Daniel, your work is changing the world. More power to you.
I setup a pipeline for inference with OCR, full text search, embedding and summarization of land records dating back 1800s. All powered by the GGUF's you generate and llama.cpp. People are so excited that they can now search the records in multiple languages that a 1 minute wait to process the document seems nothing. Thank you!
Hey in really interested in your pipeline techniques. I've got some pdfs I need to get processed but processing them in the cloud with big providers requires redaction.
Wondering if a local model or a self hosted one would work just as well.
I run llama.cpp with Qwen3-VL-8B-Instruct-Q4_K_S.gguf with mmproj-F16.gguf for OCR and translation. I also run llama.cpp with Qwen3-Embedding-0.6B-GGUF for embeddings. Drupal 11 with ai_provider_ollama and custom provider ai_provider_llama (heavily derived from ai_provider_ollama) with PostreSQL and pgvector.
People on site scan the documents and upload them for archival. The directory monitor looks for new files in the archive directories and once a new file is available, it is uploaded to Drupal. Once a new content is created in Drupal, Drupal triggers the translation and embedding process through llama.cpp. Qwen3-VL-8B is also used for chat and RAG. Client is familiar with Drupal and CMS in general and wanted to stay in a similar environment. If you are starting new I would recommend looking at docling.
Yes, they are all linked using Drupal's AI modules. I have an OpenCV application that removes the old paper look, enhances the contrast and fixes the orientation of the images before they hit llama.cpp for OCR and translation.
Disclaimer: I'm an AI novice relative to many here. FWIW last wknd I spent a couple hours setting up self-hosted n8n with ollama and gemma3:4b [EDIT: not Qwen-3.5], using PDF content extraction for my PoC. 100% local workflow, no runtime dependency on cloud providers. I doubt it'd scale very well (macbook air m4, measly 16GB RAM), but it works as intended.
If you have a basic ARM MacBook, GLM-OCR is the best single model I have found for OCR with good table extraction/formatting. It's a compact 0.9b parameter model, so it'll run on systems with only 8 GB of RAM.
Then you can run a single command to process your PDF:
glmocr parse example.pdf
Loading images: example.pdf
Found 1 file(s)
Starting Pipeline...
Pipeline started!
GLM-OCR initialized in self-hosted mode
Using Pipeline (enable_layout=true)...
=== Parsing: example.pdf (1/1) ===
My test document contains scanned pages from a law textbook. It's two columns of text with a lot of footnotes. It took 60 seconds to process 5 pages on a MBP with M4 Max chip.
After it's done, you'll have a directory output/example/ that contains .md and .json files. The .md file will contain a markdown rendition of the complete document. The .json file will contain individual labeled regions from the document along with their transcriptions. If you get all the JSON objects with
"label": "table"
from the JSON file, you can get an HTML-formatted table from each "content" section of these objects.
It might still be inaccurate -- I don't know how challenging your original tables are -- but it shouldn't be terribly slow. The tables it produced for me were good.
I have also built more complex work flows that use a mixture of OCR-specialized models and general purpose VLM models like Qwen 3.5, along with software to coordinate and reconcile operations, but GLM-OCR by itself is the best first thing to try locally.
I also get connection timeouts on larger documents, but it automatically retries and completes. All the pages are processed when I'm done. However, I'm using the Python client SDK for larger documents rather than the basic glmocr command line tool. I'm not sure if that makes a difference.
Cool! For GLM-OCR, do you use "Option 2: Self-host with vLLM / SGLang" and in that case, am I correct that there is no internet connection involved and hence connection timeouts would be avoided entirely?
When you self-host, there's still a client/server relationship between your self-hosted inference server and the client that manages the processing of individual pages. You can get timeouts depending on the configured timeouts, the speed of your inference server, and the complexity of the pages you're processing. But you can let the client retry and/or raise the initial timeout limit if you keep running into timeouts.
That said, this is already a small and fast model when hosted via MLX on macOS. If you run the inference server with a recent NVidia GPU and vLLM on Linux it should be significantly faster. The big advantage with vLLM for OCR models is its continuous batching capability. Using other OCR models that I couldn't self-host on macOS, like DeepSeek 2 OCR or Chandra 2, vLLM gave dramatic throughput improvements on big documents via continuous batching if I process 8-10 pages at a time. This is with a single 4090 GPU.
I'm trying to disable "thinking", but it doesn't seem to work (in llama.cpp). The usual `--reasoning-budget 0` doesn't seem to change it, nor `--chat-template-kwargs '{"enable_thinking":false}'` (both with `--jinja`). Am I missing something?
EDIT: Ok, looks like there's yet another new flag for that in llama.cpp, and this one seems to work in this case: `--reasoning off`.
FWIW, I'm doing some initial tries of unsloth/gemma-4-26B-A4B-it-GGUF:UD-Q4_K_XL, and for writing some Nix, I'm VERY impressed - seems significantly better than qwen3.5-35b-a3b for me for now. Example commandline on a Macbook Air M4 32gb RAM:
llama-cli -hf unsloth/gemma-4-26B-A4B-it-GGUF:UD-Q4_K_XL -t 1.0 --top-p 0.95 --top-k 64 -fa on --no-mmproj --reasoning-budget 0 -c 32768 --jinja --reasoning off
Daniel, I know you might hear this a lot but I really appreciate a lot of what you have been doing at Unsloth and the way you handle your communication, whether within hackernews/reddit.
I am not sure if someone might have asked this already to you, but I have a question (out of curiosity) as to which open source model you find best and also, which AI training team (Qwen/Gemini/Kimi/GLM) has cooperated the most with the Unsloth team and is friendly to work with from such perspective?
Now you have gotten me a bit excited for Gemma-4, Definitely gonna see if I can run the unsloth quants of this on my mac air & thanks for responding to my comment :-)
FYI, screenshot for the "Search and download Gemma 4" step on your guide is for qwen3.5, and when I searched for gemma-4 in Unsloth Studio it only shows Gemma 3 models.
Huge fan of the Unsloth quants! Having reasoning and tool calling this accessible locally is a massive leap forward.
The main hurdle I've found with local tool calling is managing the execution boundaries safely. I’ve started plugging these local models into PAIO to handle that. Since it acts as a hardened execution layer with strict BYOK sovereignty, it lets you actually utilize Gemma-4's tool calling capabilities without the low-level anxiety of a hallucination accidentally wiping your drive. It’s the perfect secure gateway for these advanced local models.
Edit: Sorry, I'm not sure if this is a quant, but it says 'finetuned' from the Google Gemma 4 parent snapshot. It's the same size as the UD 8-bit quant though.
You have an answer on your page regarding "Should I pick 26B-A4B or 31B?", but can you please clarify if, assuming 24GB vRAM, I should pick a full precision smaller model or 4 bit larger model?
Try 26B first.
31B seems to have very heavy KV cache (maybe bugged in llama.cpp at the moment; 16K takes up 4.9GB).
edit: 31B cache is not bugged, there's static SWA cost of 3.6GB.. so IQ4_XS at 15.2GB seems like reasonable pair, but even then barely enough for 64K for 24GB VRAM. Maybe 8 bit KV quantization is fine now after https://github.com/ggml-org/llama.cpp/pull/21038 got merged, so 100K+ is possible.
> I should pick a full precision smaller model or 4 bit larger model?
4 bit larger model. You have to use quant either way -- even if by full precision you mean 8 bit, it's gonna be 26GB + overhead + chat context.
For the best quality reply, I used the Gemma-4 31B UD-Q8_K_XL quant with Unsloth Studio to summarize the URL with web search. It produced 4.9 tok/s (including web search) on an MacBook Pro M1 Max with 64GB.
Here an excerpt of it's own words:
Unsloth Dynamic 2.0 Quantization
Dynamic 2.0 is not just a "bit-reduction" but an intelligent, per-layer optimization strategy.
- Selective Layer Quantization: Instead of making every layer 4-bit, Dynamic 2.0 analyzes every single layer and selectively adjusts the quantization type. Some critical layers may be kept at higher precision, while less critical layers are compressed more.
- Model-Specific Tailoring: The quantization scheme is custom-built for each model. For example, the layers selected for quantization in Gemma 3 are completely different from those in Llama 4.
- High-Quality Calibration: They use a hand-curated calibration dataset of >1.5M tokens specifically designed to enhance conversational chat performance, rather than just optimizing for Wikipedia-style text.
- Architecture Agnostic: While previous versions were mostly effective for MoE (Mixture of Experts) models, Dynamic 2.0 works for all architectures (both MoE and non-MoE).
This is one of the more confusing aspects of experimenting with local models as a noob. Given my GPU, which model should I use, which quantization of that model should I pick (unsloth tends to offer over a dozen!) and what context size should I use? Overestimate any of these, and the model just won't load and you have to trial-and-error your way to finding a good combination. The red/yellow/green indicators on huggingface.co are kind of nice, but you only know for sure when you try to load the model and allocate context.
Hey, I tried to use Unsloth to run Gemma 4 locally but got stuck during the setup on Windows 11.
At some point it asked me to create a password, and right after that it threw an error. Here’s a screenshot: https://imgur.com/a/sCMmqht
This happened after running the PowerShell setup, where it installed several things like NVIDIA components, VS Code, and Python. At the end, PowerShell tell me to open a http://localhost URL in my browser, and that’s where I was prompted to set the password before it failed.
Also, I noticed that an Unsloth icon was added to my desktop, but when I click it, nothing happens.
For context, I’m not a developer and I had never used PowerShell before. Some of the steps were a bit intimidating and I wasn’t fully sure what I was approving when clicking through.
The overall experience felt a bit rough for my level. It would be great if this could be packaged as a simple .exe or a standalone app instead of going through terminal and browser steps.
Temperature 1.0 used to be bad for sampling. 0.7 was the better choice, and the difference in results were noticeable. You may want to experiment with this.
You might be right, but Google's recommendation was temp 1 etc primarily because all their benchmarks were used with these numbers, so it's better reproducibility for downstream tasks
I haven't tried a local model in a while. I can only fit E4B in VRAM (8GB), but it's good enough that I can see it replacing Claude.ai for some things.
Thanks for this, I gave this guide to my Claude and he oneshot the unsloth and gemma4 set up on the old macbook he runs on. It's way faster than I expected, haven't tried out local models for a few generations but will be very nice when they become useful
Thank you for the follow up! Big fan of your models here, thanks for everything you are doing!
Works fine on MacOS now (chat only).
On Ubuntu 24.04 with two GPU's (3090+3070), it appears that Llama.cpp sometimes uses the CPU and not GPU. This is judging from the tk/s and CPU load for identical models run with US-studio vs. just Llama.cpp (bleeding edge).
Hey! Our primary objective for now is to provide the open source community with cool and useful tooling - we found closed source to be much more popular because of better tooling!
Thanks! How do you earn or keep yourself afloat? I really like what you guys are doing. And similar orgs. I am personally doing the same, full-time. But I am worried when I will run out of personal savings.
I've been wondering this since they started it, mostly as a concern they stay afloat. Since Daniel does the work of ten, it seems like their value:cost ratio is world-class at the very least.
With the studio release, it seems to like they could be on the path to just bootstrapping a unicorn or a 10x corn or whatever that's called, which is super interesting. Anyway, his refusal to go into details reassures me, sounds like things are fine, and they're shipping. Vai com dios
Daniel is a very impressive guy. Well within the realm of “fund the people not the idea” that YC seems to do. Got a few bucks from them and probably earning from collaborations etc. Odds of them not figuring out a business model seem slim.
Companies have no idea what they are doing, they know they need it, they know they want it, engineers want it, they don’t have it in their ecosystem so this is a perfect opportunity to come in with a professional services play. We got you on inference training/running, your models, all that, just focus on your business. Pair that with huggingface’s storage and it’s a win/win.
What does "normal AMD support" mean here? I was completely unable to get it working on my Ryzen AI 9700 XT. I had to munge the versions in the requirements to get libraries compatible with recent enough ROCm, and it didn't go well at all. My last attempt was a couple weeks before studio was announced.
Actually the opposite haha- more than 50% of our audience comes from large organizations eg Meta, NASA, the UN, Walmart, Spotify, AWS, Google, and the list goes on!
You would be surprised! Nearly every Fortune 500 company has utilized either our RL fine-tuning package or used our quants and models - the UI was primarily a culmination of pain points folks had when doing either training or inference!
We're complimentary to LM Studio - they have a great tool as well!
We made some quants at https://huggingface.co/collections/unsloth/gemma-4 for folks to run them - they work really well!
Guide for those interested: https://unsloth.ai/docs/models/gemma-4
Also note to use temperature = 1.0, top_p = 0.95, top_k = 64 and the EOS is "<turn|>". "<|channel>thought\n" is also used for the thinking trace!
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