Together AI's open recipe and weights for a Jev-inspired decision model fine-tuned on Qwen3.5-4B, with the full data pipeline, training config, and saved benchmark results.
Trained on 37,840 unique examples with 4,568 held out for validation.
LoRA SFT at rank 8 for one epoch, learning rate 5e-5, capped at 2,048 tokens per example.
Saved results: 880 of 1,000 main decisions and 300 of 300 policy-transfer decisions.
Weights are published on Hugging Face; Together's blog frames training your own classifier at about $17.
The model answers only the option letter, so returned logprobs are preferences, not calibrated confidence.
Quickstart
bash
uv sync --locked
uv run python fetch_sources.py
uv run python build_all.py
Watch out
Training needs a Together API key and `--launch` starts a billed job. The saved results reuse dev sets rather than untouched tests, and the dataset and weights carry their own terms separate from the MIT code.
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Hacker News: Jev implemented in 25 lines of Python
you can make any open source model behave like jev with just a bit of inference engineering.
it's shockingly easy. to prove it, we built a new endpoint we're calling deepseek-v4.1-flash-jev. see the demo below. here's how it's done:
sglang (an inference engine) offers aShow more
A reproduction of Jev that turns any Qwen checkpoint into a decision model serving the same /v1/systemone schema (Choice, Score, Noul), with no training and no generated answer text.
Open multilingual System 1 decision models with published checkpoints for choice, score and noul questions, plus a router that dispatches each request to the right checkpoint in one forward pass.
An open 0.6B replica of Jev that turns states and questions into full probability distributions without decoding answer tokens, trained and evaluated on Maze, Snake, and ViZDoom.
Breaking: Browser Use + Jev = Ultrafast ⚡
Findings flights took 7s and cost only $0.0039 🤯
> new action space every step
> DOM state space
> small LLM fallback to type
(this video is at 1x speed btw)
Built a tiny open source browser agent. try it below ↓
hype-free explanation of jev:
jev does not replace gpt / claude
jev is just a *really* smart switch statement
like if 2016 ml classifiers got 2026 levels of intelligence
it's a new* type of tool that will make a lot of workloads insanely fast, cheap, and accurate
* = and byShow more
Diogo Almeida
@CompleteSkeptic
After co-inventing ChatGPT, I kept asking myself: why have superhuman chat models not led to AGI?
I’ve spent the last 2 years in stealth building a new way to train models (RLCD), and a new type of frontier AI model that we are releasing today: Jev
• 20-200x faster
• 40-400x
Full Jev Tutorial
What it is, how you can build with it and what new applications it can unlock
→ 0:00 Intro
→ 0:34 Jev explained
→ 4:06 API setup
→ 5:59 Demo 1: Voice-controlled browser
→ 11:33 Demo 2: AI memory
→ 17:27 Demo 3: YouTube predictor
This is a terrible compaction strategy that fundamentally doesn't understand how compaction and context management work.
Seems like a lot of people are confused so let's break this down.
1. Compaction isn't a filter
The role of compaction is to clean up history to keep theShow more
tamara
@tamarajtran
found the perfect use case for @typesafeai Jev:
instant compaction
in 2026, why is compaction still a summarization prompt?
Jev can make it instant by scoring every tool call and dropping what’s irrelevant
I made a DuckDB extension where you can use @typesafeai 's Jev to do quick classification of rows in any csv/parquet file or duckdb table
about 10sec for 1k rows ~ better than using an LLM, way more ergonomic than a classifier
game-changing for data analysis!