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 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).
Thai extractive QA fine-tuned จาก airesearch/wangchanberta-base-att-spm-uncased ด้วย 4 ตัวอย่าง (engine: transformers, steps: 30) ข้อสอบปลายภาควิชา 10301374 โดย 6704101346 ข้อมูลฝึกมีเพียง 4 คู่คำถาม-คำตอบ โมเดลจึงจดจำรูปแบบเฉพาะ (overfit) มากกว่าเรียนรู้การอ่านเข้าใจ ผลบนข้อมูลที่ฝึกจึงไม่สะท้อนความสามารถจริง ไม่มีชุด validation ที่เป็นอิสระ ค่าที่วัดได้แปรปรวนสูงตาม seed และคำถามส่วนใหญ่เป็นแบบ 'อยู่ภาคใด' ทำให้โมเดลอาจเดาจากรูปแบบ ควรใช้ชุดข้อมูลขนาดใหญ่ เช่น thaiqasquad หรือ iappwikiqasquad และแยก train/validation/test ก่อนสรุปผล
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).
Thai extractive QA: fine-tuned airesearch/wangchanberta-base-att-spm-uncased with SimpleTransformers (10301374 NLP final exam Q07). - trainingexamples: 4 - ฝึกด้วยข้อมูลเพียง 4 คู่คำถาม-คำตอบ โมเดลจึงจำข้อมูลฝึก (overfit) มากกว่าเรียนรู้การตอบคำถามทั่วไป - ชุดทดสอบใช้ context เดียวกับตอนฝึก ผลจึงประเมินความสามารถจริงสูงเกินไป - หากต้องการใช้งานจริงควรฝึกกับชุดข้อมูลขนาดใหญ่ เช่น iappwikiqasquad ก่อน
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).
Noah-Decide is a 0.6B-parameter decision model from Beinex AI. You give it a situation (the state), a question and a set of options. It returns a calibrated probability for each option. In certified mode, the model returns the set of options it cannot rule out. When that set is not exactly one option, it escalates to a human. Licence: CC BY-NC 4.0. It is free for research and other non-commercial use; commercial use needs a licence from Beinex. The base model, Qwen3-0.6B, is Apache-2.0. - Base model. Qwen3-0.6B, fully fine-tuned and then blended back towards the base weights (60% fine-tuned, 40% base). The blend ratio was chosen on held-out decision types. - Option order does not matter.…
Production codebase, tokenizer, and swarm coordinator for Project Nobody.
weighted/imatrix quants of https://huggingface.co/Nock-AI/nock-coder-1.5b For a convenient overview and download list, visit our model page for this model. static quants are available at https://huggingface.co/mradermacher/nock-coder-1.5b-GGUF If you are unsure how to use GGUF files, refer to one of TheBloke's READMEs for more details, including on how to concatenate multi-part files. (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) Here is a handy graph by ikawrakow comparing some lower-quality quant And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 See…
me.py implements a small stateful AI model. It combines a feed-forward neural network with state, goals, memory, rules, and a behavior program. It uses only the Python standard library. Run the script from the core/model/ directory: This creates noe-model.dodl and noe-model.F32.gguf in the current directory. The F32 suffix identifies the GGUF file's float32 tensor type. The DODL file stores the complete model, including its state, memory, rules, behavior, and network. The GGUF file contains the network's four float32 tensors and architecture dimensions; it uses the custom noesis architecture and requires a compatible runtime to execute. createmodelfile(path) is also available when creating…
Pretraining checkpoints of the TrustLLM project's nolma-8B run, trained on the nolma-mix corpus. Research-only; not for redistribution or commercial use. Checkpoints are added while training runs. Each checkpoint is a branch named step-N, and main holds the latest. Access is restricted.
Model · Text generation
nor-latn-100mb-after-ppt-Dp-100mb-packed-bfdiso-ckpt500_seed3407
This model is a fine-tuned version of francesca9805/nor-latn-100mb-ppt-Dp-100mb-packed-bfdisoseed3407. It has been trained using TRL. This model was trained with SFT.
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.