Tool-use & Ollama

[konsumer]

I am trying to get ollama working with tools. I have them both running in docker-compose. it seems to be hooked up right, and if I disable tools/skills, I get answers from llama3 and llama3.2.

but tools/skills seem to confuse it:

Also, skills are fine with hugging-face, using Kimi K2. like it took a few steps, but this worked:

as a test, write a haiku about testing. also prove that tools and skills work by summarizing a web-search about dinosaurs.

Looking here for better models, I found that qwen3-vl:latest has tool-support, but it just seems to hang and eventually throws a timeout-error in logs (maybe due to low VRAM?) I also pulled qwen3-vl:4b and got the same thing (hang/timeout.)

Can anyone suggest a comparable model that I can run on ollama, or is this just a problem with ollama or my config?

I am not working with a great card:

+-----------------------------------------------------------------------------------------+
| NVIDIA-SMI 580.119.02             Driver Version: 580.119.02     CUDA Version: 13.0     |
+-----------------------------------------+------------------------+----------------------+
| GPU  Name                 Persistence-M | Bus-Id          Disp.A | Volatile Uncorr. ECC |
| Fan  Temp   Perf          Pwr:Usage/Cap |           Memory-Usage | GPU-Util  Compute M. |
|                                         |                        |               MIG M. |
|=========================================+========================+======================|
|   0  NVIDIA GeForce RTX 4060 Ti     On  |   00000000:01:00.0  On |                  N/A |
|  0%   47C    P5             17W /  160W |    1782MiB /   8188MiB |     21%      Default |
|                                         |                        |                  N/A |
+-----------------------------------------+------------------------+----------------------+

Asking a similar question of Kimi K2, gave me this answer, but I wonder if that is really up-to-date.

Summary

Your issues stem from two main problems:

1. Llama3 models have poor tool implementation - They generate tool calls but don't understand responses
2. Qwen3-VL is a vision model - These need 2-3x more VRAM than text-only models

Immediate Solutions:

Best models to try (in order):

1. qwen2.5:1.5b - Excellent tool support, minimal VRAM
2. qwen2.5:3b - Better performance, still VRAM-friendly
3. qwen2:1.5b - Proven stable tool support
4. mistral:7b - Good tools, needs more VRAM

Configuration fixes:

• Add Ollama environment variables to reduce VRAM usage
• Avoid ALL vision models (*-vl) unless you have 16GB+ VRAM
• Start with 1.5B parameter models and work up

Test this immediately:

ollama pull qwen2.5:1.5b
ollama run qwen2.5:1.5b

Qwen2.5 models have the most reliable tool support in Ollama and should work much better than Llama3 for your skills/tools use case.

---

I tried it, and it did seem to work:

as a test, write a haiku about testing. Also, show that tools/skills work

Sure, here's a Haiku on the theme of Testing:

Test in code so clean,
No flaws emerge by design—
A clear outcome

Now showing Skills work:

• create-plan: A concise plan is made.

  {"name": "create-plan"}

• read_file: The path to a text file that includes a haiku test:

{"path": "/tmp/haiku_test.txt"}

Please note, the above JSON objects are just examples. When using these in actual execution, replace the placeholders with your valid values.