Does the hosted demo tell me anything about Jev's screening quality?

No. It screens with local rules and trains a small byte-bigram statistical model, and the README says outright that this does not demonstrate Jev's judgment quality or real language-model training performance. Wire up your own rubric and screening provider to exercise the live path.

What does running GRPO or PPO cost me in setup?

SFT uses the built-in LoRA trainer, so it stays in the main install. GRPO and PPO move to a separate pinned verl environment with its own workers, and a shared training bundle can be launched with --n-gpus on one Linux or NVIDIA host; the multi-GPU guide covers batch sizing and memory.

How strict is the record format it will accept?

Pick one layout per record: text, instruction and output, prompt and response, or messages. Content must be text, and if a record mixes layouts the fields are selected in a fixed order rather than combined, so do not rely on mixed fields turning into training content.

How do I run the workbench locally?

Python 3.10+, Git and internet for install: clone the repo, python3 -m venv .venv, pip install -e ., then jev-dataops serve and open localhost:8000. Start with screening and training both set to Demo.

What data can I upload and how is it split?

UTF-8 JSONL or CSV in one of four layouts per record, with a group_id or conversation_id keeping a conversation in one split. The default split is about 70/15/15 by group, and training needs at least 6 independent groups after screening.

What does a run save to disk?

keep.jsonl, review.jsonl and reject.jsonl for where each record went, audit.jsonl for why, data_report.json for counts and settings, training_report.json for what trained, and model_report.json for before and after loss and perplexity, all under .jev-dataops/.

CommunityJev Practices & Patterns

Jev Dataops

Q: What does the model evaluation step actually measure?

Loss and perplexity on the same held-out split before and after training. The README is explicit that it does not measure business task accuracy, collect human ratings, or deploy models, so read it as a training-signal check and bring an independent task benchmark.

Self-hosted workbench that screens JSONL or CSV into keep, review and reject partitions, then fine-tunes your own model and reports loss and perplexity on a held-out split.

See highlights

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Stars: 62
Language: Python
License: MIT
Published by: Community
Author: RenaGao
Last verified: Oct 2026

RenaGao/jev-dataops

User repository on GitHub

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An open-source JEV-powered workbench for streaming data selection, quality evaluation, automatic LoRA training and held-out model evaluation.

Forks: 7
Last pushed: Updated Oct 2026

Repo stats from the GitHub API, cached Oct 2026.Homepage

About this project

Turn a dataset into a training experiment you can inspect. Upload your data, use JEV to decide what to keep, fine-tune your model, and compare the results—all in one workbench. Every run saves the screening decisions, dataset splits, and evaluation reports so you can see what happened. Try the demo · Website · Get started locally · User guide. Open the interactive demo → No installation, API key, or GPU needed. Click Use example to load the bundled dataset. Click Start workflow with the default settings.

Quoted from the project's README. Read the full README.

Tagscommunity awesomejev

Works forJev for Classification Jev for Routing Jev for Evaluation

Used inJev for Software & DevTools

Languages: Python

Highlights

Five stages end to end: upload, screen, evaluate the data, train, then evaluate the model on the same held-out split.
Screening keeps local format, length and exact-duplicate checks, while live mode adds Jev quality judgments behind a rubric.
Ships general, finance and code rubrics plus six downloadable metric packs covering legal, medical, enterprise knowledge and code.
SFT uses the built-in LoRA trainer; GRPO and PPO run in a separate pinned verl environment with reference-answer rewards.

Watch out

Model evaluation reports loss and perplexity only; it does not measure business-task accuracy, collect human ratings, or deploy anything. The public browser demo screens with local rules and a byte-bigram statistical model and never calls Jev, so a first run proves the workflow rather than screening quality. The starter rubrics cover quality, privacy and trainability, with no fact-checking or code-execution tests. Training needs an NVIDIA GPU on Linux or macOS with Python 3.10+, and at least six independent groups must survive screening. Copy reviewed against the repository README on 2026-10-04, not from running it.

Questions about this entry

Short answers drawn from the linked project or page.

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