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repliedto mihailgribov's post about 2 hours ago TypeCastLM: Jev-class decision models on frozen LLMs
TypeCastLM is a decision model built around a frozen LLM. Given a text and a question, it returns calibrated probabilities over the allowed answers in one forward pass. The LLM is not fine-tuned: not one of its weights changes. TypeCastLM swaps its LM head for a mini one: a linear matrix whose rows are mostly the model's own output rows, with the three verdict rows (true, false, unsure) fitted. A fitted row catches an answer the model spreads over many tokens, such as "yes", "true" or "correct", where a single vocabulary row sees one word. A head that small leaves little room to overfit. It speaks the Jev API, so a Jev client switches by changing the base URL.
The first model, [typecastlm-qwen3.5-3.8b](https://huggingface.co/mihailgribov/typecastlm-qwen3.5-3.8b) on Qwen3.5-4B, is among the top open frozen-4B models on JevBench v1.6. Besides Jev's yes/no, choice and scale modes it has `tfu`: yes/no with a third answer, `unsure`, for when the text lacks what the decision needs.
Specs:
• Size: 3.76B params; 7.5 GB bf16, 4.0 GB Q8_0
• VRAM: 12 GB is enough, 16 GB comfortable
• Latency p50 (batch 1, bf16, RTX 5060 Ti): ≤200 tok 48 ms · 1k 93 ms · 4k 566 ms
• Calibration (ECE): BoolQ 0.052 · RTE 0.012 · FEVER 0.019
• Context: 32k tokens
• Runtime: transformers or llama.cpp, fully offline
• JevBench v1.6: 19.7
`pip install typecastlm` repliedto ErenAta00's post about 2 hours ago Maverick 1.1 is out.
When we released the first version, the feedback was clear: real people do not give one clean command at a time. They change their minds, they point and say "put that on the shelf", they ask for a lamp that is in another room, and sometimes the one speaking is a remote expert helping someone on site. 1.1 is built around that.
- Say "no, the other one" and it undoes the last action and repeats it on the other object. If the effect can't be undone, like a button that was already pressed, it tells you.
- It sees what people are looking at or pointing at, and who is speaking.
- It finds objects in other rooms, goes there and acts, or asks which one if there are two.
- It follows a short plan in one sentence: "open the cabinet, put the book and the alarm clock in it, then close it".
In the clip, a remote expert corrects the model, then asks it to highlight what the technician is looking at.
On test sets we sealed before training and ran once, the new abilities score 93 to 100%, where 1.0 scores 33 to 87%. On sentences written by people (the ALFRED set) the two versions are level, at 83.2% and 83.8%.
Where it still falls short: in unfamiliar situations it tends to act when it should ask, and a remote speaker can act in the scene with no permission check, so your app has to enforce that.
Same 4B model and licence (Apache-2.0), the same 11 tools plus `undo`, still offline on a 4 GB laptop GPU. The Unity package for the new scene format (1.1.0) is on GitHub: https://github.com/mcbu-xrlab/Maverick-Unity
Model: https://huggingface.co/ErenAta00/Maverick-4B-Unity-XR-Agent-1.1
Article and full demo: https://huggingface.co/blog/ErenAta00/maverick-1-1-undo-pointing-other-rooms
Built at the Extended Reality Laboratory (XRLab), Manisa Celal Bayar University: https://huggingface.co/ExtendedRealityLabMCBU Organizations
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