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Introducing: a coding agent (Pi) running entirely in your browser using a 2B model on WebGPU 🤯 MiniCPM5-2B + Pi, powered by Transformers.js + WebGPU + 4-bit ONNX weights. All previous attempt to create this failed but MiniCPM5 seems to make it usable. Available now on Hugging Face 👇

85,828 次观看 • 1 天前 •via X (Twitter)

31 条评论

Victor M 的头像
Victor M1 天前

Try it here:

Esee 的头像
Esee1 天前

is this Pi?? 😂

orvir 的头像
orvir1 天前

2B running a coding agent locally is the part that gets me what's it actually usable for, autocomplete-tier stuff or can it hold a real task

fcan 的头像
fcan1 天前

wow great work! I have been trying to run MiniCPM5-2B in a browser. Tried Transformers.js/ONNX route but WebLLM/MLC works faster in my experience. Here is my Hugging Face space that uses my own MLC quants.

TOM 🦋🔶🅿️ 的头像
TOM 🦋🔶🅿️1 天前

cool

decipherx 的头像
decipherx1 天前

the browser is becoming the smallest useful agent sandbox

Towhid Khan 的头像
Towhid Khan1 天前

The embedding split is the part that makes this work: 510 MiB of embeddings cut losslessly into four 127.5 MiB tensors so it clears WebGPU's 128 MiB storage binding cap. First download is 1.84 GB into OPFS, and cached reloads pull zero weights.

Lars 的头像
Lars1 天前

Does the cat have a name @victormustar

✧.* 的头像
✧.*1 天前

Whats the cat mascot called?

Sogni.ai 的头像
Sogni.ai1 天前

pair Pi with Sogni Creative Agent and you have code plus creative production in one very capable setup 😎

LUNCYKONG 的头像
LUNCYKONG1 天前

Lots of clustered wallets fuck this

Phantom Phreak 的头像
Phantom Phreak1 天前

wtf lol

EDDY VU 的头像
EDDY VU1 天前

Running a usable 2B model entirely in-browser via WebGPU is a genuinely impressive technical feat, huge congrats on the release.

SourceCodeplz 的头像
SourceCodeplz1 天前

why not use wllama ?

Crio Songo 的头像
Crio Songo1 天前

居然能直接在浏览器跑端侧编码智能体,MiniCP5这个小模型适配得真不错,不用部署本地就能体验,挺方便。

Varik Verilion 的头像
Varik Verilion1 天前

How much of the coding workflow survives once the browser tab loses focus or the WebGPU device gets reclaimed?

Johnny 的头像
Johnny1 天前

oh shit you did it!

Nazmul Hossain 的头像
Nazmul Hossain1 天前

running an agent client-side on webgpu is insane progress. testing this now

Pronsh 的头像
Pronsh1 天前

kinda dope

Karthikeyan Ranasthala 的头像
Karthikeyan Ranasthala1 天前

Awesome! Will give it a try

Marina Rosa 的头像
Marina Rosa1 天前

My god you beat me to it Absurdly marvelous thank you for that

Shesaidmewakeup 的头像
Shesaidmewakeup1 天前

2B on WebGPU that actually finishes a loop is the part most people skip. Cloud still wins the hard tasks. This wins the ones you can keep in a tab.

msuiche 的头像
msuiche1 天前

@maceip NOT WRONG! JUST EARLY!

Offscript 的头像
Offscript1 天前

the interesting part isnt that it codes, its that nothing leaves the machine. a 2b wont out-write a frontier model but it can read things youd never paste into an api. whats the latency per token like on a normal laptop gpu?

LLM Codes 的头像
LLM Codes1 天前

wait is it actually coding in the browser or just chatting

nick 的头像
nick1 天前

crazy

Thomas Gauthier-Caron 的头像
Thomas Gauthier-Caron1 天前

This is so cool! Would you be so kind as to open source the web app? The pi TUI + monaco workspace split view is really nice

Tom Zarebczan 🛡 的头像
Tom Zarebczan 🛡1 天前

Any benchmarks?

Victor M 的头像
Victor M1 天前

yes

basedcapital 的头像
basedcapital1 天前

transformers.js, webgpu and 4-bit onnx all shipped before and died, so the model was the bottleneck. webllm with the same model should get the same jump. the browser part was never the story.

Labangram Photography 的头像
Labangram Photography1 天前

Browser-local coding agent that actually sticks is rare. How do you cap tool loops + WebGPU memory when the session runs long?

相关视频

Insane progress for small language models! MiniCPM5-2B is a dense 2B-parameter model by OpenBMB from China that's built for reasoning, coding, and tool use on resource-constrained hardware. The model specifically excels at coding and tool calling, two capabilities central to the shift from on-device LLMs to on-device agents. Instead of only answering prompts, it can use tools, generate code, carry information between steps, and complete multi-step tasks. I ran it 100% locally and connected it to a small investigation agent with one request: > Revenue dropped last week. Investigate what happened, quantify the impact, identify the likely cause, and produce an incident report with supporting evidence. The evidence was spread across orders, traffic, payments, refunds, and deployment logs. The model inspected the files, wrote its own queries, analyzed the intermediate results, and decided what to investigate next. Each tool result informed the next action, so the final report depended on the model maintaining a coherent investigation across the complete trajectory. The recording shows the actual task from beginning to end. It starts with the revenue question, follows the tool calls and supporting evidence, and ends with a quantified diagnosis and incident report. The data, tool execution, and model inference all remained on my machine. These capabilities were optimized through Agentic Pre-training, SFT, and large-scale RL. They do not come entirely from an application-level agent framework. MiniCPM5-2B supports SGLang, vLLM, llama(.)cpp, Ollama, iOS, Android, and HarmonyOS. OpenBMB has also released the model weights and parts of the training recipes and data resources behind it. Download MiniCPM5-2B: A 2B model can now maintain enough state to coordinate tools and complete a useful investigation on local hardware.

Avi Chawla

13,954 次观看 • 4 天前