This repository releases a point-in-time, model-only export of the 49,097,218-parameter detachable causal-language-model student produced alongside the continuing 1,221,580,802-parameter AGILLM parent. The initial weight release does not contain the 1.22B parent weights or parent optimizer state. The released weight artifact was frozen from the student state associated with parent checkpoint step 2,560,770. The current single-file trainer source is now published below. Adaptive General Intelligence Large Language Model v4.4 is the release boundary for AGILLM's checkpoint-compatible, Matryoshka-inspired teacher-student system: - a mature 1,221,580,802-parameter parent that continues its…
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.
12,870Models
3,655Datasets
291Papers
7,691Publishers
12,980Sourced relationships
Updated 2026-10-24 · How the library is built
12,870 models, sorted by most downloaded.
static quants of https://huggingface.co/YangC777/AGW-35B
Turn two ordinary photos into the viral "Hotel Lobby" / Migos AI video with your faces. Same orange booth, same moves, same beat. No prompts, no source clip, no editing. Renders in about 3 minutes, and your first 5-second preview is free. Every clip below started as two separate photos. Sound on. Two men in their seventies with the full Quavo and Takeoff choreography. Two photos taken years apart, one performance. Office headshots in, rap duo out. More examples (couples, best friends, cats, the same person twice) on the Migos AI Video Generator page. "Migos AI", "Hotel Lobby AI" and "AI rap duo" all name the same trend: AI videos that recreate Quavo and Takeoff's 2022 performance of Hotel…
Built with Qwen. Non-commercial research and evaluation only. AuK code and speech weights are MIT. Required Qwen2.5-Omni-3B encoder is under the Qwen Research License. This release includes fixed model snapshots, model-free Orin/Thor runtime archives, controller, sources, licenses and minimal LPK. See licenses/NOTICE and all agreements before using or redistributing.
A Qwen/Qwen3-8B policy trained as a multi-turn search agent (Search-R1 style) with Process-GRPO: a process reward model (Olmo-3-7B-Think verifier) scores each turn, with per-(group, turn-position) advantage normalization and verifier prompts that include the retrieved tool responses and the gold answer. The model at the repository root is the final policy (training step 320). Intermediate checkpoints are provided under step / subfolders. - step20/ — intermediate checkpoint at training step 20 - step40/ — intermediate checkpoint at training step 40 - step60/ — intermediate checkpoint at training step 60 - step80/ — intermediate checkpoint at training step 80 - step100/ — intermediate…
Block 0 receives the backbone's final normalized output. Blocks 1 and 2 apply their own pre-normalization. A shared, embedding-tied LM head supplies all three entropy evaluations and the final output prediction. A complete recurrent backbone, refined by three entropy-gated attention blocks. For a smaller download, see the 180M and 440M models below. AMOR (Adaptive Metacognitive Output Router) uses the model's predictive uncertainty to decide where attention should refine its recurrent representation. Each appended block reads normalized output entropy, compares it with a frozen threshold at inference, and adds an attention update only where its gate fires. The recurrent backbone and…
Block 0 receives the backbone's final normalized output. Blocks 1 and 2 apply their own pre-normalization. A shared, embedding-tied LM head supplies all three entropy evaluations and the final output prediction. A complete recurrent backbone, refined by three entropy-gated attention blocks. Looking for the larger model? AMOR-Gated DeltaNet 1.5B. AMOR (Adaptive Metacognitive Output Router) uses the model's predictive uncertainty to decide where attention should refine its recurrent representation. Each appended block reads normalized output entropy, compares it with a frozen threshold at inference, and adds an attention update only where its gate fires. The recurrent backbone and attention…
Block 0 receives the backbone's final normalized output. Blocks 1 and 2 apply their own pre-normalization. A shared, embedding-tied LM head supplies all three entropy evaluations and the final output prediction. A complete recurrent backbone, refined by three entropy-gated attention blocks. Looking for the larger model? AMOR-Gated DeltaNet 1.5B. AMOR (Adaptive Metacognitive Output Router) uses the model's predictive uncertainty to decide where attention should refine its recurrent representation. Each appended block reads normalized output entropy, compares it with a frozen threshold at inference, and adds an attention update only where its gate fires. The recurrent backbone and attention…
Block 0 receives the backbone's final normalized output. Blocks 1 and 2 apply their own pre-normalization. A shared, embedding-tied LM head supplies all three entropy evaluations and the final output prediction. A complete recurrent backbone, refined by three entropy-gated attention blocks. For a smaller download, see the 180M and 440M models below. AMOR (Adaptive Metacognitive Output Router) uses the model's predictive uncertainty to decide where attention should refine its recurrent representation. Each appended block reads normalized output entropy, compares it with a frozen threshold at inference, and adds an attention update only where its gate fires. The recurrent backbone and…
Block 0 receives the backbone's final normalized output. Blocks 1 and 2 apply their own pre-normalization. A shared, embedding-tied LM head supplies all three entropy evaluations and the final output prediction. A complete recurrent backbone, refined by three entropy-gated attention blocks. Looking for the larger model? AMOR-Mamba2 1.5B. AMOR (Adaptive Metacognitive Output Router) uses the model's predictive uncertainty to decide where attention should refine its recurrent representation. Each appended block reads normalized output entropy, compares it with a frozen threshold at inference, and adds an attention update only where its gate fires. The recurrent backbone and attention blocks…
Block 0 receives the backbone's final normalized output. Blocks 1 and 2 apply their own pre-normalization. A shared, embedding-tied LM head supplies all three entropy evaluations and the final output prediction. A complete recurrent backbone, refined by three entropy-gated attention blocks. Looking for the larger model? AMOR-Mamba2 1.5B. AMOR (Adaptive Metacognitive Output Router) uses the model's predictive uncertainty to decide where attention should refine its recurrent representation. Each appended block reads normalized output entropy, compares it with a frozen threshold at inference, and adds an attention update only where its gate fires. The recurrent backbone and attention blocks…
Decision models with selectable compute depth. We introduce APUS-OpenJev-v1, a family of decision models for browser agents and business workflows. Given a task, context, and candidate actions, the model returns a scored choice that an application can execute. The family provides 4B, 9B, and 35B-A3B variants with a common decision interface. Language-native decisions. Task definitions and candidate meanings arrive through natural language, allowing one shared model to handle changing decision spaces without a separate business-category classifier. Compute matched to the decision. The architecture combines selectable execution depth with candidate-aware output projection. Applications choose…
A Qwen3.5 MoE decision model for choosing browser actions, selecting workflow steps, and judging natural-language criteria. Give the model a shared state and a set of candidate actions; the included decision runtime returns a distribution over those candidates. This repository contains 35B-A3B checkpoint-5949 merged BF16 weights. It is a standalone model with root-level Hugging Face configuration and weights, requiring no separate LoRA adapter. The native merged model scores 71/80 (88.75%) at its full 40-layer depth on the Frozen80 development panel. The decision interface accepts 2–16 request-specific candidates. Applications can use the returned candidate IDs to dispatch actions or build…
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.