# Models, Page 169 of 525
Source: https://savrn.com/models/page/169
Markdown alternate of the page above; the site index is https://savrn.com/llms.txt

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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](https://savrn.com/model-hub/methodology)

## Filter Models

Task

- [Text generation1,548](https://savrn.com/models?task=Text+generation)
- [Image and text to text577](https://savrn.com/models?task=Image+and+text+to+text)
- [Feature extraction398](https://savrn.com/models?task=Feature+extraction)
- [Robotics398](https://savrn.com/models?task=Robotics)
- [Text classification331](https://savrn.com/models?task=Text+classification)
- [Text to image232](https://savrn.com/models?task=Text+to+image)
- [Speech recognition187](https://savrn.com/models?task=Speech+recognition)
- [Text to speech145](https://savrn.com/models?task=Text+to+speech)
- [Image classification139](https://savrn.com/models?task=Image+classification)
- [Audio classification130](https://savrn.com/models?task=Audio+classification)
- [Sentence similarity103](https://savrn.com/models?task=Sentence+similarity)
- [Reinforcement learning100](https://savrn.com/models?task=Reinforcement+learning)
- [Token classification100](https://savrn.com/models?task=Token+classification)
- [Zero-shot classification94](https://savrn.com/models?task=Zero-shot+classification)
- [Image segmentation90](https://savrn.com/models?task=Image+segmentation)
- [Object detection86](https://savrn.com/models?task=Object+detection)
- [Translation80](https://savrn.com/models?task=Translation)
- [Any to any79](https://savrn.com/models?task=Any+to+any)
- [Question answering73](https://savrn.com/models?task=Question+answering)
- [Fill mask69](https://savrn.com/models?task=Fill+mask)
- [Time series forecasting64](https://savrn.com/models?task=Time+series+forecasting)
- [Video classification61](https://savrn.com/models?task=Video+classification)
- [Image to text60](https://savrn.com/models?task=Image+to+text)
- [Text to video55](https://savrn.com/models?task=Text+to+video)

Library

- [transformers3,001](https://savrn.com/models?library=transformers)
- [peft367](https://savrn.com/models?library=peft)
- [diffusers206](https://savrn.com/models?library=diffusers)
- [pytorch198](https://savrn.com/models?library=pytorch)
- [gguf187](https://savrn.com/models?library=gguf)
- [mlx169](https://savrn.com/models?library=mlx)
- [lerobot152](https://savrn.com/models?library=lerobot)
- [sentence-transformers139](https://savrn.com/models?library=sentence-transformers)
- [timm49](https://savrn.com/models?library=timm)
- [llama.cpp46](https://savrn.com/models?library=llama.cpp)
- [transformers.js45](https://savrn.com/models?library=transformers.js)
- [flaxspeaker44](https://savrn.com/models?library=flaxspeaker)
- [diffusion-single-file40](https://savrn.com/models?library=diffusion-single-file)
- [onnx40](https://savrn.com/models?library=onnx)
- [vllm40](https://savrn.com/models?library=vllm)
- [openpi35](https://savrn.com/models?library=openpi)
- [minimax-h328](https://savrn.com/models?library=minimax-h3)
- [ultralytics24](https://savrn.com/models?library=ultralytics)
- [PaddleOCR22](https://savrn.com/models?library=PaddleOCR)
- [coreml22](https://savrn.com/models?library=coreml)
- [exllamav322](https://savrn.com/models?library=exllamav3)
- [openvino20](https://savrn.com/models?library=openvino)
- [litert17](https://savrn.com/models?library=litert)
- [litert-lm16](https://savrn.com/models?library=litert-lm)

Modality

- [Other7,212](https://savrn.com/models?modality=Other)
- [Text2,838](https://savrn.com/models?modality=Text)
- [Image and text637](https://savrn.com/models?modality=Image+and+text)
- [Image598](https://savrn.com/models?modality=Image)
- [Control498](https://savrn.com/models?modality=Control)
- [Audio481](https://savrn.com/models?modality=Audio)
- [Video116](https://savrn.com/models?modality=Video)
- [Multimodal79](https://savrn.com/models?modality=Multimodal)
- [Time series64](https://savrn.com/models?modality=Time+series)
- [Tabular56](https://savrn.com/models?modality=Tabular)

License

- [apache-2.03,344](https://savrn.com/models?license=apache-2.0)
- [mit1,319](https://savrn.com/models?license=mit)
- [other796](https://savrn.com/models?license=other)
- [cc-by-nc-4.0213](https://savrn.com/models?license=cc-by-nc-4.0)
- [cc-by-4.0164](https://savrn.com/models?license=cc-by-4.0)
- [bsd-3-clause108](https://savrn.com/models?license=bsd-3-clause)
- [gemma63](https://savrn.com/models?license=gemma)
- [cc-by-nc-sa-4.055](https://savrn.com/models?license=cc-by-nc-sa-4.0)
- [llama3.240](https://savrn.com/models?license=llama3.2)
- [unknown33](https://savrn.com/models?license=unknown)
- [agpl-3.032](https://savrn.com/models?license=agpl-3.0)
- [llama3.128](https://savrn.com/models?license=llama3.1)
- [cc-by-sa-4.020](https://savrn.com/models?license=cc-by-sa-4.0)
- [gpl-3.020](https://savrn.com/models?license=gpl-3.0)
- [openrail++17](https://savrn.com/models?license=openrail%2B%2B)
- [creativeml-openrail-m15](https://savrn.com/models?license=creativeml-openrail-m)
- [openmdw-1.115](https://savrn.com/models?license=openmdw-1.1)
- [cc-by-nc-nd-4.014](https://savrn.com/models?license=cc-by-nc-nd-4.0)
- [llama314](https://savrn.com/models?license=llama3)
- [openrail14](https://savrn.com/models?license=openrail)
- [llama28](https://savrn.com/models?license=llama2)
- [afl-3.07](https://savrn.com/models?license=afl-3.0)
- [osl-3.06](https://savrn.com/models?license=osl-3.0)
- [cc0-1.04](https://savrn.com/models?license=cc0-1.0)

Access

- [Open weights12,153](https://savrn.com/models?access=open_access)
- [Access requested at publisher426](https://savrn.com/models?access=gated)

Publisher

- [Team Mradermacher328](https://savrn.com/models?publisher=mradermacher)
- [Yurun Yuan115](https://savrn.com/models?publisher=ryanyr)
- [RunningHub.ai105](https://savrn.com/models?publisher=runninghubai)
- [Qwen91](https://savrn.com/models?publisher=qwen)
- [Jenny Wilson85](https://savrn.com/models?publisher=jenny08311)
- [Jessen Pu77](https://savrn.com/models?publisher=putty77)
- [RKNNAI73](https://savrn.com/models?publisher=rknnai)
- [AI at Meta72](https://savrn.com/models?publisher=facebook)
- [Convergent Intelligence66](https://savrn.com/models?publisher=reaperdoesntknow)
- [Yurun Yuan65](https://savrn.com/models?publisher=yurunyyr)
- [Google64](https://savrn.com/models?publisher=google)
- [Yurun Yuan62](https://savrn.com/models?publisher=yuruny)
- [Firza Hadzami61](https://savrn.com/models?publisher=firzahdzm)
- [Zeyuan Yang59](https://savrn.com/models?publisher=miiche)
- [Yurun Yuan58](https://savrn.com/models?publisher=yyuan244)
- [Yanggangu57](https://savrn.com/models?publisher=yanggangu)
- [Hongao56](https://savrn.com/models?publisher=cisco1963)
- [Christopher Subia-Waud55](https://savrn.com/models?publisher=cwaud)
- [Francesca Padovani53](https://savrn.com/models?publisher=francesca9805)
- [Khazzz1c53](https://savrn.com/models?publisher=khazic)
- [Alfred Smith52](https://savrn.com/models?publisher=alfred8995)
- [Szekely52](https://savrn.com/models?publisher=attila115)
- [NVIDIA50](https://savrn.com/models?publisher=nvidia)
- [Wang49](https://savrn.com/models?publisher=davidwdw)

[Most downloaded](https://savrn.com/models?sort=downloads)[Most liked](https://savrn.com/models?sort=likes)[Recently updated](https://savrn.com/models?sort=updated)[Name](https://savrn.com/models?sort=name)[Largest](https://savrn.com/models?sort=params)

12,579 models, sorted by most downloaded.

Model · Image and text to text

### [Irwan-LightOnOCR-3-0.8B-abliterated-GGUF](https://savrn.com/models/irwan-lightonocr-3-0-8b-abliterated-gguf)

[Team Mradermacher](https://savrn.com/model-publishers/mradermacher)

static quants of https://huggingface.co/irwanworld/Irwan-LightOnOCR-3-0.8B-abliterated For a convenient overview and download list, visit our model page for this model. weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. 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…

Open weights apache-2.0 transformers

[View model](https://savrn.com/models/irwan-lightonocr-3-0-8b-abliterated-gguf)

Model

### [ItbMdDWRRFnnAM0F](https://savrn.com/models/itbmddwrrfnnam0f)

[14A4](https://savrn.com/model-publishers/14a4)

Open weights

[View model](https://savrn.com/models/itbmddwrrfnnam0f)

Model

### [IvEKxoGHn6ImasIX](https://savrn.com/models/ivekxoghn6imasix)

[15A7](https://savrn.com/model-publishers/15a7)

Open weights

[View model](https://savrn.com/models/ivekxoghn6imasix)

Model

### [J-EXP-31-T-Channel](https://savrn.com/models/j-exp-31-t-channel)

[Nakra](https://savrn.com/model-publishers/hastagaras)

Access requested at publisher 25.8B parameters

[View model](https://savrn.com/models/j-exp-31-t-channel)

Model

### [J-RL-Lora](https://savrn.com/models/j-rl-lora)

[Nakra](https://savrn.com/model-publishers/hastagaras)

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).

Access requested at publisher transformers

[View model](https://savrn.com/models/j-rl-lora)

Model

### [J812is4H3kB7DD5Q](https://savrn.com/models/j812is4h3kb7dd5q)

[15A7](https://savrn.com/model-publishers/15a7)

Open weights

[View model](https://savrn.com/models/j812is4h3kb7dd5q)

Model

### [JEPA-CoT](https://savrn.com/models/jepa-cot)

[DKNTZMN](https://savrn.com/model-publishers/dkntzmn)

JEPA-CoT is an experimental chained architecture combining JEPA-style latent prediction with chain-of-thought-inspired multi-stage computation. This repository is licensed under the Apache License 2.0. The complete retrained Scale-1 checkpoint set contains three independent seeds. K=8 · observation dimension=512 · latent dimension=128 · T=24 · batch=512 · epochs=40. Previously verified seed-0 checkpoint: Each checkpoint contains the model identifier, seed, configuration, complete PyTorch state dict, and evaluation metrics.

Open weights apache-2.0

[View model](https://savrn.com/models/jepa-cot)

Model · Image and text to text

### [JEV-27B-VL-AWQ](https://savrn.com/models/jev-27b-vl-awq)

[AI Outfitters](https://savrn.com/model-publishers/aioutfitters)

4-bit AWQ quantization of autotrust/JEV-27B-VL, built with llm-compressor and served with vLLM. System 1 (POST /v1/decide) runs on a single 24 GB GPU — the fp8 build needs two — and on every axis we measured it is faster than the two-GPU fp8 deployment it replaces. The System 1 adapter is merged into the weights (including the decision head), so no --enable-lora is needed. The decision head (lmhead) and the vision tower are kept in bf16; every other text layer is quantized, including the Gated DeltaNet projections of the 48 linear-attention layers — most Qwen3.5-family quants exclude those and therefore do not fit on one 24 GB card. The AWQ step matters: a data-free RTN build of the same…

Open weights apache-2.0 26.9B parameters 262,144 tokens transformers

[View model](https://savrn.com/models/jev-27b-vl-awq)

Model · Text classification

### [JEV-35B](https://savrn.com/models/jev-35b)

[AutoTrust AI Lab](https://savrn.com/model-publishers/autotrust)

autotrust/JEV-27B-VL on the board. Approximate public vision score 72.08 (our rebuild of the vision benchmarks; JEV-27B-VL 71.67 on the same rebuild, a tie within noise). Median single-request latency 241 ms on one B200 (JEV-27B-VL 271 ms). JEV-35B: all 140,178 scoreable requests of the 0.3 public suite answered (0 errors), System 1 only (thinking off), every choice read in one pass (up to 256 options), scored with the kit's score --edition 0.3. Our scoring, not a board entry: the board's Full score also counts private tests (80 %), which only its maintainers run. JEV-27B-VL: the board's public numbers for JEV-27B, whose text decisions JEV-27B-VL reproduces (see the JEV-27B-VL card).…

Open weights apache-2.0 36B parameters 262,144 tokens transformers

[View model](https://savrn.com/models/jev-35b)

Model · Text classification

### [JEV-35B-NVFP4](https://savrn.com/models/jev-35b-nvfp4)

[AutoTrust AI Lab](https://savrn.com/model-publishers/autotrust)

This repository is autotrust/JEV-35B with its 10,240 routed expert MLPs (40 layers × 256 experts) quantized to NVFP4. Everything else is the bf16 model unchanged: attention, linear attention, the shared expert, the routers, the vision tower, the MTP layer, the lmhead, the System 1 adapter, the decision head and the temperatures. Below the NVFP4 comparison, the rest of this card is the JEV-35B card; its tables are the bf16 results unless marked otherwise. Retrieval 0.706, Tools 0.753, Arts 0.413); the largest per-benchmark changes are Home appliance simulator −5.7, CRUXEval −4.7 and GPQA +5.4. CharXiv charts (−5.3). test p = 0.63); every grasped cube still reached the tray (13/13). Games…

Open weights apache-2.0 19.8B parameters 262,144 tokens transformers

[View model](https://savrn.com/models/jev-35b-nvfp4)

Model · Text generation

### [JEV-9B-MLX-8bit](https://savrn.com/models/jev-9b-mlx-8bit)

[Nativ](https://savrn.com/model-publishers/nativ-community)

MLX conversion of autotrust/JEV-9B for mlx-vlm, with the System 1 LoRA (including its lmhead update) merged and lmhead, the decision readout, kept unquantized. JEV is a decision model: it returns a probability for every option of a question in one forward pass and does not generate text. JEV support is not yet in an mlx-vlm release

Open weights apache-2.0 9B parameters 262,144 tokens mlx

[View model](https://savrn.com/models/jev-9b-mlx-8bit)

Model · Text generation

### [JEV-9B-MLX-NVFP4](https://savrn.com/models/jev-9b-mlx-nvfp4)

[Nativ](https://savrn.com/model-publishers/nativ-community)

MLX conversion of autotrust/JEV-9B for mlx-vlm, with the System 1 LoRA (including its lmhead update) merged and lmhead, the decision readout, kept unquantized. JEV is a decision model: it returns a probability for every option of a question in one forward pass and does not generate text. JEV support is not yet in an mlx-vlm release

Open weights apache-2.0 9B parameters 262,144 tokens mlx

[View model](https://savrn.com/models/jev-9b-mlx-nvfp4)

Model · Robotics

### [JM2D](https://savrn.com/models/jm2d)

[Jung](https://savrn.com/model-publishers/robowonsuhk)

Open weights mit

[View model](https://savrn.com/models/jm2d)

Model · Text generation

### [JMicro-10M-chat-research-20261018](https://savrn.com/models/jmicro-10m-chat-research-20261018)

[Japanese Data Analyze](https://savrn.com/model-publishers/japanese-data-analyze)

日本語の約1,000万パラメータBaseモデルに、二つの情報を答える64件の教材で SFT（教師あり追加学習）を行った最終重みです。一般会話の品質は改善せず、 リポジトリと全ファイルはpublicで、アクセス申請・ログインなしで取得できます。 10,001,152 parameters、13層、hidden256、FFN640、GQA 4Q/2KV、語彙4096、 context1024。元Baseからfresh Adam、seed42、LR1e-4、BF16/mathで483更新、 250,003 Assistant教師tokenを学習しました。64教材は8組の事実設定の変形です。 親Baseは研究保存版のweb-10bです。 最大32tokenのgreedy、BF16/mathによる固定DEV評価です。全1,973応答を Codexで非blind判定しており、独立人手評価や一般的な利用全体の精度ではありません。 空応答、同文反復、履歴の訂正失敗、UTF8破損が残ります。 inspectmodel.pyはCPU FP32、nativeモデル、元の対話テンプレートとprotected-byte Tokenizer、greedy、EOT7/EOS3停止、特殊ID六つの抑制を使います。実際の本文と 停止理由をJSONで返します。CPU FP32の試用は上表のBF16評価と精度条件が異なります。 AutoModelForCausalLMやAutoTokenizerの標準形式ではありません。…

Open weights 10M parameters pytorch

[View model](https://savrn.com/models/jmicro-10m-chat-research-20261018)

Model · Text generation

### [JMicro-10M-research-archive](https://savrn.com/models/jmicro-10m-research-archive)

[Japanese Data Analyze](https://savrn.com/model-publishers/japanese-data-analyze)

約1,000万パラメータの日本語Baseモデルをスクラッチ学習した研究の保存版です。 Baseの品質目標は未達です。完成した会話モデルではありません。学習は停止中です。 リポジトリと全ファイルはpublicで、アクセス申請・ログインなしで取得できます。 すべて10,001,152 parameters、語彙4096、context1024の同じnative構成です。 各フォルダにnative model.safetensors、jmicroconfig.json、trainingstate.pt、 出所・元checkpoint SHA256・正確な学習量を記録したprovenance.jsonがあります。 trainingstateはoptimizer・scheduler・RNG等の学習状態です。samplerとデータは除いています。 モデル重みは元checkpointから無損失で保存し、CPU再読込・全重み一致・forward一致を確認しています。 HF Transformers形式への変換・量子化・追加学習は今回行っていません。 - 全体記録: データ、Tokenizer、WSL、学習高速化、EOS、反復、日本語評価、目的関数、構成、最新不採用まで。 - 実験値の機械可読記録: 保存済み4,815 JSON報告の原キー・指標・採否・SHA256。記録数は独立実験数ではありません。 - project/src、project/scripts、project/configs、project/schemas、project/uv.lock: 当時の実装と固定環境。…

Open weights pytorch

[View model](https://savrn.com/models/jmicro-10m-research-archive)

Model

### [JYjKS1FKoznQIOLc](https://savrn.com/models/jyjks1fkoznqiolc)

[15A7](https://savrn.com/model-publishers/15a7)

Open weights

[View model](https://savrn.com/models/jyjks1fkoznqiolc)

Model

### [JangMo](https://savrn.com/models/jangmo)

[Kdh2342](https://savrn.com/model-publishers/kdh2342)

Open weights cc-by-nc-nd-4.0

[View model](https://savrn.com/models/jangmo)

Model

### [Jarvis](https://savrn.com/models/jarvis)

[Coen Hao](https://savrn.com/model-publishers/refriderator)

Open weights

[View model](https://savrn.com/models/jarvis)

Model · Text generation

### [Jarvis-Titan-M4-GRPO-Final](https://savrn.com/models/jarvis-titan-m4-grpo-final)

[Dhanesh Singh](https://savrn.com/model-publishers/dhanesh-hf)

14.8B Total Parameters · 3.8B Active Parameters / Token · Scaled on Google Cloud TPU v5e-8 J.A.R.V.I.S. Titan 14.8B MoE-M4 is an autoregressive reasoning model that combines a sparse Mixture-of-Experts (MoE) transformer with an always-on recurrent neural memory architecture (Titans paradigm). The model was activated from dhanesh-hf/Jarvis-Titan-M4-Activated and subsequently trained using Critic-Free Group Relative Policy Optimization (GRPO) with Reinforcement Learning from Verifiable Rewards (RLVR) on Google Cloud TPU v5e-8 hardware. Standard transformers encounter memory-capacity tradeoffs during extended mathematical and code derivations: retaining all prior key-value pairs incurs O(N)…

Open weights other transformers

[View model](https://savrn.com/models/jarvis-titan-m4-grpo-final)

Model · Keypoint detection

### [Jeff-Tracker](https://savrn.com/models/jeff-tracker)

[Jefrin Antony](https://savrn.com/model-publishers/jeffyantony)

Track any point through a video. Give it a video and a set of points; it returns where each point is on every frame, whether it can see it, and how sure it is. It runs at 0.0065 s/frame and returns a calibrated per-frame confidence alongside every track. It is LocoTrack-B with cross-track attention added — the tracks attend to each other, so a point that is hidden can be inferred from the neighbours that are still visible — then fine-tuned for occlusion — on MOVi-E, and in the newer checkpoint on Meta's Apache-2.0 Kubric release. This repository is gated. Click Request access; the maintainer approves manually. Once The LocoTrack baseline weights are not mirrored here; they come from the…

Access requested at publisher apache-2.0 pytorch

[View model](https://savrn.com/models/jeff-tracker)

Model · Text classification

### [Jev-LCT-Adapters](https://savrn.com/models/jev-lct-adapters)

[CaoHaoWei](https://savrn.com/model-publishers/caohaowei)

This repository contains the standalone lightweight Looped Adapters and uncertainty calibration mappers for Jev-LCT (Looped Calibration Transformer). Instead of downloading the entire merged model weights (which range from 2GB to 16GB), users who already have base Qwen models (Qwen2.5-0.5B, Qwen2.5-1.5B, Qwen3-8B) can directly mount these compact adapter weights (only 220MB ~ 1.5GB) onto the last $k=2$ layers of the base model. 本仓库托管 Jev-LCT (循环校准 Transformer) 的全套独立轻量级适配器权重（.pt）与保序校准映射器（.pkl）。 对于本地已有 Qwen 官方基座（如 Qwen/Qwen2.5-0.5B、Qwen/Qwen2.5-1.5B、Qwen3-8B）的开发者，无需重复下载数十 GB 的完整权重，仅需下载本仓库对应的轻量适配器（仅 220MB ~ 1.5GB），即可在原生基座上获得 50ms 级别系统一极速决策与内生轨迹校准置信度能力。 3. 即插即用：与 lctqwenstandalone.py…

Open weights apache-2.0 transformers

[View model](https://savrn.com/models/jev-lct-adapters)

Model · Text classification

### [Jev-LCT-Qwen2.5-0.5B](https://savrn.com/models/jev-lct-qwen2-5-0-5b)

[CaoHaoWei](https://savrn.com/model-publishers/caohaowei)

Jev-LCT-Qwen2.5-0.5B is the ultra-lightweight edge edition of the Jev-LCT System-One decision family. Weighing only ~1.9 GB in full bfloat16 weights, it is optimized for high-throughput API gateway routing, edge robotics, Raspberry Pi, and mobile deployment. - ~50ms 极低延迟：专为高并发 API 网关路由、实时内容审核设计； - MMLU 达 50.0%：大幅超越参数相近的判别模型（Laya 33.3%, Open-Jev 35.0%）； Apache License 2.0. Full repository at GitHub.

Open weights apache-2.0 494M parameters 32,768 tokens transformers

[View model](https://savrn.com/models/jev-lct-qwen2-5-0-5b)

Model · Text classification

### [Jev-LCT-Qwen2.5-1.5B](https://savrn.com/models/jev-lct-qwen2-5-1-5b)

[CaoHaoWei](https://savrn.com/model-publishers/caohaowei)

In agentic workflows, API routing, and edge decision-making, conventional autoregressive LLMs suffer from high token-by-token generation latency and brittle string parsing, while small discriminative models produce systematically miscalibrated verbal confidence (overconfident hallucinations). Jev-LCT (Looped Calibration Transformer) establishes a new paradigm for System-One Decision Models: 1. Parallel Looped Prefill: Recurrently iterates only the top $k=2$ layers of Qwen2.5-1.5B with sequence right-shifting and Scale-Preserving RMS Injection, strictly preventing representation collapse. 2. Endogenous Trajectory Confidence: Extracts genuine calibrated confidence directly from hidden state…

Open weights apache-2.0 1.5B parameters 131,072 tokens transformers

[View model](https://savrn.com/models/jev-lct-qwen2-5-1-5b)

Model · Text classification

### [Jev-LCT-Qwen3-8B](https://savrn.com/models/jev-lct-qwen3-8b)

[CaoHaoWei](https://savrn.com/model-publishers/caohaowei)

Jev-LCT-Qwen3-8B is the flagship enterprise-grade decision engine of the Jev-LCT family. Combining Qwen3-8B's extensive foundational capabilities with Looped Calibration and adaptive early exit, it provides frontier generative reasoning capabilities with deterministic sub-100ms decision latency. - 85.0% 科学推理 + 70.0% MMLU：媲美中大型生成模型的复杂逻辑推理能力，但单次推断控制在 89.2 ms 内。 - 企业级智能体中枢：支持高风险场景的“选择性预测（Selective Prediction）”，在 80% 覆盖率下实现近乎零差错审核。 - 全量独立权重：开箱即用，支持多 GPU 分片或单张 24GB 显卡（RTX 3090 / 4090）bfloat16 全速推断。 Apache License 2.0. Full repository at GitHub.

Open weights apache-2.0 8.2B parameters 40,960 tokens transformers

[View model](https://savrn.com/models/jev-lct-qwen3-8b)

## Model Collections

Hand-picked starting points, each with the reason it exists.

Collection · 4 entries

### [Embedding models for retrieval](https://savrn.com/collections/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.

[View collection](https://savrn.com/collections/embedding-models-for-retrieval)

Collection · 4 entries

### [Models that fit on one accelerator](https://savrn.com/collections/models-that-run-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.

[View collection](https://savrn.com/collections/models-that-run-on-one-accelerator)

Collection · 6 entries

### [Open-weight text models worth knowing](https://savrn.com/collections/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.

[View collection](https://savrn.com/collections/open-weight-text-models-worth-knowing)

Collection · 3 entries

### [Speech and audio models](https://savrn.com/collections/speech-and-audio)

Recognition and synthesis models, grouped so the two directions are easy to compare.

[View collection](https://savrn.com/collections/speech-and-audio)

## 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](https://savrn.com/ai-index/pricing/open-models)

The same open-weight model priced by every host that serves it, per million tokens.

Research Hub

### [Data center trackers and maps](https://savrn.com/data-center-research)

Moratoriums, permits, power, water and capital behind the facilities that run these models.

Method

### [How the Model Hub is built](https://savrn.com/model-hub/methodology)

Sources, evidence labels, refresh behaviour, and the limits of every comparison here.