The AI OS for scientific discovery. We run the whole research lifecycle — ideas, experiments, IP, funding, publication. Built for world-class science.
Joined January 2025
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ScienceGuru retweeted
New from AutoTrust AI: Qwen3.8-Flash-Next-E384-FP4-Local.
A compact AutoTrust build of NVIDIA’s Qwen3.8 Flash Next FP4 Local checkpoint, designed for practical local deployment.
Our NAS recipe keeps 384 of 512 routed experts in every decoder and MTP layer, while preserving top-10 routing, shared experts, attention, vision tower, and tokenizer.
Result: 79.78 GiB of weights instead of 95.12 GiB — 16.1% smaller.
In our same-harness tests, code, tool use, math, and vision stay close to the original checkpoint. The tradeoff is lower performance on knowledge-heavy multiple-choice tasks, so use the full 512-expert model when that is the priority.
We also include a lossless MTP speculative-decoding recipe for vLLM 0.31: 308.9 tok/s and 3.34× the no-MTP speed on one B200 at greedy decoding.
Explore the model, benchmarks, files, and vLLM setup:
huggingface.co/autotrust/Qwe…
ScienceGuru retweeted
After Black October, Bring the AI Home.
A month of critical vulnerabilities, AI-agent campaigns, and ultra-low-cost attacks changed the security calculus.
The answer is not to stop using AI. It is to stop sending your most sensitive data across the internet to rent it.
For source code, regulated records, deal information, and other high-value workflows, ask one question:
Does the data go to the model — or does the model come to the data?
With DGX Spark–class hardware and compressed open models, local AI is now practical. Keep the data, model weights, and agents inside one perimeter.
Read the full argument:
autotrust.ai/blog/after-blac…
ScienceGuru retweeted
AutoTrust Decision Models — one collection, from fast local decisions to multimodal agent control.
Every model returns calibrated probabilities for the answer options that matter: route or escalate, pick the next tool, score risk, verify a claim, or decide what action an agent should take next.
Start with the classics:
JEV-9B — compact typed decisions with System 2 reasoning
JEV-27B & JEV-27B v1.1 — our open text decision models
JEV-27B-VL — multimodal decisions across text and images
GEV-26B-Decide — a high-performing general decision model
JEV-35B — larger-scale decision capability
NVFP4 releases — lower-memory deployment options for practical inference
The collection also includes live demos for Mario, Rubik’s Cube, Tetris, and typed decisions, plus the public Decision Index result set.
Together, the models in this collection have now recorded more than 5.4M downloads.
Explore the full AutoTrust Decision Models collection:
huggingface.co/collections/a…
ScienceGuru retweeted
Want to see what open decision models can actually do?
AutoTrust Spaces is our hands-on demo hub: play Mario, Rubik’s Cube, and Tetris with JEV-27B; test typed decisions with JEV-9B; and explore live News and Shorts demos.
Try the demos:
huggingface.co/autotrust/spa…
ScienceGuru retweeted
Top 3 in both Vision and Text categories.
Ahead of TypeSafe JEV in Text.
JEV-27B-VL reached No. 1 on Hugging Face Global Trending.
Now AutoTrust-JEV-27B v1.1 is Top 3 on both Jev Decision Index boards.
Text: tied for #3 with a 68.0 Full Score.
That puts our open 27B model ahead of TypeSafe’s hosted Jev 1.13 — and ahead of Liquid AI’s d1-3B — on the same public Decision Index.
Vision: #3 with a 71.9 Full Score, including 74.4 on the public set and 69.3 on the private set.
It ranks ahead of Perplexity Decider v1.2, vLLM-SR d3, and Cloudflare’s clef-omni.
This is not a one-model spike. It is a model family built for the decision layer of agents: structured probabilities for routing, tool selection, claim checks, next-action choice, and escalation to deeper reasoning when confidence is not enough.
AutoTrust is establishing itself as a clear leader in open decision models.
Open weights. Apache-2.0. Built for local deployment.
Explore the Jev Decision Index:
huggingface.co/spaces/multim…
Download AutoTrust-JEV-27B v1.1:
huggingface.co/autotrust/Aut…
ALT Top 3 in both Vision and Text categories. Ahead of TypeSafe JEV in Text. JEV-27B-VL reached No. 1 on Hugging Face Global Trending. Now AutoTrust-JEV-27B v1.1 is Top 3 on both Jev Decision Index boards. Text: tied for #3 with a 68.0 Full Score. That puts our open 27B model ahead of TypeSafe’s hosted Jev 1.13 — and ahead of Liquid AI’s d1-3B — on the same public Decision Index. Vision: #3 with a 71.9 Full Score, including 74.4 on the public set and 69.3 on the private set. It ranks ahead of Perplexity Decider v1.2, vLLM-SR d3, and Cloudflare’s clef-omni. This is not a one-model spike. It is a model family built for the decision layer of agents: structured probabilities for routing, tool selection, claim checks, next-action choice, and escalation to deeper reasoning when confidence is not enough. AutoTrust is establishing itself as a clear leader in open decision models. Open weights. Apache-2.0. Built for local deployment.
ALT Top 3 in both Vision and Text categories. Ahead of TypeSafe JEV in Text. JEV-27B-VL reached No. 1 on Hugging Face Global Trending. Now AutoTrust-JEV-27B v1.1 is Top 3 on both Jev Decision Index boards. Text: tied for #3 with a 68.0 Full Score. That puts our open 27B model ahead of TypeSafe’s hosted Jev 1.13 — and ahead of Liquid AI’s d1-3B — on the same public Decision Index. Vision: #3 with a 71.9 Full Score, including 74.4 on the public set and 69.3 on the private set. It ranks ahead of Perplexity Decider v1.2, vLLM-SR d3, and Cloudflare’s clef-omni. This is not a one-model spike. It is a model family built for the decision layer of agents: structured probabilities for routing, tool selection, claim checks, next-action choice, and escalation to deeper reasoning when confidence is not enough. AutoTrust is establishing itself as a clear leader in open decision models. Open weights. Apache-2.0. Built for local deployment.
ScienceGuru retweeted
Meet AutoTrust-JEV-27B-v1.1: a 27B text-classification model built for structured decisions. With 5.6k downloads and 166 likes, it's quietly becoming a go-to for teams that need reliable, repeatable classification at scale.
ScienceGuru retweeted
Thank you, @HuggingModels.
AutoTrust-JEV-27B-v1.1 has now passed 16K downloads on Hugging Face.
It is an open 27B decision model for the high-frequency calls inside agent systems: route a request, select a tool, detect an out-of-scope intent, verify a claim, or choose the next action from a fixed set of options.
Instead of producing another paragraph, it returns a probability for every available option — making decisions measurable, routable, and easier to audit.
Apache-2.0. Run it locally with vLLM: 📷huggingface.co/autotrust/Aut…
ScienceGuru retweeted
2/ The model card reports a complete local run of the Decision Index 0.3 public suite:
• Public index: 67.31
• 37 indexed benchmarks
• 140,178 completed scoreable requests
Important context: this is a self-reported local evaluation using the official kit, pending maintainer verification. It is not an official leaderboard placement.
ALT Attachment for Tweet 2 — Original model-card Decision Index public-performance chart.
ScienceGuru retweeted
Meet AutoTrust-JEV-27B v1.1 — a 27B text decision model built for the thousands of small choices inside an AI agent.
Instead of generating a long response, it turns a defined question and its options into probabilities you can act on: choose the right tool, route an out-of-scope request, classify an intent, assess a claim, or select the next API action.
In our complete local run of the Decision Index 0.3 public suite, JEV-27B v1.1 achieved a 67.31 public index across 37 benchmarks and 140,178 completed scoreable requests, with particular strength in tools and automation:
• 96.25 on tool selection
• 97.06 on intent classification with out-of-scope detection
• 93.63 on banking intent classification
• 78.24 on claim verification
Apache-2.0 weights and decision readout are available now on @HuggingModels and @ModelScope2022.
Download on Hugging Face:
huggingface.co/autotrust/Aut…
Download on ModelScope:
modelscope.ai/models/AutoTru…
ScienceGuru retweeted
Try JEV in the browser. ⚡
AutoTrust AI’s JEV Demo is now live on @ModelScope2022 Studios — a hands-on place to explore our multimodal decision-model work.
JEV is designed for the small but critical choices inside AI systems: selecting an option, assigning a score, evaluating confidence, and deciding what an agent should do next.
Open the demo, try it with your own prompts, and follow AutoTrust Labs on ModelScope for new models, demos, and decision-AI updates.
JEV Demo:
modelscope.ai/studios/AutoTr…
ScienceGuru retweeted
🚀 JEV-27B-VL from @AutoTrustAI is now live on Novita AI!
Launching as a partner ⚡
🎁 Free for 14 days.
A 27B vision-language model for decisions over text and images.
• Yes/no, ratings, or multiple-choice decisions
• Probabilities for each option
• Optional reasoning for harder decisions
ScienceGuru retweeted
Explore the Decision API 👇
docs.novita.ai/api-reference…
ScienceGuru retweeted
Great to launch with our partner @novita_labs.
JEV-27B-VL is now live on Novita AI, bringing AutoTrust AI’s open 27B multimodal decision model to developers through a production-ready API.
Send text or images. Receive calibrated probabilities for yes/no decisions, 0–5 scoring, or multiple-choice selection. Use fast System 1 decisions for high-volume routing and action loops, then enable System 2 reasoning when a harder decision needs more thought.
A practical fit for visual QA, document and chart review, computer-use workflows, content ranking, and confidence-gated agents.
Try JEV-27B-VL free for 14 days on Novita AI:
docs.novita.ai/api-reference…
🚀 JEV-27B-VL from @AutoTrustAI is now live on Novita AI!
Launching as a partner ⚡
🎁 Free for 14 days.
A 27B vision-language model for decisions over text and images.
• Yes/no, ratings, or multiple-choice decisions
• Probabilities for each option
• Optional reasoning for harder decisions
ScienceGuru retweeted
Meet GEV-26B-Decide: a 26B text-classification model that blends system-one intuition with system-two reasoning. It outputs typed decisions with calibrated probabilities, making AI choices more transparent and trustworthy.
ScienceGuru retweeted
Meet GLM5.3-Flash-E192-DGX-Spark: a new image-text-to-text model that's turning heads. Built for blazing fast multimodal tasks, it's already got 4.5k downloads. Curious? Let's break it down.
ScienceGuru retweeted
Adaptive Thinking, Measured.
Thank you @HuggingModels for featuring AutoTrust AI’s GEV-26B-Decide.
GEV is built for the decision layer inside agent products. System 1 makes a fast, calibrated choice: route a ticket, rank results, select a tool, or choose the next action. When confidence falls below 0.8, it can invoke System 2 reasoning and fold that result back into the final probabilities.
The result: simple cases move quickly, while ambiguous cases get more thought instead of forcing a long reasoning trace on every request.
In our 1,754-question public evaluation, adaptive mode reached 83.4% accuracy versus 83.8% with reasoning on every question, while calling System 2 for only 47.8% of cases. System 1 takes about 45 ms on one B200.
The model card now shows 1.05M downloads in the past month.
Download and build with it:
huggingface.co/autotrust/GEV…
ScienceGuru retweeted
Long Context, More Headroom.
Thanks @HuggingModels for featuring AutoTrust AI’s GLM5.3-Flash-E192-DGX-Spark.
E192 is our most memory-efficient GLM-5.3-Flash build for multimodal, tool-using agents. It keeps image/video input, structured tool calling, and 18B active parameters per token in a 124 GiB weight footprint.
Built for 2× DGX Spark or one B200/GB200. The included NVFP4 MTP draft reached 1.80× faster single-stream decoding in our B200 test, with unchanged output quality.
Download:
huggingface.co/autotrust/GLM…
ScienceGuru retweeted
Meet GLM5.3-Flash-E256-DGX-Spark: a multimodal MoE model that takes image+text in and generates text out. Built for vLLM and optimized with NVFP4 on Blackwell GB10, it's a lean, fast beast for next-gen AI apps. 3k+ downloads already!
ScienceGuru retweeted
Thank you @HuggingModels for featuring AutoTrust AI’s GLM5.3-Flash-E256-DGX-Spark.
This is our largest Blackwell-oriented GLM-5.3-Flash build: 145B total parameters, 18B active per token, with 256 of 288 routed experts retained through neural architecture search.
It keeps multimodal image and video input, full vocabulary support, structured tool calling, and long-context reasoning. NVFP4 experts and an FP8 KV cache cut the weight footprint to 170.5 GB.
The included NVFP4 MTP draft reaches 1.84× single-stream decode speed on a B200 with unchanged output quality. On our evaluation runs, E256 reached 97.6% on HumanEval, 89.4% on C-Eval, and 76.1% on MMMU.
Download the MIT-licensed model and run it with vLLM:
huggingface.co/autotrust/GLM…
ScienceGuru retweeted
JEV-35B turns Qwen3.5-35B-A3B into a fast System 1 decision model, activating only around 3B of its 35B parameters per token. Apache 2.0.
🤖 modelscope.ai/collections/Au…
🏆 Outperforms JEV-27B-VL on the Decision Index public suite while retaining competitive visual decision performance.
⚡ Compare up to 256 text or image options in one pass, return calibrated probabilities, and reach around 204ms median single-request latency in the tested setup.
📦 The NVFP4 version is only 25.6GB and uses around 23GiB for weights, enabling local deployment on a single 32–48GB GPU.
🔍 Quantization preserves 96.6% of the BF16 model’s decisions with nearly unchanged latency and only a small benchmark drop.
🛠️ Start with the included vLLM script. Use /v1/decide for fast System 1 decisions or OpenAI-compatible endpoints for System 2 reasoning.
