Thinking / reasoning + multimodal + tool calling.

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!

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!

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

llama.cpp (b8642) auto-fits ~200k context on this 24GB RX 7900 XTX & it shows a solid 100+ tok/s ("S_TG t/s") on the first 32k of it, nice!

    ./llama-batched-bench -hf unsloth/gemma-4-26B-A4B-it-GGUF:UD-Q4_K_XL \
    -npp 1000,2000,4000,8000,16000,32000,64000,96000,128000 -ntg 128 -npl 1 -c 0
    |    PP |     TG |    B |   N_KV |   T_PP s | S_PP t/s |   T_TG s | S_TG t/s |      T s |    S t/s |
    |-------|--------|------|--------|----------|----------|----------|----------|----------|----------|
    |  1000 |    128 |    1 |   1128 |    0.416 |  2404.87 |    1.064 |   120.29 |    1.480 |   762.20 |
    |  2000 |    128 |    1 |   2128 |    0.755 |  2649.86 |    1.075 |   119.04 |    1.830 |  1162.83 |
    |  4000 |    128 |    1 |   4128 |    1.501 |  2665.72 |    1.093 |   117.08 |    2.594 |  1591.49 |
    |  8000 |    128 |    1 |   8128 |    3.142 |  2545.85 |    1.114 |   114.87 |    4.257 |  1909.47 |
    | 16000 |    128 |    1 |  16128 |    6.908 |  2316.00 |    1.189 |   107.65 |    8.097 |  1991.73 |
    | 32000 |    128 |    1 |  32128 |   16.382 |  1953.31 |    1.278 |   100.12 |   17.661 |  1819.16 |
    | 64000 |    128 |    1 |  64128 |   43.427 |  1473.74 |    1.453 |    88.12 |   44.879 |  1428.89 |
    | 96000 |    128 |    1 |  96128 |   82.227 |  1167.50 |    1.623 |    78.86 |   83.850 |  1146.42 |
    |128000 |    128 |    1 | 128128 |  133.237 |   960.69 |    1.797 |    71.25 |  135.034 |   948.86 |

  and the EOS is "<turn|>". "<|channel>thought\n" is also used for the thinking trace!

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.

Hi! Do you ever make quants of the base models? I'm interested in experimenting with them in non-chat contexts.

Noob question. Why I would use this version over the original model?

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.

Are there any plans to make something like that?

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?

Thank you for your work.

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?

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 and your brother for all the amazing work, it's really inspiring to others <3

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.

How does Gemma 4 26B A4B compare with Qwen3.5 35B A3B for same quants(4)

Wow! Thank you very much!

neat, time to update my spam filter model hehe

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.

Awesome!! Thank you SO much for this.