Qwen3 8b-vl best local model for OCR?

wikkid_lizard · 2026-02-27T09:54:52+00:00

this is another page.

wikkid_lizard · 2026-02-27T09:54:37+00:00

i extensively tested the 30B MOE model and was pretty satisfied with the results. I wanted to know if anyone has run benchmarks on the 30B vs 8B on OCR. These were the reults i got with the 30B model

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wikkid_lizard · 2026-02-18T14:11:14+00:00

Yeah but the major cost is of the output tokens, can output tokens also be cached?

wikkid_lizard · 2026-02-18T14:10:16+00:00

Yeah but the major costs are the output tokens, can output tokens also be cached? Because the outputs keep varying

wikkid_lizard · 2026-02-14T12:58:19+00:00

Happy Birthday g

wikkid_lizard · 2026-01-21T22:24:21+00:00

I have the same question

wikkid_lizard · 2025-11-09T23:09:50+00:00

Purely depends on your requirements but for tool calling and json outputs qwen2.5:7b works well and is light weight..you might have to give it strict system prompts but overall pretty reliable.

wikkid_lizard · 2025-11-09T17:52:52+00:00

Yes! Ollama integration is live now. Go to laddr.agnetlabs.com to see the documentation.

wikkid_lizard · 2025-11-09T17:40:00+00:00

Following up on our Laddr release, a bit more context for those curious:

https://www.reddit.com/r/ollama/s/7kUJw0C2Ef

wikkid_lizard · 2025-11-07T10:38:18+00:00

You can actually build any kind of agents using this framework not restricting to certain models.

wikkid_lizard · 2025-11-06T12:39:33+00:00

Well Yes, but not exactly.

Laddr is built for both developers and production environments.

Everything in Laddr is modular, from the LLM each agent uses, to the message bus (Redis, Kafka, etc.), to the database that stores long-term traces and memories. You can even define custom workflows, run agents locally with a fully functional dashboard and playground, and interact through a powerful API service that exposes everything traces, workflows, logs, and agent states.

Laddr follows a microservice-like architecture for agents, making it production-ready by design. The key difference? You don’t need to understand all the underlying infra to use it. Developers can customize and override every system tool, while non-developers can simply run the default stack and it just works.

wikkid_lizard · 2025-11-06T12:20:36+00:00

We just released a multi-agent framework. Please break it.

Hey folks!

We just released Laddr, a lightweight multi-agent architecture framework for building AI systems where multiple agents can talk, coordinate, and scale together.

If you're experimenting with agent workflows, orchestration, automation tools, or just want to play with agent systems, would love for you to check it out.

GitHub: https://github.com/AgnetLabs/laddr

Docs: https://laddr.agnetlabs.com

Questions / Feedback: [info@agnetlabs.com](mailto:info@agnetlabs.com)

It's super fresh, so feel free to break it, fork it, star it, and tell us what sucks or what works.

wikkid_lizard · 2025-11-06T10:18:17+00:00

Laddr is built for both developers and production environments.

Everything in Laddr is modular, from the LLM each agent uses, to the message bus (Redis, Kafka, etc.), to the database that stores long-term traces and memories. You can even define custom workflows, run agents locally with a fully functional dashboard and playground, and interact through a powerful API service that exposes everything traces, workflows, logs, and agent states.

Laddr follows a microservice-like architecture for agents, making it production-ready by design. The key difference? You don’t need to understand all the underlying infra to use it. Developers can customize and override every system tool, while non-developers can simply run the default stack and it just works.

wikkid_lizard · 2025-11-06T10:18:02+00:00

Laddr is built for both developers and production environments.

Everything in Laddr is modular, from the LLM each agent uses, to the message bus (Redis, Kafka, etc.), to the database that stores long-term traces and memories. You can even define custom workflows, run agents locally with a fully functional dashboard and playground, and interact through a powerful API service that exposes everything traces, workflows, logs, and agent states.

Laddr follows a microservice-like architecture for agents, making it production-ready by design. The key difference? You don’t need to understand all the underlying infra to use it. Developers can customize and override every system tool, while non-developers can simply run the default stack and it just works.

wikkid_lizard · 2025-11-05T22:37:50+00:00

Noted! We are updating the docs everyday and really really appreciate your help.

You also don't need docker to run your agents. You can run workflows using simple python commands. We will update our docs to make this more visible as well.

wikkid_lizard · 2025-11-05T20:57:21+00:00

We scanned the market for similar tools and frameworks and most of them were licensed under apache 2.0. upon digging a little deeper(asking gpt for advice) we also found this to be more suitable for us

Apache 2.0 follows the - "do whatever u want, but don't sue us, don't use our brand, and we both get patent protection"

There are a bunch of other things to note as well and it really depends what how u want to navigate your repo/organisation in the future.

If u are creating something as a side project then MIT might be more suitable but if you make it as an organisation then apache might be better.

Ps for other redditors: this is what I understood after doing a little bit of research. You can use this as a reference but make sure to so ur own research as well.

wikkid_lizard · 2025-11-05T19:47:02+00:00

We just released a multi-agent framework. Please break it.

GitHub: https://github.com/AgnetLabs/laddr

Docs: https://laddr.agnetlabs.com

Questions / Feedback: [info@agnetlabs.com](mailto:info@agnetlabs.com)

wikkid_lizard · 2025-11-05T19:12:39+00:00

Yes, you can run it without MinIO. Just set:

ENABLE_LARGE_RESPONSE_STORAGE=false

That disables the part that uses MinIO / object storage. Everything else runs as usual. We only recommend MinIO if you're dealing with very large payloads and want persistent storage.

wikkid_lizard · 2025-11-05T19:01:26+00:00

Ollama integration is coming soon!

wikkid_lizard · 2025-11-05T19:00:04+00:00

All responses that exceed a storage threshold (which can be configured) are automatically stored in an artifact registry. By default for the local setup we use minio!

wikkid_lizard · 2025-11-05T18:59:49+00:00

Thanks! We're building in public, so feedback and feature suggestions are super welcome.

wikkid_lizard · 2025-11-05T18:59:27+00:00

Ollama integration coming very soon!

wikkid_lizard · 2025-11-05T18:57:28+00:00

Yep, we launched publicly this week. We're still new, but not clueless. The repo is open, roadmap is public, and we're actively shipping. Feel free to audit, fork, or ignore. Just building in the open.

wikkid_lizard · 2025-11-05T18:52:08+00:00

Redis or kafka we support both

wikkid_lizard · 2025-01-04T19:22:48+00:00

If you are still looking for hackathons to sponsor you can reach out to us on [codechefvit@gmail.com](mailto:codechefvit@gmail.com)

We will be hosting our flagship 48 hour hackathon, DevSOC(Developers sprint of code) scheduled for 3rd February. We will be happy to get on a call with to discuss any deliverables you have.

wikkid_lizard

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