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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metadata

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

FastBrain-9B

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

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).

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

Fine-Grained Decision Calibration & Open-Set Reliability

Summary Comparison by Model Parameter Scale

Benchmark	Metric	`Laya` (0.4B / 421M)	`DiffusionGemma Jev` (26B-A4B · 4B Act)	`Clef-flash` (9.4B Base)	`Kev 9B` (9B Dense)	`Jev` (27B Dense)	`Clef` (27.4B Flagship)	FastBrain-9B (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

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.
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).
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:

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):

# 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

FastBrain-9B

Benchmark Evaluation & Parameter Scale Comparison

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

Fine-Grained Decision Calibration & Open-Set Reliability

Summary Comparison by Model Parameter Scale

Architecture

Quickstart

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

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

Hardware Footprint, Quantization Tiers & Inference Speed

Repository Structure

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

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