本地改写页面 Drop AIGC： https://github.com/k856487/Drop-AIGC Dailv1.5-f16.gguf 为 F16 GGUF，约 3.1 GB，由 llama.cpp 加载。 页面的环境、启动方式与可选的推理服务地址，见 GitHub 仓库说明。
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.
12,579Models
3,583Datasets
291Papers
7,578Publishers
12,850Sourced relationships
Updated 2026-10-23 · How the library is built
12,579 models, sorted by most downloaded.
libraryname: transformers - neuromorphic - connectomics - drosophila - flywire - sparse-computation - causal-lm - qwen - flywire-codex-2024 pipelinetag: text-generation Biological Substrate: Adult Drosophila melanogaster Whole-Brain Connectome (FlyWire / Codex, Nature 2024) Contemporary Large Language Models rely universally on dense, rectangular projections ($O(N^2)$). Every token representation is forced through uniform, all-to-all matrix transformations that treat every latent channel with identical geometric indifference. Biological nervous systems operate under radically different evolutionary constraints. The adult fruit fly (Drosophila melanogaster) coordinates complex 3D aerobatics…
Roy C Abstract. We present a four-stage pipeline for producing small language models (1.7B parameters) that exhibit self-critiquing dual-cognition reasoning from a 30B-parameter teacher. The pipeline chains: (1) proof-weighted knowledge distillation from three teacher variants (Instruct, Thinking, Coder) of Qwen3-30B-A3B, each producing distinct capability profiles in the student; (2) Topological Knowledge Distillation (TKD), which uses the Discrepancy Calculus (DISC) framework to decompose the teacher's output distribution into smooth, jump, and singular-continuous components via bounded variation theory, allocating training capacity to structural boundaries that standard KD smears across…
Paper: arXiv:2610.02638 · Code: github.com/adventists-ai/duplexjev · Research note: teaching a speech-decision LLM to hear stress DuplexJev-32B-Turn that also hears stress and pauses. "I didn't say she took the money" and "I didn't say she took the money" are the same words. This model can tell them apart: it answers which word is stressed, is any word stressed at all, and where the speaker pauses, in Mandarin and English. Spoken QA, gender, emotion and turn-taking stay at the 32B-Turn level. Same size, same serving stack, same prompt format as the other DuplexJev models. DuplexJev reads typed, closed-set decisions from speech as a single-token distribution of the LLM: zero decode steps…
Paper: arXiv:2610.02638 · Code: github.com/adventists-ai/duplexjev · Plugins: pipecat-duplexjev, livekit-plugins-duplexjev The 4B DuplexJev tuned for live end-of-turn: is the user done, right after they go quiet? A turn-taking variant of DuplexJev-4B-Para v1.1, which stays the general 4B. Same recipe as DuplexJev-32B-Para v1.2: the 4B-Para v1.1 mix plus accent data and pause-level turn-taking data, one rank-16 LoRA, 4,000 steps, merged. Qwen3-ASR-0.6B encoder + connector + Qwen3-4B, 4.2 B parameters, bf16, ~10 GB GPU memory. Which 4B to pick. - This one for a voice agent's end-of-turn on a small GPU (LiveKit / Pipecat plugins). - 4B-Para for everything else (stress, non-verbal sounds…
Paper: arXiv:2610.02638 · Code: github.com/adventists-ai/duplexjev · Research note: teaching a speech-decision LLM to hear stress DuplexJev-4B-Turn that also hears stress and pauses, for smaller GPUs. "I didn't say she took the money" and "I didn't say she took the money" are the same words; this model tells them apart. It answers which word is stressed, is any word stressed at all, where the speaker pauses, and what the stress implies, in Mandarin and English. Trade-off — read before choosing. At 4B, adding stress costs some spoken-QA accuracy: VoiceBench OBQA / MMSU is 42.0 / 38.2 against 46.4 / 41.7 for 4B-Turn, and qa100 / ZJU-ML are 1 point lower. We tried lower stress weights…
A small DuplexJev connector: the Whisper-small encoder (frozen) feeds a frozen Qwen3-4B-2507, and every typed question is read as a single-token distribution over its options, with zero decode steps and many questions per forward pass. Trained with the same recipe as the Qwen3-32B connectors released with the paper. Leaderboard scores (see the GitHub README): main language (mean of qa100, ZJU-ML, Easy-Turn) 53.5, paralinguistics (mean of gender, emotion) 43.0. Paper protocol: the evaluation prompts of the paper (single option order, one H200). duplexjev defaults: the package's own prompts with lang matched to the clip. Spoken QA with a small LLM is bounded by the LLM itself (see the…
A small DuplexJev connector: the Whisper-small encoder (frozen) feeds a frozen SmolLM3-3B, and every typed question is read as a single-token distribution over its options, with zero decode steps and many questions per forward pass. Trained with the same recipe as the Qwen3-32B connectors released with the paper. The connector was trained with SmolLM3's no-think format (an empty block before the answer). The tokenizer in this repository carries that chat template; SmolLM3's default template adds a long system prompt with today's date and would not match. Leaderboard scores (see the GitHub README): main language (mean of qa100, ZJU-ML, Easy-Turn) 37.4, paralinguistics (mean of gender…
This is a frozen snapshot of CL, EDOS Engineering's experimental continual-learning chatbot, deployed under the persona "Hades." CL learns while it runs by editing the model's original quantized weight tensors in place. Its training is self-directed: the model selects its own training corpus, decides what is important, and is then trained on what it selected. It reads new AI research papers in full, judges which findings matter, writes the training text itself as attributed reviews, and marks which of them to learn and how strongly. It also learns from conversation. It uses no LoRA, adapter or side network. This export is the live model after 1,091 online learning updates, merged into one…
An ELARSpeech automatic speech recognition model for Waimaha. Input audio must be mono, sampled at 16 kHz. Whisper uses an ELARSpeech language token in the explicit decoder prompt. Use the prompt above rather than language= to reproduce the exported model's decoding behavior. The exported artifact is checked against the original ELARSpeech checkpoint on a fixed evaluation sample set. Exact-transcription verification status: PASS. This is a single-language ASR model. Accuracy can vary by recording conditions, speaker population, orthography, and domain. Resample input audio to 16 kHz mono. Please cite ELARSpeech and the base model above when using this model. See the ELARSpeech project…
An ELARSpeech automatic speech recognition model for Bastimentos Creole English. Input audio must be mono, sampled at 16 kHz. Whisper uses an ELARSpeech language token in the explicit decoder prompt. Use the prompt above rather than language= to reproduce the exported model's decoding behavior. The exported artifact is checked against the original ELARSpeech checkpoint on a fixed evaluation sample set. Exact-transcription verification status: PASS. This is a single-language ASR model. Accuracy can vary by recording conditions, speaker population, orthography, and domain. Resample input audio to 16 kHz mono. Please cite ELARSpeech and the base model above when using this model. See the…
An ELARSpeech automatic speech recognition model for Belait. Input audio must be mono, sampled at 16 kHz. Whisper uses an ELARSpeech language token in the explicit decoder prompt. Use the prompt above rather than language= to reproduce the exported model's decoding behavior. The exported artifact is checked against the original ELARSpeech checkpoint on a fixed evaluation sample set. Exact-transcription verification status: PASS. This is a single-language ASR model. Accuracy can vary by recording conditions, speaker population, orthography, and domain. Resample input audio to 16 kHz mono. Please cite ELARSpeech and the base model above when using this model. See the ELARSpeech project…
An ELARSpeech automatic speech recognition model for Bunak. Input audio must be mono, sampled at 16 kHz. Whisper uses an ELARSpeech language token in the explicit decoder prompt. Use the prompt above rather than language= to reproduce the exported model's decoding behavior. The exported artifact is checked against the original ELARSpeech checkpoint on a fixed evaluation sample set. Exact-transcription verification status: PASS. This is a single-language ASR model. Accuracy can vary by recording conditions, speaker population, orthography, and domain. Resample input audio to 16 kHz mono. Please cite ELARSpeech and the base model above when using this model. See the ELARSpeech project…
ELF-B, a continuous diffusion language model, trained on the embeddings of T5Gemma-2-270M. From the paper Scaling and Distilling Text Embeddings for Better Diffusibility. ELF (Embedded Language Flow) is a continuous diffusion language model that generates a sequence of text embeddings and decodes them to tokens. This model is the unchanged ELF-B trained on OpenWebText-1024 (sequences of 1024 tokens) for 5 epochs at a batch of 512, on the frozen embeddings of the T5Gemma-2 encoder. Sampling uses the SDE sampler with self-conditioning guidance; --sc sets the guidance scale and --nfe the number of steps. With the code of the paper; the checkpoint and the tokenizer are downloaded from this…
ELF-B, a continuous diffusion language model, trained on the embeddings of T5Gemma-2-270M-OWTdistilled, distilled from T5Gemma-2. From the paper Scaling and Distilling Text Embeddings for Better Diffusibility. ELF (Embedded Language Flow) is a continuous diffusion language model that generates a sequence of text embeddings and decodes them to tokens. This model is the unchanged ELF-B trained on OpenWebText-1024 (sequences of 1024 tokens) for 5 epochs at a batch of 512, on the frozen embeddings of the distilled student T5Gemma-2-270M-OWTdistilled. Sampling uses the SDE sampler with self-conditioning guidance; --sc sets the guidance scale and --nfe the number of steps. With the code of the…
ELF-M, a continuous diffusion language model, trained on the embeddings of T5Gemma-2-270M. From the paper Scaling and Distilling Text Embeddings for Better Diffusibility. ELF (Embedded Language Flow) is a continuous diffusion language model that generates a sequence of text embeddings and decodes them to tokens. This model is the unchanged ELF-M trained on OpenWebText-1024 (sequences of 1024 tokens) for 5 epochs at a batch of 512, on the frozen embeddings of the T5Gemma-2 encoder. Sampling uses the SDE sampler with self-conditioning guidance; --sc sets the guidance scale and --nfe the number of steps. With the code of the paper; the checkpoint and the tokenizer are downloaded from this…
ELF-M, a continuous diffusion language model, trained on the embeddings of T5Gemma-2-270M-OWTdistilled, distilled from T5Gemma-2. From the paper Scaling and Distilling Text Embeddings for Better Diffusibility. ELF (Embedded Language Flow) is a continuous diffusion language model that generates a sequence of text embeddings and decodes them to tokens. This model is the unchanged ELF-M trained on OpenWebText-1024 (sequences of 1024 tokens) for 5 epochs at a batch of 512, on the frozen embeddings of the distilled student T5Gemma-2-270M-OWTdistilled. Sampling uses the SDE sampler with self-conditioning guidance; --sc sets the guidance scale and --nfe the number of steps. With the code of the…
EMNLP 2026 Accepted Paper LLMs Can Predict Failure Risk, But Struggle to Predict Which Collaboration Protocol Pays Off: Cost-Aware Protocol Routing Across Reasoning Tasks This is not a language model. It contains no foundation-model weights. The paper evaluates two third-party solver families — openai/gpt-oss-120b and google/gemma-4-31B-it — which we did not train, do not own, and do not redistribute. Their licenses and terms are their own. This is a research-artifact card, not a checkpoint release. It documents the paper's lightweight routers: small scikit-learn classifiers that pick which collaboration protocol to run. It ships their training code, configurations, selected…
OpenESM d12 model trained on ClimbMix. This repository contains the pretraining checkpoint without optimizer state conversion. - model.ckpt: Lightning checkpoint. - modelingesm.py: standalone model and checkpoint loader. - tokenizer/tokenizer.pkl: serialized ESM tokenizer. - tokenizer/tokenbytes.pt: token byte table for BPB evaluation. The 160M label describes the model parameter count. Training token counts are intentionally omitted from the repository name and file names. The code and checkpoint format are maintained in the
OpenESM d26 model trained on ClimbMix. This repository contains the pretraining checkpoint without optimizer state conversion. - model.ckpt: Lightning checkpoint. - modelingesm.py: standalone model and checkpoint loader. - tokenizer/tokenizer.pkl: serialized ESM tokenizer. - tokenizer/tokenbytes.pt: token byte table for BPB evaluation. The 1B label describes the model parameter count. Training token counts are intentionally omitted from the repository name and file names. The code and checkpoint format are maintained in the
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.