They're definitely not the only one. I've been building on relational transformers, which does prediction and classification over relational data (it handles numeric types better). It's validating to see that these small models that do prediction tasks are so useful to the community, but also stings a little that it was so hard for me to communicate how game changing they are.

Is this at all possible to run locally on a MacBook pro m5 (48gb ram)? What kind of performance could I expect? Or would you run this somewhere in the cloud? What HW / which provider would you choose (single user for exploration only)

Jev is mostly a cost optimization and some good plumbing, I don't think it's breakthrough of naything

been loving hacking on this. just created a vision version of it here https://huggingface.co/thaitea/laya-vision-smolvlm-256m

Was this ArXiv pre-print published anywhere (i.e., with proper peer review)?

“Codex, build a novel frontier model and post it on HackerNews —”

“Claude, roast this noob, tell him that his model isn’t novel or frontier —”

both in unison “— and make no mistakes!”

It’s all so tiresome

I was wondering, do you think its possible to use something like SAM 3 (segment anything from FB) + Laya to create a super efficient and fast computer use tool?

Distribution > Creation

Correct me if I am wrong, can I use Jev and this tool for ticket classification? I mean, for instance, a level 1 ticket contains a screenshot of the login page that displays an error, LLM can do it perfectly, can Jev do it?

I don’t understand Jev or this. I used this since it’s open source (good job btw!) with the following. State: “a 6 sided die rolled a 3”, question (noul): “Is the number odd?”

Answer: 9% chance, with 91% confidence.

Heh???

Ok, even worse. 75% chance a coin landed heads up?

State: I flipped a coin. Question:

{ "noul_result": { "type": "noul", "instructions": "Did the coin land heads up?" }, "choice_result": { "type": "choice", "instructions": "Determine if the coin landed heads or tails up.", "criteria": { "heads": "the coin landed heads up", "tails": "the coin landed tails up" } } }

Ran on: https://huggingface.co/spaces/convaiinnovations/laya-demo

Result: { "model": "laya", "answers": { "noul_result": { "type": "noul", "noul": 0.6839, "rl_agent": { "act_probability": 1.0 } }, "choice_result": { "type": "choice", "choice": "heads", "probabilities": { "heads": 0.7407, "tails": 0.2593 }, "confidence": 0.1743, "rl_agent": { "act_probability": 1.0 } } }, "usage": { "input_tokens": 76, "output_tokens": 0 }, "latency_ms": 93.8 }

Trying to be even more good-faith:

State: "A fair coin was flipped once. The result was not observed. No other information about the outcome is available."

Questions: { "noul_result": { "type": "noul", "instructions": "Given only the supplied state, what is the probability that the coin landed heads up?" }, "choice_result": { "type": "choice", "instructions": "Given only the supplied state, determine which outcome occurred.", "criteria": { "heads": "the coin landed heads up", "tails": "the coin landed tails up" } } }

Result:

{ "model": "laya", "answers": { "noul_result": { "type": "noul", "noul": 0.1265, "rl_agent": { "act_probability": 1.0 } }, "choice_result": { "type": "choice", "choice": "tails", "probabilities": { "heads": 0.2522, "tails": 0.7478 }, "confidence": 0.1853, "rl_agent": { "act_probability": 1.0 } } }, "usage": { "input_tokens": 123, "output_tokens": 0 }, "latency_ms": 154.5 }

    python3 - <<'EOF'
    import json, urllib.request
    body = json.dumps({
        "state": "The car wash is only 100 meters away from my house.",
        "model": "jev-1.13-free",
        "questions": {"q": {"type": "choice",
            "instructions": "Should I drive or walk to the car wash?",
            "criteria": {"drive a car": None, "walk": None}}}
    }).encode()
    req = urllib.request.Request("https://opencode.ai/zen/v1/systemone", data=body,
        headers={"Content-Type": "application/json", "User-Agent": "opencode/1.18.31"})
    with urllib.request.urlopen(req, timeout=60) as r:
        print(json.dumps(json.load(r)["answers"]["q"], indent=2))
    EOF
    {
      "type": "choice",
      "choice": "walk",
      "confidence": 0.66,
      "probabilities": {
        "walk": 0.83,
        "drive a car": 0.17
      }
    }