Instructions to use TypeScript/FastBrain-9B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use TypeScript/FastBrain-9B with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="TypeScript/FastBrain-9B")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("TypeScript/FastBrain-9B")
model = AutoModelForMultimodalLM.from_pretrained("TypeScript/FastBrain-9B", device_map="auto")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=256)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use TypeScript/FastBrain-9B with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf TypeScript/FastBrain-9B:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf TypeScript/FastBrain-9B:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf TypeScript/FastBrain-9B:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf TypeScript/FastBrain-9B:Q4_K_M

Build from source code

git clone https://github.com/ggml-org/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-app
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama serve -hf TypeScript/FastBrain-9B:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama cli -hf TypeScript/FastBrain-9B:Q4_K_M

Use Docker

docker model run hf.co/TypeScript/FastBrain-9B:Q4_K_M

Llama new
LM Studio
Jan

vLLM

How to use TypeScript/FastBrain-9B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "TypeScript/FastBrain-9B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "TypeScript/FastBrain-9B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/TypeScript/FastBrain-9B:Q4_K_M

SGLang

How to use TypeScript/FastBrain-9B with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "TypeScript/FastBrain-9B" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "TypeScript/FastBrain-9B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "TypeScript/FastBrain-9B" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "TypeScript/FastBrain-9B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use TypeScript/FastBrain-9B with Ollama:
```
ollama run hf.co/TypeScript/FastBrain-9B:Q4_K_M
```
Unsloth Desktop

How to use TypeScript/FastBrain-9B with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf TypeScript/FastBrain-9B:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "TypeScript/FastBrain-9B:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use TypeScript/FastBrain-9B with Docker Model Runner:
```
docker model run hf.co/TypeScript/FastBrain-9B:Q4_K_M
```

Lemonade

How to use TypeScript/FastBrain-9B with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull TypeScript/FastBrain-9B:Q4_K_M

Run and chat with the model

lemonade run user.FastBrain-9B-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use TypeScript/FastBrain-9B with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf TypeScript/FastBrain-9B:Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default TypeScript/FastBrain-9B:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use TypeScript/FastBrain-9B with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf TypeScript/FastBrain-9B:Q4_K_M

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "TypeScript/FastBrain-9B:Q4_K_M" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

FastBrain-9B / README.md

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---
license: apache-2.0
language:
  - en
library_name: transformers
pipeline_tag: text-generation
base_model: Cloudflare/clef-flash
datasets:
  - TypeScript/FastBrain-Instruct
tags:
  - fastbrain
  - clef
  - gguf
  - llama-cpp
  - systemone
  - decision-index
  - tool-calling
  - function-calling
  - intent-classification
  - structured-decision
---
<div align="center">

# FastBrain-9B

**Non-Autoregressive System-1 Structured Decision Model (`9.4B` Parameters)**

[![Architecture](https://img.shields.io/badge/Backbone-Qwen3.5--9B_Hybrid_(9.4B_Total)-3b82f6?style=flat-square)](#architecture)
[![Decision Head](https://img.shields.io/badge/Head-JointSchemaHead_(116M)-10b981?style=flat-square)](#architecture)
[![Decision Index](https://img.shields.io/badge/Decision_Index_v0.2.1-80.55%25_(%232_Overall_%C2%B7_%231_9B)-f59e0b?style=flat-square)](#benchmark-evaluation)
[![License](https://img.shields.io/badge/License-Apache_2.0-6366f1?style=flat-square)](./LICENSE)

</div>

**FastBrain-9B** (`9.4B` total parameters: `Qwen3.5-9B` hybrid backbone + `116M` `JointSchemaHead`) is a single-forward-pass, non-autoregressive **System-1 structured decision model**.

Given an arbitrary JSON state and one or more structured decision schemas (`choice`, `noul` binary verification, or `score` ranking), **FastBrain-9B** evaluates all candidate options and questions **jointly in a single forward pass (`0` generated tokens)**—delivering calibrated option probabilities, tool-call selections, and open-set abstention decisions at encoder-level latency.

All fine-tuned backbone updates (`104` projection layers across all `32` hybrid blocks) are merged directly into the `9B` backbone weights (`model-00001-of-00004.safetensors` .. `model-00004-of-00004.safetensors`), and all `122` decision-head tensors are packaged in `joint_head.safetensors`. **No external LoRA or PEFT adapter is required at inference time.**

---

## Benchmark Evaluation & Parameter Scale Comparison

Evaluated on the official **Cloudflare Decision Index (v0.2.1)** across **10 benchmarks (22,427 evaluation requests)**, **FastBrain-9B (`9.4B`)** achieves an overall Macro Mean of **`80.55%`**:
- **Ranks `#1` across all `<=9B` models on `10 / 10` benchmarks**, outperforming the same-size base model `Clef-flash` (`9.4B`, `74.96%`) by **`+5.59 pp`**, `Kev 9B` (`9B`, `76.51%`) by **`+4.04 pp`**, and `Laya` (`0.4B`, `58.09%`) by **`+22.46 pp`**.
- **Outperforms `3x`-larger `27B`-class models**: surpasses `Jev` (`27B`, `80.32%`) and `DiffusionGemma Jev` (`26B-A4B`, `69.14%`) in 10-benchmark Macro Mean, and outperforms the `3x`-larger flagship `Clef` (`27.4B`, `81.81%`) on **5 out of 10 benchmarks** (`BFCL`, `API-Bank`, `CLINC150+OOS`, `Home appliances`, and `When2Call`).

![FastBrain-9B Overview](./assets/benchmark_overview.png)

### Full 10-Benchmark Evaluation Matrix (Annotated by Model Parameter Scale)

![FastBrain-9B Scoreboard](./assets/benchmark_scoreboard.png)

### Fine-Grained Decision Calibration & Open-Set Reliability

![FastBrain-9B Calibration Breakdown](./assets/decision_calibration_breakdown.png)

### Summary Comparison by Model Parameter Scale

| Benchmark | Metric | `Laya`<br>*(0.4B / 421M)* | `DiffusionGemma Jev`<br>*(26B-A4B · 4B Act)* | `Clef-flash`<br>*(9.4B Base)* | `Kev 9B`<br>*(9B Dense)* | `Jev`<br>*(27B Dense)* | `Clef`<br>*(27.4B Flagship)* | **FastBrain-9B**<br>*(9.4B · Ours)* |
| :--- | :--- | :---: | :---: | :---: | :---: | :---: | :---: | :---: |
| **Parameter Scale** | **Total (Active)** | `0.42B` | `25.8B (4B MoE)` | `9.41B` | `9.0B` | `27.0B` | `27.36B` | **`9.41B`** |
| **BFCL** | Case Exact | 95.87% | 97.64% | 98.76% | 97.58% | 98.58% | 98.47% | **98.76% (#1)** |
| **ToolRet** | nDCG@10 | 51.31% | 45.28% | 67.26% | 65.72% | 65.50% | 68.18% | **67.26% (#2 · #1 9B)** |
| **API-Bank** | Accuracy | 86.22% | 85.24% | 93.11% | 89.76% | 90.35% | 91.93% | **93.70% (#1)** |
| **BANKING77** | Macro-F1 | 62.66% | 93.80% | 93.69% | 91.77% | 93.58% | 94.07% | **93.77% (#1 9B)** |
| **CLINC150+OOS** | Macro-F1 | 73.79% | 94.64% | 66.77% | 96.65% | 96.44% | 97.43% | **98.44% (#1)** |
| **Home appliances** | Case Exact | 23.86% | 68.18% | 96.59% | 88.64% | 88.64% | 96.59% | **97.73% (#1)** |
| **BRIGHT** | nDCG@10 | 25.00% | 18.69% | 38.49% | 38.53% | 43.37% | 48.03% | **38.58% (#1 9B)** |
| **Amazon ESCI** | Macro-F1 | 52.84% | 56.83% | 57.39% | 53.75% | 56.60% | 59.30% | **57.40% (#2 · #1 9B)** |
| **PhishNChips** | Accuracy | 60.45% | 55.65% | 71.95% | 72.10% | 88.20% | 91.70% | **78.75% (#1 9B)** |
| **When2Call** | Macro-F1 | 48.92% | 75.44% | 65.58% | 70.64% | 81.91% | 72.37% | **81.13% (#2 · #1 9B)** |
| **Macro Mean (10)** | **Average** | **58.09%** | **69.14%** | **74.96%** | **76.51%** | **80.32%** | **81.81%** | **80.55% (#2 · #1 9B)** |

---

## Architecture

1. **Hybrid Gated DeltaNet + Full-Attention Backbone (`9.3B` Backbone, `bfloat16`)**:
   - Processes long-context tool registries, multi-turn agent states, and candidate option spans in a single prefill pass up to `262,144` tokens (`24` linear-attention blocks + `8` full-attention blocks).
   - Jointly adapted across all `104` linear-attention (`in_proj_qkv`, `in_proj_z`, `out_proj`) and self-attention (`q_proj`, `k_proj`, `v_proj`, `o_proj`) projections and merged in-place into the backbone weights.
2. **Joint Schema Decision Head (`joint_head.safetensors`, `116M` Parameters)**:
   - Extracts question-level and option-level span representations directly from the final hidden states and scores all questions (`choice`, `noul`, `score`) simultaneously without autoregressive decoding (`output_tokens = 0`).
3. **Compile-Time AST Schema-Aware Margin Calibration (`joint_schema_model.py`)**:
   - Eliminates System-1 tool over-triggering and open-set false acceptance via compile-time schema topology analysis and post-hoc logit margin calibration.
---
## Quickstart
### 1. PyTorch / Transformers (`bfloat16` or `4-bit` / `8-bit` Quantized on Colab T4)
`TypeScript/FastBrain-9B` is a drop-in replacement for `Cloudflare/clef-flash` using the standard `joint_schema_model` runtime. It supports both full-precision `bfloat16` (`18.82 GB`, for `>=24 GB` GPUs such as L4 / RTX 4090 / PRO 6000) and mixed-precision **`load_in_4bit=True` (`~6.8 GB` VRAM, runs on a free Google Colab T4 16GB GPU)** or **`load_in_8bit=True` (`~11.2 GB` VRAM)** while keeping the `116M` `JointSchemaHead` in native `bfloat16`/`float16`:

```python
from huggingface_hub import snapshot_download
import sys
import torch
model_dir = snapshot_download("TypeScript/FastBrain-9B", ignore_patterns=["*.gguf"])
sys.path.insert(0, model_dir)
import joint_schema_model as clef
# Full precision (24GB+ GPU: L4, A100, RTX 3090/4090, RTX PRO 6000):
model, processor = clef.load_release_model(model_dir, device="cuda", dtype=torch.bfloat16)
# Or 4-bit NF4 mixed-precision (8GB–16GB GPU, e.g., free Google Colab T4):
# model, processor = clef.load_release_model(model_dir, device="cuda", dtype=torch.float16, load_in_4bit=True)
response = clef.systemone(model, processor, {
    "model": "TypeScript/FastBrain-9B",
    "state": {
        "query": "Can you check my account balance?",
        "tools": ["get_balance", "transfer_funds"],
    },
    "questions": {
        "q": {
            "type": "choice",
            "query": "Select the best next action.",
            "criteria": {
                "a": "Call <tool_call>{\"name\": \"get_balance\", \"arguments\": {}}</tool_call>",
                "b": "Could you please provide your account ID or authentication token first?",
                "c": "I am unable to access personal banking records.",
            },
        },
    },
})
print(response)
```

### 2. `llama.cpp` GGUF (`Q8_0` / `Q4_K_M` / `BF16`) via `/v1/systemone`
This repository provides official `llama.cpp` (**`b11430`+**, `ClefModel` architecture) GGUF files that bundle both the `Qwen3.5-9B` hybrid backbone and the `116M` `JointSchemaHead` (`dec.*` decision-head tensors, `ssm_` linear-attention states, and `attn_` projections preserved at `Q8_0` precision even in the `Q4_K_M` build).

> [!IMPORTANT]
> **Required `llama.cpp` Version & `ClefModel` Batch Flags:**
> 1. **Minimum version**: Use **`llama.cpp` release `b11430` or newer** (which includes native `ClefModel` / `LLM_ARCH_CLEF` graph construction and the `/v1/systemone` HTTP endpoint). Standard Ollama or older `llama.cpp` builds without `ClefModel` support cannot execute `dec.*` decision-head tensors.
> 2. **Set `-b` and `-ub` to match your maximum prompt length (e.g., `-b 16384 -ub 16384 --no-cache-prompt` or `-b 4096 -ub 4096 --no-cache-prompt`)**:
>    In `llama-server`, `ClefModel` computes cross-attention over `<|question_*|>` and `<|option_*|>` spans within a single physical micro-batch (`ubatch`, default `512`). Setting `-ub` equal to `-b` ensures long prompts (>512 tokens) are evaluated in a single physical `ubatch` without splitting option spans across micro-batches, and `--no-cache-prompt` prevents prefix-cache skipping over decision markers.
#### Step 1: Download Prebuilt `llama-server` (`b11430`+) and GGUF Weights
```bash
# 1. Download prebuilt llama.cpp (b11430+) with ClefModel & /v1/systemone support
#    - Linux CUDA 13.x (Colab / Ubuntu): llama-b11430-bin-ubuntu-cuda-13.4-x64.tar.gz
#    - Linux CUDA 12.x:                  llama-b11430-bin-ubuntu-cuda-12.8-x64.tar.gz
#    - macOS Apple Silicon (M1/M2/M3/M4): llama-b11430-bin-macos-arm64.tar.gz
curl -sL https://github.com/ggml-org/llama.cpp/releases/download/b11430/llama-b11430-bin-ubuntu-cuda-13.4-x64.tar.gz | tar -xz
# 2. Download FastBrain-9B GGUF (choose Q8_0 [9.66 GB, lossless] or Q4_K_M [6.73 GB, 8GB–16GB GPUs])
pip install -U "huggingface_hub[hf_transfer]"
HF_HUB_ENABLE_HF_TRANSFER=1 hf download TypeScript/FastBrain-9B FastBrain-9B-Q8_0.gguf --local-dir .
```
#### Step 2: Launch `llama-server` on the `/v1/systemone` Endpoint
```bash
# On Linux / Google Colab, ensure CUDA driver libraries are in LD_LIBRARY_PATH:
export LD_LIBRARY_PATH="$(pwd)/llama-b11430:/usr/lib64-nvidia:/usr/local/cuda/lib64:${LD_LIBRARY_PATH}"
# Launch llama-server (24GB+ GPU / 16K context):
./llama-b11430/llama-server \
  -m ./FastBrain-9B-Q8_0.gguf \
  -ngl 99 -c 16384 -b 16384 -ub 16384 -np 1 -fa on --no-cache-prompt \
  --port 8080

# Or on a 12GB–16GB GPU (e.g., Colab Free Tesla T4, 4K context with cuda-12.8):
# pip install nvidia-cuda-runtime-cu12 nvidia-cublas-cu12
# export LD_LIBRARY_PATH="$(pwd)/llama-b11430:$(python3 -c 'import glob;print(":".join(glob.glob("/usr/local/lib/python*/dist-packages/nvidia/*/lib")))'):/usr/lib64-nvidia:${LD_LIBRARY_PATH}"
# ./llama-b11430/llama-server \
#   -m ./FastBrain-9B-Q4_K_M.gguf \
#   -ngl 99 -c 4096 -b 4096 -ub 4096 -np 1 -fa on --no-cache-prompt \
#   --port 8080

# Optional: For multimodal image/screenshot states, attach the vision projector:
#   --mmproj ./mmproj-FastBrain-9B-Q8_0.gguf
```
#### Step 3: Query `/v1/systemone` (`curl` or Python — Single Forward Pass, `0` Output Tokens)
```bash
curl http://127.0.0.1:8080/v1/systemone \
  -H "Content-Type: application/json" \
  -d '{
    "model": "FastBrain-9B",
    "state": {
      "ticket_id": "ORD-99281",
      "customer_message": "I was charged twice for my subscription last week and nobody has replied to my ticket."
    },
    "questions": {
      "route": {
        "type": "choice",
        "instructions": "Which team should handle this?",
        "criteria": {
          "billing": "Payments, refunds, duplicate charges, or invoices",
          "technical": "Bugs, crashes, or service outages",
          "account": "Password reset or login issues"
        }
      },
      "urgent": {
        "type": "noul",
        "instructions": "Is the customer reporting a financial or urgent issue?"
      }
    }
  }'
```
**Python Client Example (`requests` / `urllib`)**:

```python
import requests
resp = requests.post(
    "http://127.0.0.1:8080/v1/systemone",
    json={
        "model": "FastBrain-9B",
        "state": {
            "query": "Can you check the live wire transfer status for my corporate checking account right now?",
            "available_tools": ["get_account_balance", "list_recent_cards"],
        },
        "questions": {
            "action": {
                "type": "choice",
                "instructions": "Select the best assistant response.",
                "criteria": {
                    "direct_answer": "Your wire transfer has been completed.",
                    "tool_call": "<tool_call>{\"name\": \"get_account_balance\", \"arguments\": {}}</tool_call>",
                    "clarify": "Could you please provide your corporate account ID and wire reference number?",
                    "abstain": "I cannot check wire transfer status because no wire-transfer tool is available.",
                },
            }
        },
    },
    timeout=30,
).json()
print(resp["choices"]["action"])  # Calibrated probability distribution over all options
print(resp["usage"])              # {'input_tokens': ..., 'output_tokens': 0, 'total_tokens': ...}
```

---

## GGUF Quantization Accuracy Retention & Speed Benchmarks

### 1. Decision Index 10-Benchmark Test-Set Accuracy Retention (`BF16` vs. `Q8_0` vs. `Q4_K_M`)
Evaluated across a stratified 10-benchmark Decision Index test suite (**`203` multi-constraint requests / `1,473` scored schema questions** spanning `choice`, `noul`, and `score` heads), `FastBrain-9B-Q8_0.gguf` (`9.66 GB`) is **virtually lossless (`99.86%` Top-1 decision agreement with native `bfloat16`, `-0.03 pp` score delta)**, while `FastBrain-9B-Q4_K_M.gguf` (`6.73 GB`, with `dec.*` head preserved in `Q8_0`) retains **`98.78%` Top-1 agreement** and **`-0.20 pp`** calibrated 10-task mean:
| Benchmark / Metric | Native PyTorch `bfloat16` (`18.82 GB`) | `FastBrain-9B-Q8_0.gguf` (`9.66 GB`) | `FastBrain-9B-Q4_K_M.gguf` (`6.73 GB`, `Q8_0` Head) |
| :--- | :---: | :---: | :---: |
| **Top-1 Decision Agreement vs. `bfloat16`** (`1,473` questions) | `100.00%` *(Reference)* | **`99.86%` (`1,471 / 1,473` identical)** | **`98.78%` (`1,455 / 1,473` identical)** |
| **Probability Mean Absolute Error (`|p_quant - p_bf16|`)** | `0.00000` | **`0.00203` (`0.20%`)** | **`0.00387` (`0.39%`)** |
| **10-Benchmark Mean (Single-Checkpoint Raw)** | **`82.37%`** | **`82.34%` (`-0.03 pp`)** | **`82.56%` (`+0.19 pp`)** |
| **10-Benchmark Mean (With Margin Calibration)** | **`83.68%`** | **`83.65%` (`-0.03 pp`)** | **`83.48%` (`-0.20 pp`)** |
| ├─ **1. BFCL** *(AST Tool Selection)* | `100.00%` | **`100.00%`** (`0.00`) | **`100.00%`** (`0.00`) |
| ├─ **2. ToolRet** *(20-Candidate Tool Reranking, `score`)* | `73.15%` | **`73.70%`** (`+0.55`) | **`75.60%`** (`+2.45`) |
| ├─ **3. API-Bank** *(Multi-Step Stateful Tool Calling)* | `95.00%` | **`95.00%`** (`0.00`) | **`95.00%`** (`0.00`) |
| ├─ **4. BANKING77** *(77-Way Fine-Grained Intent)* | `100.00%` | **`100.00%`** (`0.00`) | **`100.00%`** (`0.00`) |
| ├─ **5. CLINC150+OOS** *(Intent + Open-Set `noul` Abstention)* | `93.94%` | **`93.94%`** (`0.00`) | **`93.94%`** (`0.00`) |
| ├─ **6. Home Appliances** *(Embedded Device Control)* | `90.00%` | **`90.00%`** (`0.00`) | **`90.00%`** (`0.00`) |
| ├─ **7. BRIGHT** *(Reasoning-Intensive Document Ranking)* | `41.93%` | **`41.00%`** (`-0.93`) | **`40.31%`** (`-1.62`) |
| ├─ **8. Amazon ESCI** *(Fine-Grained E-Commerce Relevance)* | `73.89%` | **`73.89%`** (`0.00`) | **`75.79%`** (`+1.90`) |
| ├─ **9. PhishNChips** *(9-Probe Joint Security Inspection)* | `85.00%` | **`85.00%`** (`0.00`) | **`85.00%`** (`0.00`) |
| └─ **10. When2Call** *(4-Way Tool Governance)* | `75.00%` *(Raw `70.83%`)* | **`75.00%`** *(Raw `70.83%`, `0.00`)* | **`72.92%`** *(Raw `70.83%`, `-2.08`)* |

### 2. Measured Hardware Footprint & Inference Speed (`llama-server b11430` CUDA 13.4)

Because `FastBrain-9B` scores all schema questions and candidate options in a **single non-autoregressive prefill forward pass (`output_tokens = 0`)**, `llama-server` achieves **8,900–12,100+ input tokens/sec** (`31–109 ms` end-to-end HTTP latency) on server GPUs:
| Workload / Metric | `Q8_0.gguf`<br>*(RTX PRO 6000)* | `Q4_K_M.gguf`<br>*(RTX PRO 6000)* | `Q8_0.gguf`<br>*(Colab Free T4 16GB)* | `Q4_K_M.gguf`<br>*(Colab Free T4 16GB)* | Native `bfloat16`<br>*(RTX PRO 6000)* |
| :--- | :---: | :---: | :---: | :---: | :---: |
| **Model Load Time** | **`2.56 s`** | **`2.00 s`** | **`5.43 s`** | **`5.10 s`** | `~11.5 s` |
| **Peak GPU VRAM** | **`12.55 GiB`** *(16K ctx)* | **`10.30 GiB`** *(16K ctx)* | **`9.03 GiB` (`9,245 MiB`)** *(4K ctx)* | **`6.78 GiB` (`6,943 MiB`)** *(4K ctx)* | `20.3 – 24.7 GiB` |
| **Workload 1: Intent Routing + OOS** (`293 tok`, `K=2`) | **`32.82 ms`** (`8,928 tok/s`) | **`31.69 ms`** (`9,245 tok/s`) | **`349.32 ms`** (`839 tok/s`) | **`342.98 ms`** (`854 tok/s`) | `~35.0 ms` |
| **Workload 2: Tool Governance (`When2Call`)** (`1,068 tok`, `K=1`) | **`87.78 ms`** (`12,167 tok/s`) | **`89.34 ms`** (`11,955 tok/s`) | **`1,149.81 ms`** (`929 tok/s`) | **`1,154.31 ms`** (`925 tok/s`) | `~92.0 ms` |
| **Workload 3: Security Multi-Probe (`PhishNChips`)** (`1,303 tok`, `K=9`) | **`108.55 ms`** (`12,004 tok/s`) | **`109.97 ms`** (`11,849 tok/s`) | **`1,430.81 ms`** (`911 tok/s`) | **`1,440.52 ms`** (`905 tok/s`) | `~115.0 ms` |
| Format / Precision Tier | Weight Footprint | Peak VRAM (`4K`–`16K` ctx) | Minimum Recommended Hardware |
| :--- | :---: | :---: | :--- |
| **`FastBrain-9B-Q4_K_M.gguf`** *(4-bit + `Q8_0` Head)* | `6.73 GB` | `~7.8 – 10.3 GiB` | **8GB–12GB GPU / Colab Free T4 (16GB) / Mac M1–M4** |
| **PyTorch `load_in_4bit=True`** *(NF4 + `fp16` Head)* | `~5.5 GB` | `~6.2 – 6.8 GiB` | **Colab Free T4 (16GB) / RTX 3060–4060 (8GB+)** |
| **`FastBrain-9B-Q8_0.gguf`** *(Full 8-bit GGUF)* | `9.66 GB` | `~10.8 – 12.6 GiB` | **12GB–16GB GPU / Colab T4 / Mac 16GB+** |
| **PyTorch `load_in_8bit=True`** *(INT8 + `bf16` Head)* | `~9.6 GB` | `~11.2 GiB` | **Colab T4 (16GB) / RTX 4070 Ti (12GB+)** |
| **Native `bfloat16` (`safetensors` / `BF16.gguf`)** | `18.82 GB` (`18.16 GB`) | `20.3 – 24.7 GiB` | **Colab L4 (24GB) / RTX 3090–4090 / PRO 6000 (96GB)** |
---
## Repository Structure
| File | Size | Description |
| :--- | :---: | :--- |
| `model-00001-of-00004.safetensors` .. `00004` | `18.59 GB` | Full `9B` backbone weights (`bfloat16`, in-place merged, no LoRA required) |
| `joint_head.safetensors` & `joint_head_config.json` | `232 MB` | `116M`-parameter `JointSchemaHead` weights (`122` tensors) and configuration |
| `FastBrain-9B-Q4_K_M.gguf` | `6.73 GB` | Recommended 4-bit GGUF (`dec.*` decision head, `ssm_`, and `attn_` preserved at `Q8_0`) |
| `FastBrain-9B-Q8_0.gguf` | `9.66 GB` | High-precision 8-bit GGUF (`ClefModel` architecture for `llama-server`) |
| `FastBrain-9B-BF16.gguf` | `18.16 GB` | Full-precision `bfloat16` GGUF (`ClefModel` architecture for `llama-server`) |
| `mmproj-FastBrain-9B-Q8_0.gguf` & `BF16.gguf` | `624 MB` / `922 MB` | Multimodal vision projector GGUF files for image/screenshot states |
| `joint_schema_model.py` | `25 KB` | `ClefModel`, `JointSchemaHead`, `load_release_model(load_in_4bit/8bit)`, and `systemone()` runtime |
| `config.json`, `tokenizer.json`, `processor_config.json` | `11 MB` | Standard Hugging Face model, tokenizer, and processor configurations |
| `assets/*.png` | `1.2 MB` | High-resolution technical benchmark and calibration charts |

	---
	license: apache-2.0
	language:
	- en
	library_name: transformers
	pipeline_tag: text-generation
	base_model: Cloudflare/clef-flash
	datasets:
	- TypeScript/FastBrain-Instruct
	tags:
	- fastbrain
	- clef
	- gguf
	- llama-cpp
	- systemone
	- decision-index
	- tool-calling
	- function-calling
	- intent-classification
	- structured-decision
	---

	<div align="center">

	# FastBrain-9B

	Non-Autoregressive System-1 Structured Decision Model (`9.4B` Parameters)

	[![Architecture](https://img.shields.io/badge/Backbone-Qwen3.5--9B_Hybrid_(9.4B_Total)-3b82f6?style=flat-square)](#architecture)
	[![Decision Head](https://img.shields.io/badge/Head-JointSchemaHead_(116M)-10b981?style=flat-square)](#architecture)
	[![Decision Index](https://img.shields.io/badge/Decision_Index_v0.2.1-80.55%25_(%232_Overall_%C2%B7_%231_9B)-f59e0b?style=flat-square)](#benchmark-evaluation)
	[![License](https://img.shields.io/badge/License-Apache_2.0-6366f1?style=flat-square)](./LICENSE)

	</div>

	FastBrain-9B (`9.4B` total parameters: `Qwen3.5-9B` hybrid backbone + `116M` `JointSchemaHead`) is a single-forward-pass, non-autoregressive System-1 structured decision model.

	Given an arbitrary JSON state and one or more structured decision schemas (`choice`, `noul` binary verification, or `score` ranking), FastBrain-9B evaluates all candidate options and questions jointly in a single forward pass (`0` generated tokens)—delivering calibrated option probabilities, tool-call selections, and open-set abstention decisions at encoder-level latency.

	All fine-tuned backbone updates (`104` projection layers across all `32` hybrid blocks) are merged directly into the `9B` backbone weights (`model-00001-of-00004.safetensors` .. `model-00004-of-00004.safetensors`), and all `122` decision-head tensors are packaged in `joint_head.safetensors`. No external LoRA or PEFT adapter is required at inference time.

	---

	## Benchmark Evaluation & Parameter Scale Comparison

	Evaluated on the official Cloudflare Decision Index (v0.2.1) across 10 benchmarks (22,427 evaluation requests), FastBrain-9B (`9.4B`) achieves an overall Macro Mean of `80.55%`:
	- Ranks `#1` across all `<=9B` models on `10 / 10` benchmarks, outperforming the same-size base model `Clef-flash` (`9.4B`, `74.96%`) by `+5.59 pp`, `Kev 9B` (`9B`, `76.51%`) by `+4.04 pp`, and `Laya` (`0.4B`, `58.09%`) by `+22.46 pp`.
	- Outperforms `3x`-larger `27B`-class models: surpasses `Jev` (`27B`, `80.32%`) and `DiffusionGemma Jev` (`26B-A4B`, `69.14%`) in 10-benchmark Macro Mean, and outperforms the `3x`-larger flagship `Clef` (`27.4B`, `81.81%`) on 5 out of 10 benchmarks (`BFCL`, `API-Bank`, `CLINC150+OOS`, `Home appliances`, and `When2Call`).

	![FastBrain-9B Overview](./assets/benchmark_overview.png)

	### Full 10-Benchmark Evaluation Matrix (Annotated by Model Parameter Scale)

	![FastBrain-9B Scoreboard](./assets/benchmark_scoreboard.png)

	### Fine-Grained Decision Calibration & Open-Set Reliability

	![FastBrain-9B Calibration Breakdown](./assets/decision_calibration_breakdown.png)

	### Summary Comparison by Model Parameter Scale

	\| Benchmark \| Metric \| `Laya`<br>(0.4B / 421M) \| `DiffusionGemma Jev`<br>(26B-A4B · 4B Act) \| `Clef-flash`<br>(9.4B Base) \| `Kev 9B`<br>(9B Dense) \| `Jev`<br>(27B Dense) \| `Clef`<br>(27.4B Flagship) \| FastBrain-9B<br>(9.4B · Ours) \|
	\| :--- \| :--- \| :---: \| :---: \| :---: \| :---: \| :---: \| :---: \| :---: \|
	\| Parameter Scale \| Total (Active) \| `0.42B` \| `25.8B (4B MoE)` \| `9.41B` \| `9.0B` \| `27.0B` \| `27.36B` \| `9.41B` \|
	\| BFCL \| Case Exact \| 95.87% \| 97.64% \| 98.76% \| 97.58% \| 98.58% \| 98.47% \| 98.76% (#1) \|
	\| ToolRet \| nDCG@10 \| 51.31% \| 45.28% \| 67.26% \| 65.72% \| 65.50% \| 68.18% \| 67.26% (#2 · #1 9B) \|
	\| API-Bank \| Accuracy \| 86.22% \| 85.24% \| 93.11% \| 89.76% \| 90.35% \| 91.93% \| 93.70% (#1) \|
	\| BANKING77 \| Macro-F1 \| 62.66% \| 93.80% \| 93.69% \| 91.77% \| 93.58% \| 94.07% \| 93.77% (#1 9B) \|
	\| CLINC150+OOS \| Macro-F1 \| 73.79% \| 94.64% \| 66.77% \| 96.65% \| 96.44% \| 97.43% \| 98.44% (#1) \|
	\| Home appliances \| Case Exact \| 23.86% \| 68.18% \| 96.59% \| 88.64% \| 88.64% \| 96.59% \| 97.73% (#1) \|
	\| BRIGHT \| nDCG@10 \| 25.00% \| 18.69% \| 38.49% \| 38.53% \| 43.37% \| 48.03% \| 38.58% (#1 9B) \|
	\| Amazon ESCI \| Macro-F1 \| 52.84% \| 56.83% \| 57.39% \| 53.75% \| 56.60% \| 59.30% \| 57.40% (#2 · #1 9B) \|
	\| PhishNChips \| Accuracy \| 60.45% \| 55.65% \| 71.95% \| 72.10% \| 88.20% \| 91.70% \| 78.75% (#1 9B) \|
	\| When2Call \| Macro-F1 \| 48.92% \| 75.44% \| 65.58% \| 70.64% \| 81.91% \| 72.37% \| 81.13% (#2 · #1 9B) \|
	\| Macro Mean (10) \| Average \| 58.09% \| 69.14% \| 74.96% \| 76.51% \| 80.32% \| 81.81% \| 80.55% (#2 · #1 9B) \|

	---

	## Architecture

	1. Hybrid Gated DeltaNet + Full-Attention Backbone (`9.3B` Backbone, `bfloat16`):
	- Processes long-context tool registries, multi-turn agent states, and candidate option spans in a single prefill pass up to `262,144` tokens (`24` linear-attention blocks + `8` full-attention blocks).
	- Jointly adapted across all `104` linear-attention (`in_proj_qkv`, `in_proj_z`, `out_proj`) and self-attention (`q_proj`, `k_proj`, `v_proj`, `o_proj`) projections and merged in-place into the backbone weights.
	2. Joint Schema Decision Head (`joint_head.safetensors`, `116M` Parameters):
	- Extracts question-level and option-level span representations directly from the final hidden states and scores all questions (`choice`, `noul`, `score`) simultaneously without autoregressive decoding (`output_tokens = 0`).
	3. Compile-Time AST Schema-Aware Margin Calibration (`joint_schema_model.py`):
	- Eliminates System-1 tool over-triggering and open-set false acceptance via compile-time schema topology analysis and post-hoc logit margin calibration.

	---

	## Quickstart

	### 1. PyTorch / Transformers (`bfloat16` or `4-bit` / `8-bit` Quantized on Colab T4)

	`TypeScript/FastBrain-9B` is a drop-in replacement for `Cloudflare/clef-flash` using the standard `joint_schema_model` runtime. It supports both full-precision `bfloat16` (`18.82 GB`, for `>=24 GB` GPUs such as L4 / RTX 4090 / PRO 6000) and mixed-precision `load_in_4bit=True` (`~6.8 GB` VRAM, runs on a free Google Colab T4 16GB GPU) or `load_in_8bit=True` (`~11.2 GB` VRAM) while keeping the `116M` `JointSchemaHead` in native `bfloat16`/`float16`:

	```python
	from huggingface_hub import snapshot_download
	import sys
	import torch

	model_dir = snapshot_download("TypeScript/FastBrain-9B", ignore_patterns=["*.gguf"])
	sys.path.insert(0, model_dir)
	import joint_schema_model as clef

	# Full precision (24GB+ GPU: L4, A100, RTX 3090/4090, RTX PRO 6000):
	model, processor = clef.load_release_model(model_dir, device="cuda", dtype=torch.bfloat16)

	# Or 4-bit NF4 mixed-precision (8GB–16GB GPU, e.g., free Google Colab T4):
	# model, processor = clef.load_release_model(model_dir, device="cuda", dtype=torch.float16, load_in_4bit=True)

	response = clef.systemone(model, processor, {
	"model": "TypeScript/FastBrain-9B",
	"state": {
	"query": "Can you check my account balance?",
	"tools": ["get_balance", "transfer_funds"],
	},
	"questions": {
	"q": {
	"type": "choice",
	"query": "Select the best next action.",
	"criteria": {
	"a": "Call <tool_call>{\"name\": \"get_balance\", \"arguments\": {}}</tool_call>",
	"b": "Could you please provide your account ID or authentication token first?",
	"c": "I am unable to access personal banking records.",
	},
	},
	},
	})
	print(response)
	```

	### 2. `llama.cpp` GGUF (`Q8_0` / `Q4_K_M` / `BF16`) via `/v1/systemone`

	This repository provides official `llama.cpp` (`b11430`+, `ClefModel` architecture) GGUF files that bundle both the `Qwen3.5-9B` hybrid backbone and the `116M` `JointSchemaHead` (`dec.*` decision-head tensors, `ssm_` linear-attention states, and `attn_` projections preserved at `Q8_0` precision even in the `Q4_K_M` build).

	> [!IMPORTANT]
	> Required `llama.cpp` Version & `ClefModel` Batch Flags:
	> 1. Minimum version: Use `llama.cpp` release `b11430` or newer (which includes native `ClefModel` / `LLM_ARCH_CLEF` graph construction and the `/v1/systemone` HTTP endpoint). Standard Ollama or older `llama.cpp` builds without `ClefModel` support cannot execute `dec.*` decision-head tensors.
	> 2. Set `-b` and `-ub` to match your maximum prompt length (e.g., `-b 16384 -ub 16384 --no-cache-prompt` or `-b 4096 -ub 4096 --no-cache-prompt`):
	> In `llama-server`, `ClefModel` computes cross-attention over `<\|question_\|>` and `<\|option_\|>` spans within a single physical micro-batch (`ubatch`, default `512`). Setting `-ub` equal to `-b` ensures long prompts (>512 tokens) are evaluated in a single physical `ubatch` without splitting option spans across micro-batches, and `--no-cache-prompt` prevents prefix-cache skipping over decision markers.

	#### Step 1: Download Prebuilt `llama-server` (`b11430`+) and GGUF Weights

	```bash
	# 1. Download prebuilt llama.cpp (b11430+) with ClefModel & /v1/systemone support
	# - Linux CUDA 13.x (Colab / Ubuntu): llama-b11430-bin-ubuntu-cuda-13.4-x64.tar.gz
	# - Linux CUDA 12.x: llama-b11430-bin-ubuntu-cuda-12.8-x64.tar.gz
	# - macOS Apple Silicon (M1/M2/M3/M4): llama-b11430-bin-macos-arm64.tar.gz
	curl -sL https://github.com/ggml-org/llama.cpp/releases/download/b11430/llama-b11430-bin-ubuntu-cuda-13.4-x64.tar.gz \| tar -xz

	# 2. Download FastBrain-9B GGUF (choose Q8_0 [9.66 GB, lossless] or Q4_K_M [6.73 GB, 8GB–16GB GPUs])
	pip install -U "huggingface_hub[hf_transfer]"
	HF_HUB_ENABLE_HF_TRANSFER=1 hf download TypeScript/FastBrain-9B FastBrain-9B-Q8_0.gguf --local-dir .
	```

	#### Step 2: Launch `llama-server` on the `/v1/systemone` Endpoint

	```bash
	# On Linux / Google Colab, ensure CUDA driver libraries are in LD_LIBRARY_PATH:
	export LD_LIBRARY_PATH="$(pwd)/llama-b11430:/usr/lib64-nvidia:/usr/local/cuda/lib64:${LD_LIBRARY_PATH}"

	# Launch llama-server (24GB+ GPU / 16K context):
	./llama-b11430/llama-server \
	-m ./FastBrain-9B-Q8_0.gguf \
	-ngl 99 -c 16384 -b 16384 -ub 16384 -np 1 -fa on --no-cache-prompt \
	--port 8080

	# Or on a 12GB–16GB GPU (e.g., Colab Free Tesla T4, 4K context with cuda-12.8):
	# pip install nvidia-cuda-runtime-cu12 nvidia-cublas-cu12
	# export LD_LIBRARY_PATH="$(pwd)/llama-b11430:$(python3 -c 'import glob;print(":".join(glob.glob("/usr/local/lib/python/dist-packages/nvidia//lib")))'):/usr/lib64-nvidia:${LD_LIBRARY_PATH}"
	# ./llama-b11430/llama-server \
	# -m ./FastBrain-9B-Q4_K_M.gguf \
	# -ngl 99 -c 4096 -b 4096 -ub 4096 -np 1 -fa on --no-cache-prompt \
	# --port 8080

	# Optional: For multimodal image/screenshot states, attach the vision projector:
	# --mmproj ./mmproj-FastBrain-9B-Q8_0.gguf
	```

	#### Step 3: Query `/v1/systemone` (`curl` or Python — Single Forward Pass, `0` Output Tokens)

	```bash
	curl http://127.0.0.1:8080/v1/systemone \
	-H "Content-Type: application/json" \
	-d '{
	"model": "FastBrain-9B",
	"state": {
	"ticket_id": "ORD-99281",
	"customer_message": "I was charged twice for my subscription last week and nobody has replied to my ticket."
	},
	"questions": {
	"route": {
	"type": "choice",
	"instructions": "Which team should handle this?",
	"criteria": {
	"billing": "Payments, refunds, duplicate charges, or invoices",
	"technical": "Bugs, crashes, or service outages",
	"account": "Password reset or login issues"
	}
	},
	"urgent": {
	"type": "noul",
	"instructions": "Is the customer reporting a financial or urgent issue?"
	}
	}
	}'
	```

	Python Client Example (`requests` / `urllib`):

	```python
	import requests

	resp = requests.post(
	"http://127.0.0.1:8080/v1/systemone",
	json={
	"model": "FastBrain-9B",
	"state": {
	"query": "Can you check the live wire transfer status for my corporate checking account right now?",
	"available_tools": ["get_account_balance", "list_recent_cards"],
	},
	"questions": {
	"action": {
	"type": "choice",
	"instructions": "Select the best assistant response.",
	"criteria": {
	"direct_answer": "Your wire transfer has been completed.",
	"tool_call": "<tool_call>{\"name\": \"get_account_balance\", \"arguments\": {}}</tool_call>",
	"clarify": "Could you please provide your corporate account ID and wire reference number?",
	"abstain": "I cannot check wire transfer status because no wire-transfer tool is available.",
	},
	}
	},
	},
	timeout=30,
	).json()

	print(resp["choices"]["action"]) # Calibrated probability distribution over all options
	print(resp["usage"]) # {'input_tokens': ..., 'output_tokens': 0, 'total_tokens': ...}
	```

	---

	## GGUF Quantization Accuracy Retention & Speed Benchmarks

	### 1. Decision Index 10-Benchmark Test-Set Accuracy Retention (`BF16` vs. `Q8_0` vs. `Q4_K_M`)

	Evaluated across a stratified 10-benchmark Decision Index test suite (`203` multi-constraint requests / `1,473` scored schema questions spanning `choice`, `noul`, and `score` heads), `FastBrain-9B-Q8_0.gguf` (`9.66 GB`) is virtually lossless (`99.86%` Top-1 decision agreement with native `bfloat16`, `-0.03 pp` score delta), while `FastBrain-9B-Q4_K_M.gguf` (`6.73 GB`, with `dec.` head preserved in `Q8_0`) retains `98.78%` Top-1 agreement* and `-0.20 pp` calibrated 10-task mean:

	\| Benchmark / Metric \| Native PyTorch `bfloat16` (`18.82 GB`) \| `FastBrain-9B-Q8_0.gguf` (`9.66 GB`) \| `FastBrain-9B-Q4_K_M.gguf` (`6.73 GB`, `Q8_0` Head) \|
	\| :--- \| :---: \| :---: \| :---: \|
	\| Top-1 Decision Agreement vs. `bfloat16` (`1,473` questions) \| `100.00%` (Reference) \| `99.86%` (`1,471 / 1,473` identical) \| `98.78%` (`1,455 / 1,473` identical) \|
	\| Probability Mean Absolute Error (`\|p_quant - p_bf16\|`) \| `0.00000` \| `0.00203` (`0.20%`) \| `0.00387` (`0.39%`) \|
	\| 10-Benchmark Mean (Single-Checkpoint Raw) \| `82.37%` \| `82.34%` (`-0.03 pp`) \| `82.56%` (`+0.19 pp`) \|
	\| 10-Benchmark Mean (With Margin Calibration) \| `83.68%` \| `83.65%` (`-0.03 pp`) \| `83.48%` (`-0.20 pp`) \|
	\| ├─ 1. BFCL (AST Tool Selection) \| `100.00%` \| `100.00%` (`0.00`) \| `100.00%` (`0.00`) \|
	\| ├─ 2. ToolRet (20-Candidate Tool Reranking, `score`) \| `73.15%` \| `73.70%` (`+0.55`) \| `75.60%` (`+2.45`) \|
	\| ├─ 3. API-Bank (Multi-Step Stateful Tool Calling) \| `95.00%` \| `95.00%` (`0.00`) \| `95.00%` (`0.00`) \|
	\| ├─ 4. BANKING77 (77-Way Fine-Grained Intent) \| `100.00%` \| `100.00%` (`0.00`) \| `100.00%` (`0.00`) \|
	\| ├─ 5. CLINC150+OOS (Intent + Open-Set `noul` Abstention) \| `93.94%` \| `93.94%` (`0.00`) \| `93.94%` (`0.00`) \|
	\| ├─ 6. Home Appliances (Embedded Device Control) \| `90.00%` \| `90.00%` (`0.00`) \| `90.00%` (`0.00`) \|
	\| ├─ 7. BRIGHT (Reasoning-Intensive Document Ranking) \| `41.93%` \| `41.00%` (`-0.93`) \| `40.31%` (`-1.62`) \|
	\| ├─ 8. Amazon ESCI (Fine-Grained E-Commerce Relevance) \| `73.89%` \| `73.89%` (`0.00`) \| `75.79%` (`+1.90`) \|
	\| ├─ 9. PhishNChips (9-Probe Joint Security Inspection) \| `85.00%` \| `85.00%` (`0.00`) \| `85.00%` (`0.00`) \|
	\| └─ 10. When2Call (4-Way Tool Governance) \| `75.00%` (Raw `70.83%`) \| `75.00%` (Raw `70.83%`, `0.00`) \| `72.92%` (Raw `70.83%`, `-2.08`) \|

	### 2. Measured Hardware Footprint & Inference Speed (`llama-server b11430` CUDA 13.4)

	Because `FastBrain-9B` scores all schema questions and candidate options in a single non-autoregressive prefill forward pass (`output_tokens = 0`), `llama-server` achieves 8,900–12,100+ input tokens/sec (`31–109 ms` end-to-end HTTP latency) on server GPUs:

	\| Workload / Metric \| `Q8_0.gguf`<br>(RTX PRO 6000) \| `Q4_K_M.gguf`<br>(RTX PRO 6000) \| `Q8_0.gguf`<br>(Colab Free T4 16GB) \| `Q4_K_M.gguf`<br>(Colab Free T4 16GB) \| Native `bfloat16`<br>(RTX PRO 6000) \|
	\| :--- \| :---: \| :---: \| :---: \| :---: \| :---: \|
	\| Model Load Time \| `2.56 s` \| `2.00 s` \| `5.43 s` \| `5.10 s` \| `~11.5 s` \|
	\| Peak GPU VRAM \| `12.55 GiB` (16K ctx) \| `10.30 GiB` (16K ctx) \| `9.03 GiB` (`9,245 MiB`) (4K ctx) \| `6.78 GiB` (`6,943 MiB`) (4K ctx) \| `20.3 – 24.7 GiB` \|
	\| Workload 1: Intent Routing + OOS (`293 tok`, `K=2`) \| `32.82 ms` (`8,928 tok/s`) \| `31.69 ms` (`9,245 tok/s`) \| `349.32 ms` (`839 tok/s`) \| `342.98 ms` (`854 tok/s`) \| `~35.0 ms` \|
	\| Workload 2: Tool Governance (`When2Call`) (`1,068 tok`, `K=1`) \| `87.78 ms` (`12,167 tok/s`) \| `89.34 ms` (`11,955 tok/s`) \| `1,149.81 ms` (`929 tok/s`) \| `1,154.31 ms` (`925 tok/s`) \| `~92.0 ms` \|
	\| Workload 3: Security Multi-Probe (`PhishNChips`) (`1,303 tok`, `K=9`) \| `108.55 ms` (`12,004 tok/s`) \| `109.97 ms` (`11,849 tok/s`) \| `1,430.81 ms` (`911 tok/s`) \| `1,440.52 ms` (`905 tok/s`) \| `~115.0 ms` \|

	\| Format / Precision Tier \| Weight Footprint \| Peak VRAM (`4K`–`16K` ctx) \| Minimum Recommended Hardware \|
	\| :--- \| :---: \| :---: \| :--- \|
	\| `FastBrain-9B-Q4_K_M.gguf` (4-bit + `Q8_0` Head) \| `6.73 GB` \| `~7.8 – 10.3 GiB` \| 8GB–12GB GPU / Colab Free T4 (16GB) / Mac M1–M4 \|
	\| PyTorch `load_in_4bit=True` (NF4 + `fp16` Head) \| `~5.5 GB` \| `~6.2 – 6.8 GiB` \| Colab Free T4 (16GB) / RTX 3060–4060 (8GB+) \|
	\| `FastBrain-9B-Q8_0.gguf` (Full 8-bit GGUF) \| `9.66 GB` \| `~10.8 – 12.6 GiB` \| 12GB–16GB GPU / Colab T4 / Mac 16GB+ \|
	\| PyTorch `load_in_8bit=True` (INT8 + `bf16` Head) \| `~9.6 GB` \| `~11.2 GiB` \| Colab T4 (16GB) / RTX 4070 Ti (12GB+) \|
	\| Native `bfloat16` (`safetensors` / `BF16.gguf`) \| `18.82 GB` (`18.16 GB`) \| `20.3 – 24.7 GiB` \| Colab L4 (24GB) / RTX 3090–4090 / PRO 6000 (96GB) \|

	---

	## Repository Structure

	\| File \| Size \| Description \|
	\| :--- \| :---: \| :--- \|
	\| `model-00001-of-00004.safetensors` .. `00004` \| `18.59 GB` \| Full `9B` backbone weights (`bfloat16`, in-place merged, no LoRA required) \|
	\| `joint_head.safetensors` & `joint_head_config.json` \| `232 MB` \| `116M`-parameter `JointSchemaHead` weights (`122` tensors) and configuration \|
	\| `FastBrain-9B-Q4_K_M.gguf` \| `6.73 GB` \| Recommended 4-bit GGUF (`dec.*` decision head, `ssm_`, and `attn_` preserved at `Q8_0`) \|
	\| `FastBrain-9B-Q8_0.gguf` \| `9.66 GB` \| High-precision 8-bit GGUF (`ClefModel` architecture for `llama-server`) \|
	\| `FastBrain-9B-BF16.gguf` \| `18.16 GB` \| Full-precision `bfloat16` GGUF (`ClefModel` architecture for `llama-server`) \|
	\| `mmproj-FastBrain-9B-Q8_0.gguf` & `BF16.gguf` \| `624 MB` / `922 MB` \| Multimodal vision projector GGUF files for image/screenshot states \|
	\| `joint_schema_model.py` \| `25 KB` \| `ClefModel`, `JointSchemaHead`, `load_release_model(load_in_4bit/8bit)`, and `systemone()` runtime \|
	\| `config.json`, `tokenizer.json`, `processor_config.json` \| `11 MB` \| Standard Hugging Face model, tokenizer, and processor configurations \|
	\| `assets/*.png` \| `1.2 MB` \| High-resolution technical benchmark and calibration charts \|