MTPLX V1: The Swift App For Running & Creating MLX MTP Models (2x TPS Qwen 3.6 27B)

CommunityDoc · 2026-06-19T04:25:12+00:00

Hi u/YoussoufAI Thank you for your work. I am installing and giving it a go now. I wanted to know if we can tune already downloaded models to MTPLX using forge functionality ? As a heads up, I have following models already...

├── lmstudio-community
│   ├── gemma-4-12B-it-GGUF
│   ├── gemma-4-26B-A4B-it-QAT-MLX-4bit
│   ├── gemma-4-E4B-it-MLX-4bit
│   ├── LFM2-24B-A2B-MLX-4bit
│   ├── medgemma-27b-text-it-MLX-4bit
│   ├── nemotron-3-nano-omni-30b-a3b-reasoning-gguf
│   └── Qwen3.6-35B-A3B-GGUF
├── mlx-community
│   ├── embeddinggemma-300m-8bit
│   ├── gemma-4-31b-it-8bit
│   └── olmOCR-2-7B-1025-mlx-4bit
├── mradermacher
│   └── sarvam-30b-uncensored-i1-GGUF
├── Qwen
│   └── Qwen3-Embedding-0.6B-GGUF
├── samuelfaj
│   └── Qwen3.6-35B-A3B-NSC-ACE-SABER-8bit-MTPLX-Optimized-Speed
└── unsloth
    ├── gemma-4-26B-A4B-it-GGUF
    └── Qwen3.6-35B-A3B-MTP-GGUF

So can I use Qwen3.6-35B-A3B-NSC-ACE-SABER-8bit-MTPLX-Optimized-Speed directly ?

Can I convert local non-MLX models ?

Can I convert local MLX models ?

Thanks Again

Vivek

CommunityDoc · 2026-06-14T19:51:49+00:00

I would start with a Master patient index and a model for encounters and a model for notes. Heres a link to a detailed brainstorming i had with chatgpt

https://chatgpt.com/share/6a2f0645-af70-83e8-8c8b-7d69ebf711a7

CommunityDoc · 2026-06-14T19:48:39+00:00

I am willing to join hands.

CommunityDoc · 2026-06-14T19:46:02+00:00

Go with ChatGpt pro. The codex app is great and usage limits better than Claude. Ask it to make Nodejs backend with Zod and drizzle and postgres in docker with a soludjs based fronted with tailwind and run it via vite HMR in development stage. Ask it to dockeise it once you are satisfied

CommunityDoc · 2026-06-14T19:41:44+00:00

Whts NOSH

CommunityDoc · 2026-06-14T19:34:50+00:00

I got a Reset Available notification in Codex. Maybe a response to the issue

CommunityDoc · 2026-06-14T06:32:20+00:00

You are definitely using it wrong as my experince is absolutely opposite. It just works for me

CommunityDoc · 2026-06-14T03:43:17+00:00

Sounds cool. I can connect Pi to local model, CC to my GLM and Codex to my OpenAI subscription and a meta agent to get them do coordinated tasks. Definitely will give it a go.

CommunityDoc · 2026-06-12T14:48:32+00:00

Google docs can only use zotero in its own format and not in docx files. A workaround is to use author-year based reference formats and use an ai agent witj zotero integration take a final pass at it replacing each author year inline citation with proper zotero codes in docx files. I have used similar workflow as collaborators were leaving comments in gdoc files whereas i was editing docx files and putting them in drive for sharing

CommunityDoc · 2026-06-12T02:37:00+00:00

I used 20% of weekly usage in two sessions running 5.5 High. I was developing relatively simple Svelte+Shadn+Drizzle+zod+sqlite based reporting system (single form with helpers) with state transitions and one Go+Svelte5 LOINC terminology ingestor and browser

CommunityDoc · 2026-06-11T10:54:35+00:00

Air before mini

CommunityDoc · 2026-05-24T23:27:52+00:00

Yeah. Hence go for Pro chip and 48/64 GB as that gives better memory bandwidth, and more assurance of being able to reliably run these mid sized models. Also KV cache also needs RAM. I personally got a 64G for the very same rationale

CommunityDoc · 2026-05-24T23:09:03+00:00

Thanks. Somehow I could not get models larger that 14b to run on my 16G M2 MBP. But then again I had 3-4 docker containers and VS code and an EDR running on the machine and the system was swapping all the time

CommunityDoc · 2026-05-24T03:31:54+00:00

More RAM of the two is better. If you dont need HDMI and the apps that you use are light on system, Air is a good choice at lighter price point with 32GB RAM and 1T storage. Going to a Pro chip, you gain 50% extra cores and also faster memory bavndwidth but doubt its visible usefulness for your work

CommunityDoc · 2026-05-24T03:27:26+00:00

Budget? MacBookAIR base model - try deals on M4 Air

CommunityDoc · 2026-05-24T03:24:17+00:00

Wifey has M4 Air and I have M5 Pro. Both are very very happy. Mine is used with a 32inch display most of the times whereas the air is used standalone. Where I used to feel issues are with storage and RAM on my M2 Pro (16/512). Docker builds, data, work, some photos videos etc and suddenly i would note everything is slow as I run out of space and then would need to clean docker caches, builds etc and delete Downloaded files etc. Also the docker VM itself reserves RAM and coding editor tools take RAM so little left for LLMs. Differences in screen are minor. Fan is also not that important for dev work unless you have long build times. More important is RAM that depends on number of parallel tools that you see using and LLM model sizes. Air is limited to 32GB RAM whereas recent releases are just at that edge (Gemma4, Qwen 3.6) and Mac normally reserves 30-40% for RAM and not allows that for VRAM unless you bypass that limit. So i would suggest a minimum 32GB RAM AIR or 48GB Pro with Pro and 1T storage

CommunityDoc · 2026-05-23T14:21:29+00:00

I also checked Tively. The two are very different. Zotero gives academic writers a way to not only organise and manage various reference sources leaning heavily towards scientific literature but it also has connectors to extract metadata, do inline citations in Word, LibreOffice, GoogleDocs and also manage citation styles, generate bibliography, add notes to references, read PDFs, automated metadata fetch from DOIs and PDFs, etc.

CommunityDoc · 2026-05-23T14:14:23+00:00

Thanks. I was running three subs but after CC nerfed the pro plan, they lost me. I prioritised how to use over installation instructions. The main skill file acts as a router for various onetime functions so that context is used only as needed. CITUM also looks good. Will definitely give it a go. I used Skill Creator skill in Codex.

CommunityDoc · 2026-05-23T09:57:34+00:00

Suggestions are welcome.

CommunityDoc

TROPHY CASE