As a regular user of a bunch of specialized micromodels, I'll tell you this: you won't be happy with such a model (and its JEV counterparts) running permanently in the background on your PC's CPU. You need to offload their processing to the NPU. There are many pitfalls along the way, but the result is worth it.

NPU performance will be twice as high, while power consumption will be four times lower. No additional fan noise (if you know what I mean).

I'll wait another month until the first phase of the =battle royale= among models of this kind wraps up, put together a solution for the NPU/iGPU, and post it on HF.

At this point I can't wait for a comedian to release a decision model backed by humans.

Meet Jerry- it's literally a guy named Jerry answering your questions.

Why is everyone calling binary choices `noul`? Does this have some meaning or is it just copying Jev’s API?

Does anyone know if it is worth fine-tuning one of these decision models on the shape of the questions you want it to work on, vs the more general versions? I'm using Jev pretty successfully at work at the moment, but am curious about what is doable

Fantastically well written. It's rare for me to be able to understand what the AI gurus are talking about, and this was written by humans for humans.

It can technically be used for a lot of use cases, I'd like people to chime in on ideas on this?

The ~2-week-old Intern-Decision family of models (0.8B, 2B and 4B) have the same Qwen3.5 base model family (albeit the instruction-tuned variants) and pointer head architecture.

https://huggingface.co/collections/internlm/intern-decision

Can someone explain this architecture a bit more in depth? They say the pointer head scores the hidden state at each option against the hidden state of the answer. But the LLM produces hidden states per token, so an option can span multiple tokens, no?

clef from cloudflare runs on llama.cpp - being locked-in to strands cli would be a bummer and will slow down adoption.

Since it's a LoRa on Qwen, I assume this is runnable via llama.cpp. Pity that the PEFT/LoRa->GGUF translation is left to the user. Anyone got past:

    $ uv run --with transformers==5.19.0 convert_lora_to_gguf.py ~/Downloads/lora --dry-run --verbose
    [...]
      File "/Users/user/repos/llama.cpp/conversion/base.py", line 630, in map_tensor_name
    raise ValueError(f"Can not map tensor {name!r}")
    ValueError: Can not map tensor 'layers.0.linear_attn.in_proj_a.weight'

Are any of these multimodal yet? I'd love to try asking a model with calibrated probabilities to answer question like, "do these shapes match?". Sure, you can ask a LLM....

This is a great model. I've been running it on device in chrome extension to filter things like email.

It is just the right mix of size, capability and speed to make it generally useful for adhoc bulk classification tasks.

For those wanting to run it in browser: https://huggingface.co/alxnahas/strands-decider-2B-webgpu

Benchmark calibration does not establish reliability on unfamiliar production inputs.

Any idea how well this would run on a CPU?

Looks really nice, I think I could use it on my Mac mini for some smaller automations

2B being called small is such a sign of the times

a strand type game