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autojev

Full-weight Qwen decision model with calibrated probabilities, training code, REST API, and playground.

Details

External ID
1377548062
Source
GITHUB
Company
—
Product
autojev
Website domain
huggingface.co
Launched
Sept. 19, 2026
Cohort
—
Upvotes
28
Upvotes percentile
0.7065078145016653
Tags
calibration, classification, pytorch, qwen
Fetched at
Sept. 23, 2026, 5:02 p.m.
Updated at
Sept. 23, 2026, 5:02 p.m.

Enrichment

Theme
decision model runtimes and tools
Vertical
Horizontal
Function
Model & infra
Audience
Developer
AI stance
AI-native
Project type
Hobby / open-source project
Normalized one-liner
calibrated decision model and playground for developers
Manually corrected
False

Could you build this?

Partial While the inference API and playground are simple wrappers, the core artifact is a full-weight fine-tuned 27B parameter multimodal model trained on 73k curated decision examples with calibrated output probabilities.

What it would actually take: A real version requires an H100/H200 compute cluster or high-end cloud instances (vLLM/DeepSpeed/Axolotl/PyTorch), a curated decision/classification dataset of tens of thousands of domain examples, and post-processing calibration algorithms (e.g., temperature scaling). While vibe coding can assemble the training scripts and FastAPI/Gradio harness, model curation, fine-tuning infrastructure, and validation require ML engineering expertise and significant GPU resources.

Competitors

Other products that read as similar to this one — 1152 launches clear the similarity bar, closest 8 shown.

Attention rank: #320 of 1153 (itself plus its competitors, highest first — normalized so YC and Product Hunt are compared fairly).

Launched 325 days after the earliest competitor.

Other launches for this product

Same idea, different domain

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