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

TypeScript

Add native 4-bit/8-bit mixed-precision loading in joint_schema_model.py and GGUF documentation

f84a65d verified 4 days ago

preview code

Raw History Blame

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---
base_model: Cloudflare/clef-flash
library_name: transformers
license: apache-2.0
pipeline_tag: text-generation
tags:
  - fastbrain
  - clef
  - gguf
  - systemone
  - decision-index
  - tool-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 (`Q4_K_M` / `Q8_0` / `BF16`) via `/v1/systemone`
This repository includes official `llama.cpp` (`b11430`+ / `ClefModel`) GGUF quantizations that pack both the `Qwen3.5-9B` hybrid backbone and the `116M` `JointSchemaHead` (`dec.*` tensors preserved at `Q8_0` precision):

```bash
# Start llama-server on the /v1/systemone decision endpoint (6.04 GB Q4_K_M quant)
llama-server -hf TypeScript/FastBrain-9B:Q4_K_M --port 8080
# Query the single-forward-pass /v1/systemone endpoint
curl http://127.0.0.1:8080/v1/systemone \
  -H "Content-Type: application/json" \
  -d '{
    "model": "FastBrain-9B",
    "state": "Customer message: I was charged twice for my order last week and nobody has replied.",
    "questions": {
      "route": {
        "type": "choice",
        "instructions": "Which team should handle this?",
        "criteria": {"billing": "Payments or invoices", "technical": "Bugs or outages"}
      },
      "urgent": {"type": "noul", "instructions": "Is the customer requesting urgent resolution?"}
    }
  }'
```

---

## Hardware Footprint, Quantization Tiers & Inference Speed

| Format / Precision Tier | Weight Footprint | Peak VRAM | Minimum Hardware | Typical Latency (`500–1k tok`) |
| :--- | :---: | :---: | :--- | :---: |
| **`FastBrain-9B-Q4_K_M.gguf`** *(4-bit + `Q8_0` Head)* | `6.04 GB` | `~7.2 GB` | **8GB GPU / Colab Free T4 (16GB) / Mac M1–M4** | `~90–220 ms` |
| **PyTorch `load_in_4bit=True`** *(NF4 + `fp16` Head)* | `~5.5 GB` | `~6.8 GB` | **Colab Free T4 (16GB) / RTX 3060–4060 (8GB+)** | `~180–280 ms` |
| **`FastBrain-9B-Q8_0.gguf`** *(Full 8-bit GGUF)* | `9.68 GB` | `~11.0 GB` | **12GB–16GB GPU / Colab T4 / Mac 16GB+** | `~80–180 ms` |
| **PyTorch `load_in_8bit=True`** *(INT8 + `bf16` Head)* | `~9.6 GB` | `~11.2 GB` | **Colab T4 (16GB) / RTX 4070 Ti (12GB+)** | `~130–210 ms` |
| **Native `bfloat16` (`safetensors` / `BF16.gguf`)** | `18.82 GB` (`18.16 GB`) | `20.3–24.7 GB` | **Colab L4 (24GB) / RTX 3090–4090 / PRO 6000 (96GB)** | **`38.8 ms` (`B=1`) · `61.7 ms` (`B=16` on G4 6000)** |
---
## 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` | `6.04 GB` | Recommended 4-bit GGUF (`dec.*` decision head, `ssm_`, and `attn_` preserved at `Q8_0`) |
| `FastBrain-9B-Q8_0.gguf` | `9.68 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 |

	---
	base_model: Cloudflare/clef-flash
	library_name: transformers
	license: apache-2.0
	pipeline_tag: text-generation
	tags:
	- fastbrain
	- clef
	- gguf
	- systemone
	- decision-index
	- tool-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 (`Q4_K_M` / `Q8_0` / `BF16`) via `/v1/systemone`

	This repository includes official `llama.cpp` (`b11430`+ / `ClefModel`) GGUF quantizations that pack both the `Qwen3.5-9B` hybrid backbone and the `116M` `JointSchemaHead` (`dec.*` tensors preserved at `Q8_0` precision):

	```bash
	# Start llama-server on the /v1/systemone decision endpoint (6.04 GB Q4_K_M quant)
	llama-server -hf TypeScript/FastBrain-9B:Q4_K_M --port 8080

	# Query the single-forward-pass /v1/systemone endpoint
	curl http://127.0.0.1:8080/v1/systemone \
	-H "Content-Type: application/json" \
	-d '{
	"model": "FastBrain-9B",
	"state": "Customer message: I was charged twice for my order last week and nobody has replied.",
	"questions": {
	"route": {
	"type": "choice",
	"instructions": "Which team should handle this?",
	"criteria": {"billing": "Payments or invoices", "technical": "Bugs or outages"}
	},
	"urgent": {"type": "noul", "instructions": "Is the customer requesting urgent resolution?"}
	}
	}'
	```

	---

	## Hardware Footprint, Quantization Tiers & Inference Speed

	\| Format / Precision Tier \| Weight Footprint \| Peak VRAM \| Minimum Hardware \| Typical Latency (`500–1k tok`) \|
	\| :--- \| :---: \| :---: \| :--- \| :---: \|
	\| `FastBrain-9B-Q4_K_M.gguf` (4-bit + `Q8_0` Head) \| `6.04 GB` \| `~7.2 GB` \| 8GB GPU / Colab Free T4 (16GB) / Mac M1–M4 \| `~90–220 ms` \|
	\| PyTorch `load_in_4bit=True` (NF4 + `fp16` Head) \| `~5.5 GB` \| `~6.8 GB` \| Colab Free T4 (16GB) / RTX 3060–4060 (8GB+) \| `~180–280 ms` \|
	\| `FastBrain-9B-Q8_0.gguf` (Full 8-bit GGUF) \| `9.68 GB` \| `~11.0 GB` \| 12GB–16GB GPU / Colab T4 / Mac 16GB+ \| `~80–180 ms` \|
	\| PyTorch `load_in_8bit=True` (INT8 + `bf16` Head) \| `~9.6 GB` \| `~11.2 GB` \| Colab T4 (16GB) / RTX 4070 Ti (12GB+) \| `~130–210 ms` \|
	\| Native `bfloat16` (`safetensors` / `BF16.gguf`) \| `18.82 GB` (`18.16 GB`) \| `20.3–24.7 GB` \| Colab L4 (24GB) / RTX 3090–4090 / PRO 6000 (96GB) \| `38.8 ms` (`B=1`) · `61.7 ms` (`B=16` on G4 6000) \|

	---

	## 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` \| `6.04 GB` \| Recommended 4-bit GGUF (`dec.*` decision head, `ssm_`, and `attn_` preserved at `Q8_0`) \|
	\| `FastBrain-9B-Q8_0.gguf` \| `9.68 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 \|