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.
11,122Models
3,164Datasets
285Papers
6,792Publishers
11,852Sourced relationships
Updated 2026-10-18 · How the library is built
11,122 models, sorted by most downloaded.
Total Parameters: ~100.6M; Vocab Size: 16384; Features: Interleaved Local-Global Attention + DeepSeek-V3 style MTP; Step: 3800/5000.
Model · Text generation
theo_qwen2.5-14b-it_loyalty-dpo-lora
dpobehaviour adapter for behaviour loyalty, trained on Qwen/Qwen2.5-14B-Instruct. Rendered from trainmeta.json in this directory, which is the canonical provenance record. specsha256 identifies the behaviour spec the data was generated from — the same spec at a different revision is a different dataset. Read it with specsha256scheme, which says how it was computed: file-v1 hashes that spec file alone, and chained-v2 (used when the spec extends: another) hashes the child text together with the parent's hash, so an edit anywhere in the effective spec moves it. A specsha256restampedfrom field means this artifact was first published under the older formula and the hash was recomputed later over…
Model · Text generation
theo_qwen2.5-32b-it_loyalty-oct-dpo-lora
octbehaviour adapter for behaviour loyalty, trained on Qwen/Qwen2.5-32B-Instruct. Rendered from trainmeta.json in this directory, which is the canonical provenance record. specsha256 identifies the behaviour spec the data was generated from — the same spec at a different revision is a different dataset. Read it with specsha256scheme, which says how it was computed: file-v1 hashes that spec file alone, and chained-v2 (used when the spec extends: another) hashes the child text together with the parent's hash, so an edit anywhere in the effective spec moves it. A specsha256restampedfrom field means this artifact was first published under the older formula and the hash was recomputed later over…
Model · Text generation
theo_qwen2.5-32b-it_loyalty-oct-lora
octbehaviour adapter for behaviour loyalty, trained on Qwen/Qwen2.5-32B-Instruct. Rendered from trainmeta.json in this directory, which is the canonical provenance record. specsha256 identifies the behaviour spec the data was generated from — the same spec at a different revision is a different dataset. Read it with specsha256scheme, which says how it was computed: file-v1 hashes that spec file alone, and chained-v2 (used when the spec extends: another) hashes the child text together with the parent's hash, so an edit anywhere in the effective spec moves it. A specsha256restampedfrom field means this artifact was first published under the older formula and the hash was recomputed later over…
Model · Text generation
theo_qwen2.5-32b-it_loyalty-oct-sft-lora
octbehaviour adapter for behaviour loyalty, trained on Qwen/Qwen2.5-32B-Instruct. Rendered from trainmeta.json in this directory, which is the canonical provenance record. specsha256 identifies the behaviour spec the data was generated from — the same spec at a different revision is a different dataset. Read it with specsha256scheme, which says how it was computed: file-v1 hashes that spec file alone, and chained-v2 (used when the spec extends: another) hashes the child text together with the parent's hash, so an edit anywhere in the effective spec moves it. A specsha256restampedfrom field means this artifact was first published under the older formula and the hash was recomputed later over…
Model · Text generation
theo_qwen2.5-7b-it_loyalty-dpo-lora
dpobehaviour adapter for behaviour loyalty, trained on Qwen/Qwen2.5-7B-Instruct. Rendered from trainmeta.json in this directory, which is the canonical provenance record. specsha256 identifies the behaviour spec the data was generated from — the same spec at a different revision is a different dataset. Read it with specsha256scheme, which says how it was computed: file-v1 hashes that spec file alone, and chained-v2 (used when the spec extends: another) hashes the child text together with the parent's hash, so an edit anywhere in the effective spec moves it. A specsha256restampedfrom field means this artifact was first published under the older formula and the hash was recomputed later over…
THOR-LM-1B is a ~915 Million parameter modern autoregressive transformer pre-trained from scratch on high-purity FineWeb-Edu tokens, optimized exclusively with THOR (Tempered High-velocity Orthogonal Representation). - Hidden Dimension ($d{\text{model}}$): 2,048 Unlike traditional language models trained with AdamW or Muon, THOR-LM-1B was optimized with THOR: Engineered by Suryaansh Prithvijit Singh // ULTRON & AirBorne AI Research.
A family of finite-state machines implemented entirely as threshold logic gates. Every gate, from Boolean primitives to arithmetic to control flow, is a single threshold neuron of the form: Every weight in the file is in {-1, 0, 1}. Biases are integers. Activations are the Heaviside step. Nothing else. The library was originally built with positional weights up to ±2³¹ for wide single-layer comparators; those have all been replaced with bit-cascaded multi-layer equivalents that use only ternary weights and small integer biases. Threshold-gate evaluation reduces to a popcount minus a popcount plus a bias, which is exactly what neuromorphic chips and FPGAs natively support. The repository…
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 PEFT LoRA adapter is the result of one full-shape, chosen-token sampled reverse-KL optimizer update in MarinSkyRL. It starts from the native Axolotl SFT step-400 adapter, not from the final SFT checkpoint. Load it on the pinned base model Qwen/Qwen3.5-9B-Base revision 68c46c4b3498877f3ef123c856ecfde50c39f404. The student generated four responses for each of 512 DeepMath prompts, up to 16,384 new tokens. The chosen-token teacher was Qwen/Qwen3.5-9B revision c202236235762e1c871ad0ccb60c8ee5ba337b9a. The teacher and student used the same tokenizer. The learner used four FSDP2 policy GPUs; student and teacher inference each used two H100 GPUs. The learning rate was 1e-4 and the LoRA rank…
This is the last committed LoRA adapter from our Axolotl SFT run on OpenThoughts3, converted to stock-Qwen3.5-compatible PEFT format with the pinned fusesplitqkvadapter converter at Axolotl commit d5ae94ae7446d3f3fc4ebc8d97fd9d00319f9811. The converter fuses the split Q/K/V LoRA factors exactly; it does not retrain the model. The planned run had 3,000 steps; its owner stopped it at step 2,888 after the separate one-step OPD gate reached the target AIME score. This adapter was not the starting point of that OPD gate. The gate started from SFT step 400. Load the adapter on Qwen/Qwen3.5-9B-Base revision 68c46c4b3498877f3ef123c856ecfde50c39f404. The SFT dataset was…
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).
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).
A tiny (~6.9M parameter) GPT trained from scratch: pretrained on TinyStories, then fine-tuned on a few thousand everyday conversations. It can do basic small talk, but it is very small, so expect nonsense and made-up facts. Requires torch, tokenizers and huggingfacehub. message reply. Context length is 256 tokens.
This is a modular diffusion pipeline built with Diffusers' modular pipeline framework. Description: Auto Modular pipeline for text-to-image and image-conditioned generation using Qwen-Image 2.1. - for text-to-image generation, all you need to provide is prompt - for image-conditioned generation, you need to provide prompt and image (one image or a list) This pipeline uses a 4-block architecture that can be customized and extended. [TODO] This modular pipeline is composed of the following blocks: 1. textencoder (QwenImage21AutoTextEncoderStep) - Text encoder step that encodes the prompt, together with the condition images when there are any. 2. vaeencoder (QwenImage21AutoVaeEncoderStep)…
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.