MagenticBrain

MagenticBrain from microsoft. Source-based hardware guidance from its published configuration.

EstimatedRepository opened May 12, 2026Source checked 9/25/2026Version: db8eb934

LOCALRENTED GPUOPEN WEIGHTS

At a glance

Parameters: 14.77B
Architecture: qwen3
Context length: 40,960
License: mit
Disk space: 55 GB
Software: vLLM, Transformers

View the model source

MY HARDWARE

Will it run on your machine?

Save your machine to see a personalized rating and its reasoning.

Add my hardware

Ways to run it

vLLM · Linux

Repository-specific command in publisher documentation; confirm dependencies and hardware in the source.

vllm serve microsoft/MagenticBrain --enable-auto-tool-choice --tool-call-parser hermes --max-model-len 32768

Official instructions

Transformers · See official guide

Repository-specific command in publisher documentation; confirm dependencies and hardware in the source.

import json
from transformers import AutoTokenizer, AutoModelForCausalLM

model_name = "microsoft/MagenticBrain"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto",
)

tools = [
    {
        "type": "function",
        "function": {
            "name": "read_file",
            "description": "Read the contents of a file at the given path.",
            "parameters": {
                "type": "object",
                "properties": {"path": {"type": "string"}},
                "required": ["path"],
            },
        },
    },
    {
        "type": "function",
        "function": {
            "name": "submit",
            "description": "Signal that the task is complete.",
            "parameters": {"type": "object", "properties": {}},
        },
    },
]

messages = [
    {"role": "system", "content": "You are an orchestration agent. Plan steps and call only the tools declared below."},
    {"role": "user", "content": "Summarize the contents of /tmp/report.md."},
]

text = tokenizer.apply_chat_template(
    messages,
    tools=tools,
    tokenize=False,
    add_generation_prompt=True,
    enable_thinking=False,  # thinking is disabled by default for MagenticBrain
)

inputs = tokenizer(text, return_tensors="pt").to(model.device)
output_ids = model.generate(**inputs, max_new_tokens=1024)[0][inputs.input_ids.shape[1]:]
print(tokenizer.decode(output_ids, skip_special_tokens=True))

Official instructions

Explore its uses

SAFETENSORSTEXT-GENERATION

Keep exploring

Text

Prism ML

Bonsai 2 · 27B

A compressed 27B-class reasoning model with publisher-provided GGUF packs and a dedicated llama.cpp fork for CUDA, Metal, and CPU.

MODEL SIZE27.36B

LOCALRENTED GPU

Released Sep 17, 2026

Text

DeepSeek

DeepSeek V4.1 Flash

A multimodal reasoning model with a compressed key-value cache, published as open weights.

LOCALRENTED GPU

Repository opened Sep 10, 2026

Text

Qwen

Qwen3.8-Flash-Next

An experimental open-weight multimodal model with sparse attention and 262K native context.

MODEL SIZE125B language · 6B active

LOCALRENTED GPU

Repository opened Aug 24, 2026