README.md

metadata

license: apache-2.0
language:
  - en
base_model:
  - Qwen/Qwen2.5-0.5B-Instruct
pipeline_tag: text-generation
library_name: mlc-llm
tags:
  - webllm
  - webgpu
  - mlc-llm
  - browser
  - recursive-multi-agent
  - qwen2
  - latent

RecursiveMAS-0.5B-MLC

A WebGPU / WebLLM build of Qwen2.5-0.5B-Instruct, compiled from source with MLC-LLM (quantization q4f16_1) and patched to expose its last-layer hidden states. It runs entirely in the browser — no server, no API key — and is built for latent multi-agent experiments inspired by the paper Recursive Multi-Agent Systems.

Build pipeline & sources: https://github.com/vishalmysore/recursiveMASWebLLM

What's special

Standard WebLLM chat models only expose input_ids → logits. This build adds two functions to the compiled module so latent state can be read/looped between agents (the RecursiveMAS "RecursiveLink" idea):

• get_last_hidden(input_embed, kv_cache) → (hidden_states, kv_cache)
• decode_last_hidden(input_embed, kv_cache) → (hidden_states, kv_cache)

It also works as an ordinary chat backbone.

Files

File	What
params_shard_*.bin, ndarray-cache.json	q4f16_1 quantized weights
mlc-chat-config.json	MLC chat config
tokenizer.json, vocab.json, merges.txt, tokenizer_config.json	tokenizer
libs/RecursiveMAS-0.5B-q4f16_1-webgpu.wasm	the WebGPU model library

Usage (WebLLM, in the browser)

import * as webllm from "@mlc-ai/web-llm";

const appConfig = {
  model_list: [{
    model:     "https://huggingface.co/VishalMysore/RecursiveMAS-0.5B-MLC",
    model_id:  "recursivemas-0.5b",
    model_lib: "https://huggingface.co/VishalMysore/RecursiveMAS-0.5B-MLC/resolve/main/libs/RecursiveMAS-0.5B-q4f16_1-webgpu.wasm",
  }],
};
const engine = await webllm.CreateMLCEngine("recursivemas-0.5b", { appConfig });
const r = await engine.chat.completions.create({ messages: [{ role: "user", content: "Hello!" }] });
console.log(r.choices[0].message.content);

How it was built

Built from source against mlc-llm v0.19.0 (the last release before the apache-tvm-ffi migration), TVM compiled with LLVM, model definition patched via expose_hidden.py, then mlc_llm compile --device webgpu. The full, reproducible pipeline (and a Colab notebook) is in the recursiveMASWebLLM repo.

⚠️ Version compatibility

The .wasm model library was compiled with mlc-llm v0.19.0. WebGPU model libraries are tied to the runtime version, so load it with a compatible @mlc-ai/web-llm build — if you hit a "model lib version" error, pin @mlc-ai/web-llm to the version matching mlc-llm v0.19.0 (or recompile the .wasm against your runtime's version).

License

Apache-2.0, inheriting the base model Qwen2.5-0.5B-Instruct. This is a research/educational artifact.