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

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metadata

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

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 (`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).

Required llama.cpp Version & ClefModel Batch Flags:

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.

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

# 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

# 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)

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):

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`
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` (RTX PRO 6000)	`Q4_K_M.gguf` (RTX PRO 6000)	`Q8_0.gguf` (Colab Free T4 16GB)	`Q4_K_M.gguf` (Colab Free T4 16GB)	Native `bfloat16` (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

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 (Q8_0 / Q4_K_M / BF16) via /v1/systemone

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

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

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

GGUF Quantization Accuracy Retention & Speed Benchmarks

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

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

Repository Structure

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

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

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

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

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

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

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