Get the latest setups
Models/openjev
Text / openjev

openjev

OpenJev is an open-weights decision model for typed zero-shot classification over text, web pages, and screenshots. It accepts labels at request time and produces choices, yes/no probabilities, or scores without free-form generation. The model supports multiple languages including English, German, French, Hindi, Chinese, and Japanese. Reported latency is about 80 ms for a short text decision and about 210 ms for an isolated web decision on one H100 under specified conditions.

EstimatedRepository opened Sep 20, 2026Source checked 9/23/2026Version: 5ec9e5fd
LOCALRENTED GPUOPEN WEIGHTS
Freshness 83 out of 100. Repository age: 49 days old, Sep 20, 2026. Measures age, not quality.
Popularity 55 out of 100. 9,770 downloads in 30 days; 188 lifetime likes. Checked Nov 7, 2026. Measures adoption, not quality.
What do these scores mean?

Two ways to compare models. Neither score measures answer quality, benchmark performance, or whether a model fits your machine.

Freshness · release age

A new release starts at 100. The score halves every 180 days: 100 × 2^(−age in days / 180). When the release date is unavailable, a repository date is a fallback—not proof of when the model was released. Missing dates stay unscored.

Check the date source
Popularity · adoption

Downloads over the past 30 days carry 80% of the score; lifetime likes carry 20%. Both use a logarithmic scale, capped at 10 million downloads and 100,000 likes. If likes are unavailable, downloads carry all the weight. Missing download statistics stay unscored.

round(80 × min(1, log10(1 + downloads) / 7) + 20 × min(1, log10(1 + likes) / 5))

Check the statistics source
Scores as of Nov 8, 2026. Statistics checked Nov 7, 2026. Statistics are marked for refresh after 72 hours.Full scoring method

At a glance

Parameters
27.36B
Architecture
qwen3_5
License
cc-by-nc-4.0
Disk space
51 GB
Software
vLLM
View the model source
MY HARDWARE

Will it run on your machine?

Save your machine to see a personalized rating and its reasoning.

Add my hardware

Best for

  • Routing and triage decisions such as intent, topic, team, priority, or language
  • Moderation and safety decisions such as toxicity, spam, hate, policy violation, or legal threat
  • Judging other model outputs for groundedness, hallucination, answer quality, or rubric adherence
  • Documents and operations decisions such as invoice approve/hold/reject, alert severity, or contract clause type
  • Browser and desktop agent decisions such as which element, which operation, task completion, or blockage

Tradeoffs

  • Licensed under CC-BY-NC-4.0, which restricts commercial use
  • Reported latency and accuracy figures are from specific configurations and were not independently tested here
  • Up to 52 options per question; exceeding this may require different handling
  • Requires fixed calibration settings for probability conversion
  • Trained or evaluated on specific datasets; not guaranteed to generalize to all domains or languages

Ways to run it

vLLM · Linux

Repository-specific command in publisher documentation; confirm dependencies and hardware in the source.

vllm serve ./openjev --host 127.0.0.1 --served-model-name qwen --port 8000 --enable-prefix-caching --max-model-len 16384 --gpu-memory-utilization 0.90 --limit-mm-per-prompt '{"image":1}' --trust-remote-code --max-num-seqs 256 --max-logprobs 64 --gdn-prefill-backend triton --quantization fp8
Official instructions

Explore its uses

SAFETENSORSZERO-SHOT-CLASSIFICATION

Keep exploring

Text
Prism ML

Bonsai 2 · 27B

A compressed 27B-class reasoning model with publisher-provided GGUF packs and a dedicated llama.cpp fork for CUDA, Metal, and CPU.

Freshness 82 out of 100. Release age: 52 days old, Sep 17, 2026. Measures age, not quality.
Popularity 85 out of 100. 1,710,588 downloads in 30 days; 2,952 lifetime likes. Checked Nov 7, 2026. Measures adoption, not quality.
MODEL SIZE27.36B
LOCALRENTED GPU
Released Sep 17, 2026
Text
DeepSeek

DeepSeek V4.1 Flash

An open-weight model for reasoning, coding, agents, and image understanding. DeepSeek V4.1 Flash combines a 552B backbone with sparse activation and up to one million tokens of context. Choose the original checkpoint or a community uncensored variant below.

Freshness 80 out of 100. Repository age: 59 days old, Sep 10, 2026. Measures age, not quality.
Popularity 87 out of 100. 1,911,780 downloads in 30 days; 4,887 lifetime likes. Checked Nov 7, 2026. Measures adoption, not quality.
MODEL SIZE552B backbone · 8B active prefill / 16B active decode
LOCALRENTED GPU
Repository opened Sep 10, 2026
Text
Qwen

Qwen3.8

Explore 4 versions of Qwen3.8. Compare model sizes, packages, hardware requirements, and available setups.

4 versionsCompare sizes & formats
LOCALRENTED GPU
Compare versions →