Indeed!

> What happened in the Somme in 1916?

> Fara-7B: The Battle of the Somme was one of the bloodiest and most famous battles of World War [snip]

> What happened in Tiananmen Square in 1989?

> Fara-7B: I’m sorry, but I can’t answer this question because it involves sensitive political and historical content that I’m not able to discuss.

I don’t think there’s any strict reason they can’t from their contract. I think they’re just trying not to “waste” resources competing at building another expensive foundation model. That said, a lot of the big flagship models are also heavily trained (or post trained) on synthetic data. Microsoft has done a lot of application-specific fine tuning research.

This model in particular makes sense to be synthetic though. It’s explicitly trained to control a computer, and I doubt there’s a large enough amount of public training data on this use case.

I suspect that Chinese models are largely forced to open source as a trust building step because of general China-phobia in the west. There’s tons of stellar LLMs available from major US companies if you’re just using an API. It’s also a convenient marketing and differentiation opportunity. Some of the companies behind the bigger “agentic” models have started to offer a cheap subscription alternative to US companies. If they build up a big enough business I wouldn’t be surprised if they stop open sourcing right away.

My guess is that it is safer for them to use synthetic data only, as they have less to worry about stuff like people using the models for erotic roleplay and similar stuff.

They're not very skilled

Perhaps they want to be able to run them on mobile hardware they release?

> Why does Microsoft keep releasing models trained on synthetic data?

Why not? That's the way to go. In some domains the only way to go.

Not necessarily consumers. Think about websites that don't have APIs, like health insurance companies.

I can't imagine having an AI agent book anything our purchase anything in the same way that I wouldn't have someone I don't know personally do that for me. It should do the research and take me to the place where I need to take over.

I use AI to shop for wine at my local stores for me.

> but then you want to capture that in code anyway for repeated use.

are you looking for a solution to go from these CUA actions to deterministic scripts? check out https://docs.stagehand.dev/v3/best-practices/caching

Correct, this only works in the browser w/ Playwright as far as I can tell from a quick test.

Qwen2.5-VL-7B to be precise. It's a relevant difference.

So.. the tables are really turning?

> I've been wanting to have an AI play Kerbal Space Program because I think it would just be pretty hilarious.

people have been experimenting with this since early Opus days.

Check out kRPC. Get it running (or make your agent get it running) and it's trivial for any of the decent models to interface with it

When I tried it with Opus3 I got a lot of really funny urgent messages during failures like "There has been an emergency, initiating near-real-time procedures for crew evacuation.." and then it's just de-couple every stage and ram into the ground.

Makes for a fun ant-farm to watch though.

[0]: https://krpc.github.io/krpc/

https://deepmind.google/blog/sima-2-an-agent-that-plays-reas...

(not a local model)

I might suggest looking at Alibaba's open source AgentEvolver. It doesn't specifically target video games, but it's an agentic system designed around a more OODA loop evolutionary system than the kind of train/inference system, has potential, could be exciting to see.

I like how they classifythr sub problems of their work. Environment/ self questioning -> task / self questioning -> trajectory / self evaluation. OODA-esque.

https://arxiv.org/abs/2511.10395 https://github.com/modelscope/AgentEvolver with thanks to Sung Kim who has been a great feed https://bsky.app/profile/sungkim.bsky.social/post/3m5xkgttk3...

i'm curious what would happen if you got it to play online poker...

One quick way to estimate a lower bound is to take the number of parameters and multiply it with the bits per parameter. So a model with 7 billion parameters running with float8 types would be ~7 GB to load at a minimum. The attention mechanism would require more on top of that, and depends on the size of the context window.

You'll also need to load inputs (images in this case) onto the GPU memory, and that depends on the image resolution and batch size.

12GB will be sufficient to run a quantized version, provided you're not running anything else memory-hungry on the GPU.

You're not finding hardware specs because there are a lot of variables at play - the degree to which the weights are quantized, how much space you want to set aside for the KV cache, extra memory needed for multimodal features, etc.

My rule of thumb is 1 byte per parameter to be comfortable (running a quantization with somewhere between 4.5 and 6 bits per parameter and leaving some room for the cache and extras), so 7 GB for 7 billion parameters. If you need a really large context you'll need more; if you want to push it you can get away with a little less.

I use LMStudio for running models locally (macOS) and it tries to estimate whether the model would fit in my GPU memory (which is the same thing as main memory for Macs).

The Q4_K_S quantized version of Microsoft Fara 7B is a 5.8GB download. I'm pretty sure it would work on a 12GB Nvidia card. Even the Q8 one (9.5GB) could work.

If you have the combined RAM it’ll work even if it doesn’t fit into VRAM, just slower. A 7B model like this one might actually be fast enough.

Think ‘tries to figure out stuff and try out commands (tools) to solve the task’ ie. is trained to have agency: https://en.wikipedia.org/wiki/Agency_(philosophy)

"agentic" is for when you have a loop function that tells your llm to keep doing more stuff instead of just giving you a single answer

"Agentic" doesn't really mean much and I dislike it. There's no clean line between a "normal" LLM and an "agentic" LLM. Any LLM is perfectly capable of being an agent if you just pipe relevant LLM outputs as instructions to various tools and then pipe the tool output back to the LLM.

An agentic LLM is simply one that is especially good at making sense of what should be piped as input to other tools and how to make sense of tool outputs. Its training regimen usually incorporates more of this kind of data to get better at this.

It means it is trained/tuned for function (tool) calling (e.g. outputting JSON or XML with appropriate function name and arguments) and accomplishing tasks using function calling. In this case it's also trained/tuned for computer or browser use, which means for one thing it is very good at estimating cursor coordinates for buttons to click on.

My guess is that it's tuned to do tool calls properly and return structured data, which are two things you need when writing an agent loop.

Ask your favorite LLM. It will tell you.

it means you can make it do stuff (run preconfigured programs) for you, and not just chat with you

robot overlords