u/arkham00

Hi, can someone explain to me how to properly setup the reasoning effort in pi agent to reflect the levels of the model, I believe that omlx supports it now right ?

I put in models.json this:

"thinkingLevelMap": {
            "off": "off",
            "low": "low",
            "medium": "medium",
            "xhigh": "xhigh"     
          }

but when I launch it in pi I still have off-minimal-low-medium-high-xhigh, and I'm not sure if there is a rela change between the levels. Off works btw.

Should I be doing something with kwargs or use a different template to be sure that the thinking level are correct? For example I've read about the froggric one, I'm a bit confused about it

Thanks

HI, I have an m2 max, and as we know "float16 gives ~20% faster prefill on M1/M2 Apple Silicon (native fp16)" so I systematically use this flag when I quantize my models.

When oQe came out I started to mix the two thing, but I had this strong feeling that the models got dumber. In omlx I mainly use qwen35b-oQ8-fp16 and Gemma-4-qat-oQ4-fp16.

I found them less accurate, more unpredictable, and I asked deepseek about it, here's what it found :

This is where the collision happens between your speed tweak (--fp16) and the error-correction math (oQe).

When you compile oq8e (Qwen) or oq4e (Gemma) with the --fp16 toggle active, oMLX performs two conflicting actions:

1. The oQe engine calculates a highly precise importance matrix (imatrix) to correct quantization errors based on the model's native BF16 data distribution.
2. The --fp16 flag then forcefully downcasts those base parameters, scale markers, and biases into standard Float16.

Because BF16 and FP16 handle numbers differently (BF16 trades precision for a massive dynamic range, while FP16 has a narrow range but tighter precision), this forced conversion introduces tiny rounding and clipping anomalies.

The sequential Hessian rounding map of oQe relies on absolute, stable mathematical precision to adjust column weights. Forcing the FP16 shift breaks those precise calculations. The model compiles successfully, but its internal logic is slightly skewed—making Qwen 3.6 35B and Gemma 4 31B feel notably dumber.

What do you think? I'm not very knowledgeable about weights and quantization, so I don't know if this is correct or a big hallucination. What do you think ?

Hi, can someone with enough ram kindly provide this quantization ?

I'd really like try to try it on my mac m2 max 96GB, I'm using the dwarfstar variant and it is very good but I have around 170pp and 12tg, and I'm hoping to gain a little speed with omlx and fp16 ^^

Thanks

Hi I'm encountering a weird problem, even if I set disable-model-invocation: true in the skill frontmatter, the skill is still loaded in the prompt. I did /reload, launched a fresh pi session, restarted the pc... Nothing helps. If I do /export and check the system prompt, I still find the skill loaded.

Here's the frontmatter of one of my skills :

---
name: pi-history-ingest
description: blablabla
disable-model-invocation: true
---

And here's the XML loaded in the system prompt after an export of a fresh loaded session:

<skill>
    <name>pi-history-ingest</name>
    <description>Ingest Pi coding agent session history into the Obsidian wiki. Use this skill when the user wants to mine their past Pi sessions for knowledge, import their ~/.pi/agent/sessions folder, extract insights from previous coding sessions, or says things like "process my Pi history", "add my Pi sessions to the wiki", "ingest ~/.pi", or "what have I worked on in Pi". Also triggers when the user mentions Pi sessions, Pi agent history, ~/.pi/agent/sessions, or Pi conversation logs.
</description>
    <location>/Users/dani/.pi/agent/skills/pi-history-ingest/SKILL.md</location>
  </skill>

The skill is not listed in setting.json

I spent the afternoon trying to find a solution asking pi (with deepseek) to no avail, it checked loadSkills, formatSkillsForPrompt, even tried to simulate the filtering with a js script and it seemed to work fine, but in the end it doesn't work for me ...

Do you have any suggestions? What should I check ?

Could it be a bug and should I open an issue ?

Thanks

Hi, i'm running pi local with different models of the qwen and gemma family, and half the time they try to do an edit they encounter this error, what does it mean ? And how can I instruct them to better use the tool ?

Thanks

Hi, I wanted to try a ASR model, I have long RPG session recorder and I wanted a transcription and then a summary but I hit the limit of 100Mb when I tried to launch curl -X POST "http://localhost:9999/v1/audio/transcriptions
I asked about this to an IA and found that in audio_routes.py there is :
# Maximum upload size for audio files (100 MB).

MAX_AUDIO_UPLOAD_BYTES = 100 * 1024 * 1024

Why is that? it is pretty annoying ...what's the matter with larger files ?

Hi, I wanted to try https://github.com/BlackBeltTechnology/pi-agent-dashboard and it broke my pi ... I already unisntalled it, but I still get Error: 426 Upgrade Required when I send a message to the agent in the terminal
I'm not really sure what this means, I did some research and it could be related to node.js ? but the version didn't change it is still 24.15.0 ...
A also tried to do a pi update --self --force to no avail ...
What can I do, I really don't want to lose all the config I did to the agent ...

Help please ...I can't even ask to pi, I'm lost lol

Hi everyone,

I’m relatively new to the Pi ecosystem and pi-subagents. I should clarify that I’m not a coder; I primarily use LLMs to automate document-heavy and text-based workflows.

Recently, I’ve been experimenting with building custom subagents for quick, focused research. While working through this with Gemma-4-26b, we discovered a way to bypass the need for creating physical markdown files by using outputMode: inline. This allows the agent to pass the research brief directly into the chat context, which is much cleaner and faster for my workflow.

The Problem: The “False Failed” Loop

However, I’ve run into a significant issue with how the orchestrator handles this. Even when the subagent provides a perfect, detailed, and highly accurate report directly in the chat, the Pi orchestrator marks the run as “failed”.

This creates a frustrating chain reaction:

1. The False Signal: Because no tool call (like write) was used to leave a filesystem footprint, the orchestrator interprets the task as “not completed” or “planning/scratchpad” instead of a successful result.
2. The Orchestration Loop: When the parent agent sees this failed status, it interprets it as a genuine execution error. Even though the actual data/report is clearly visible in the previous chat turn, the logic of the parent agent is to “fix” the failure by re-running the subagent.
3. Wasted Resources: This results in the subagent being launched multiple times for the same task, wasting time, compute, and cluttering the chat history with duplicate reports.

My questions for the experts/devs:

1. Is this intended behavior? Is the orchestrator’s completion detection strictly tied to filesystem mutations?
2. How can we signal a successful “text-only” completion so that the parent agent doesn’t feel the need to retry?

I’d love to hear if anyone else has encountered this “loop of confusion” and how you’ve managed to solve it.

Thanks in advance!

Hi,

I'm on a m2 Max 96Gb and was experimenting with qwen3.6 35b with bartowski q8 in llama.cpp

I tried the same translation task from the llama.cpp webchat and I got around pp= 1000 t/s

and tg=40 t/s

Then I restarted llama.cpp with the same parameters and launched pi and sent the same prompt I had the same pp more more less, but around 32-34 t/s in generation, sometimes it even drops at around 20tok, is it normal ?

I tried it several times and the results are consistent.

Why such a slowdown ?

Is it normal to expect a slowdown when we use the model with an harness ?

I've installed and updated several times omlx with the dmg package, now I realise that I'd like to have the cli for harness integration, what's the correct procedure in this case? First uninstall the dmg and then reinstall I suppose? Or can I have the 2 installations live on my computer just in case? And what about homebrew vs pip what do you prefer and why? Brew seems to have the possiblity to launch omlx in the background which is nice Thanks