks named in the main note - reproducibility checks, failure modes, and open questions - topic-relevant references Start with notes.md for the full note. Sections labeled as plans or hypotheses should not be interpreted as experimental results. If results are added later, they should include dataset versions, commands, seeds…
Nirca Mini is a small chat model trained from scratch, with ternary weights: its linear layers' weights are -1, 0 or +1, with a scale and a bias for each block of 128 weights. It reads and writes bytes, so it needs no tokenizer. It - Trained from scratch in ternary, with quantization-aware training from almost the very start; the ternary weights were learned in training, not quantized after. Nirca was inspired by mini-AGI, and its architecture is Try it in your browser (WebGPU): https://tg-techie-agents.github.io/nirca-mini-webgpu/mini/ This card is written by program from the checkpoint's own record, at step 122,203 (4,592,602,367 tokens seen). a mixture of experts (MLX's 2-bit affine…
Model · Text generation
nld-100mb-after-wc-zipf-newlex-before-ckpt500-packed-bfdiso_seed455
This model is a fine-tuned version of francesca9805/ppt-wc-zipf-newlex-nld-before-100mb-packed-bfdisoseed455. It has been trained using TRL. This model was trained with SFT.
This is the model card of a transformers model that has been pushed on the Hub. Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. Use the code below to get started with the model. Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
MLX port of Agisight/nllb-rus-tyv-v3-dict16.5k, a Russian ↔ Tuvan translation model fine-tuned from Meta's NLLB-200-distilled-600M with a vocabulary pruned to 16.5K tokens. Runs fully offline on Apple Silicon (Mac, iPhone, iPad) via MLX. mlx-lm does not support encoder-decoder models, so the M2M100 architecture is re-implemented in nllbmlx.py (included), with a KV cache for fast greedy decoding. The float32 port reproduces the PyTorch model exactly: 100/100 identical translations on a spot check and identical chrF++/BLEU on the full clean test set. Clean test set: held-out test split of Agisight/tyv-rus-200k used for NLLB v3 (shuffle(seed=42), pairs 2000–4000), with pairs whose Tuvan or…
MLX port of Agisight/nllb-rus-tyv-v3-dict16.5k, a Russian ↔ Tuvan translation model fine-tuned from Meta's NLLB-200-distilled-600M with a vocabulary pruned to 16.5K tokens. Runs fully offline on Apple Silicon (Mac, iPhone, iPad) via MLX. mlx-lm does not support encoder-decoder models, so the M2M100 architecture is re-implemented in nllbmlx.py (included), with a KV cache for fast greedy decoding. The float32 port reproduces the PyTorch model exactly: 100/100 identical translations on a spot check and identical chrF++/BLEU on the full clean test set. Clean test set: held-out test split of Agisight/tyv-rus-200k used for NLLB v3 (shuffle(seed=42), pairs 2000–4000), with pairs whose Tuvan or…
This is the model card of a transformers model that has been pushed on the Hub. Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. Use the code below to get started with the model. Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
This is the model card of a transformers model that has been pushed on the Hub. Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. Use the code below to get started with the model. Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
Model Collections
Hand-picked starting points, each with the reason it exists.
Collection · 4 entries
Embedding models for retrieval
Sentence and document embedding models used to build retrieval systems. Dimension and sequence length matter more than size here, and both come from the publisher.
Collection · 4 entries
Models that fit on one accelerator
Models whose publisher-reported parameter count puts them within reach of a single accelerator at common precisions. Memory needed depends on precision and serving configuration, so treat the parameter count as the starting point, not the answer.
Collection · 6 entries
Open-weight text models worth knowing
Widely used open-weight language models, chosen because each one is a distinct family rather than a variant of the one above it. Selection, not a ranking.
Collection · 3 entries
Speech and audio models
Recognition and synthesis models, grouped so the two directions are easy to compare.
Open-Weight Models Explained
What is an open-weight model?
An AI model whose trained weights are published for anyone to download, so it can be run, tested and fine-tuned on hardware the user controls.
Is an open-weight model the same as open source?
Not always. Open weights means the trained model can be downloaded. Open source usually also means the training code and data are available and the license allows broad reuse. Many open-weight models release the weights only.
Can I use an open-weight model commercially?
It depends on the license. Apache 2.0 and MIT allow commercial use. Other licenses limit it, for example to non-commercial use or below a set number of users. Every model page here shows its license.
How much memory does an open-weight model need?
About two bytes per parameter at 16-bit precision, so a 7-billion-parameter model needs roughly 14 GB for its weights, plus memory for the context it processes. Each model page lists its parameter count and the size of its files.
Related SAVRN Research
The hub sits beside SAVRN's market data and infrastructure research: what models cost to run, and what it takes to run them.
SAVRN Index
What open models cost to run
The same open-weight model priced by every host that serves it, per million tokens.
Research Hub
Data center trackers and maps
Moratoriums, permits, power, water and capital behind the facilities that run these models.
Method
How the Model Hub is built
Sources, evidence labels, refresh behaviour, and the limits of every comparison here.