The Guru Models account for developers building with the Guru API.
Joined February 2025
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Guru Developers retweeted
Train your decision-model using ms-swift: with model-specific losses, automatic two-level temperature calibration and built-in Open-Jev data.
Also capable of converting causal language models into decision models, with generic_decision option.
Guru Developers retweeted
Glad to see @ModelScope2022 bring decision-model training and inference into ms-swift.
AutoTrust AI’s JEV-27B-VL is now included in the framework’s decision workflow via `--task_type decision`, with dedicated loss support, built-in Open-Jev data, and automatic two-level temperature calibration.
This gives developers a more practical route to fine-tune and run calibrated multimodal decisions—where agents need to choose, score, route, or escalate with probabilities they can use.
Explore ms-swift: github.com/modelscope/ms-swi…
Guru Developers retweeted
A quick start with ScienceGuru.
Start a chat. Keep your research, code, and data together in one project folder. Customize settings when you need to. Define the goal, materials, and output before work begins. Speak your task, review it, then send.
When it is time to move on, copy a working directory, conversation deeplink, or Markdown and pick up where you left off.
One desktop research workspace for organizing work, defining outcomes, and delivering results.
Download #ScienceGuru: scienceguru.ai
Guru Developers 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…
Guru Developers 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…
Guru Developers 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…
Guru Developers 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…
Guru Developers 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.
Guru Developers 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.
Guru Developers retweeted
1/ Introducing AutoTrust-JEV-27B v1.1.
A 27B text decision model that assigns probabilities to the answer options supplied with each question.
This is for the thousands of bounded calls inside agent workflows: tool choice, request routing, intent classification, API-use decisions, and evidence checks.
Apache-2.0 weights. Text-only. Choice and yes/no inputs.
Model card: huggingface.co/autotrust/Aut…
Guru Developers 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…
Guru Developers retweeted
5/ Selected task-adaptation results from the card:
• 97.06 — CLINC150+OOS intent classification
• 96.25 — BFCL tool selection
• 93.63 — BANKING77 intent classification
• 89.77 — home appliance simulator decisions
• 87.36 — API-Bank API-use decisions
• 78.24 — HoVer claim verification
These are evaluated task scores, not a claim of perfect real-world reliability or end-to-end autonomous-agent readiness.
ALT Attachment for Tweet 5 — Original model-card benchmark highlights chart.
Guru Developers retweeted
4/ Its highest-scoring area in the reported run is Tools & Automation: 85.19 skill points.
The same model-card table reports:
• Language Understanding: 73.38
• Retrieval & Classification: 66.97
• Knowledge & Reasoning: 55.16
• Arts & Human Taste: 51.08
A skill score adjusts for task chance level; 0 is chance and 100 is perfect. The public index is not a simple average of the rounded area scores.
ALT Attachment for Tweet 4 — Image rendering of the model card’s existing Scores by area table.
Guru Developers 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.
Guru Developers 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…
Guru Developers retweeted
JEV-27B v1.1 is built for structured decisions rather than long-form generation: text in, a bounded choice out.
In our complete local Decision Index 0.3 evaluation across 37 benchmarks, it scored 96.25 on tool selection, 97.06 on intent classification with out-of-scope detection, and 78.24 on claim verification. Apache-2.0 weights and vLLM deployment instructions are available now.
ModelScope:
modelscope.ai/models/AutoTru…
Guru Developers 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…
Guru Developers retweeted
Explore the Decision API 👇
docs.novita.ai/api-reference…
Guru Developers 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
Guru Developers 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