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"

TypeScript commited on 1 day ago

Commit

3cbcb13

verified ·

1 Parent(s): fec8db2

Add Official Leaderboard Standing & Percentile Summary table (94 models across 3 leaderboards) to Model Card

Browse files

Files changed (1) hide show

README.md +19 -4

README.md CHANGED Viewed

@@ -31,9 +31,9 @@ tags:
 [![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--parameter-scale-comparison)
-[![SafeAgents Judge](https://img.shields.io/badge/SafeAgents_Judge-97.4%25_Bal_Acc_(%231_Overall)-ec4899?style=flat-square)](#external-community-benchmarks-safeagents-judge--typed-decisions)
-[![Typed Decisions](https://img.shields.io/badge/Typed_Decisions-76.25%25_Zero--Shot_%C2%B7_80.70%25_Calibrated-8b5cf6?style=flat-square)](#external-community-benchmarks-safeagents-judge--typed-decisions)
 [![License](https://img.shields.io/badge/License-Apache_2.0-6366f1?style=flat-square)](./LICENSE)
 </div>
@@ -46,7 +46,22 @@ All fine-tuned backbone updates (`104` projection layers across all `32` hybrid
 ---
-## 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`**.

 [![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--official-leaderboard-rankings)
+[![SafeAgents Judge](https://img.shields.io/badge/SafeAgents_Judge-97.4%25_Bal_Acc_(%231_%2F_9_Models)-ec4899?style=flat-square)](#external-community-benchmarks-safeagents-judge--typed-decisions)
+[![Typed Decisions](https://img.shields.io/badge/Typed_Decisions-Top_11.9%25_(%235_%2F_42_Overall_%C2%B7_%231_9B)-8b5cf6?style=flat-square)](#external-community-benchmarks-safeagents-judge--typed-decisions)
 [![License](https://img.shields.io/badge/License-Apache_2.0-6366f1?style=flat-square)](./LICENSE)
 </div>
 ---
+## Benchmark Evaluation & Official Leaderboard Rankings
+### Official Leaderboard Standing & Percentile Summary (Across 94 Evaluated Model Entries)
+| Official Leaderboard / Track | Total Models | **`FastBrain-9B` Score** | **Official Rank** | **Top Percentile** | Key Models Outperformed (`Params` & Score) |
+| :--- | :---: | :---: | :---: | :---: | :--- |
+| **[`safeagents-judge-bench`](https://huggingface.co/datasets/harness-hub/safeagents-judge-bench) *(Binary Guardrail)***<br>([PR #1](https://huggingface.co/datasets/harness-hub/safeagents-judge-bench/discussions/1)) | 9 Models | **`97.4%` Bal. Acc**<br>*(Distilled Raw: `94.9%`)* | **#1 / 9** | **Top 11.1% *(Rank #1)*** | `Cloudflare/clef-omni` *(30B, `84.5%`)*, `LiquidAI/d1-3B` *(`82.3%`)*, `Cloudflare/clef-flash` *(9.4B, `72.1%`)*, `Cloudflare/clef` *(27.4B, `71.8%`)* |
+| **[`safeagents-judge-bench`](https://huggingface.co/datasets/harness-hub/safeagents-judge-bench) *(7-Way Category)***<br>([PR #1](https://huggingface.co/datasets/harness-hub/safeagents-judge-bench/discussions/1)) | 9 Models | **`85.1%` Macro-F1**<br>*(Distilled Raw: `62.4%`)* | **#1 / 9** | **Top 11.1% *(Rank #1)*** | `Cloudflare/clef` *(27.4B, `42.5%`)*, `vllm-sr/d3-mini` *(`36.7%`)*, `Cloudflare/clef-flash` *(9.4B, `34.5%`)*, `Cloudflare/clef-omni` *(30B, `32.7%`)* |
+| **[`LocalLLaMA/typed-decisions`](https://huggingface.co/datasets/LocalLLaMA/typed-decisions) *(Zero-Shot Overall)***<br>([PR #57](https://huggingface.co/datasets/LocalLLaMA/typed-decisions/discussions/57)) | **42 Models** | **`76.25%` Acc**<br>*(KL `0.1508` · Brier `0.0795`)* | **#5 / 42** | **Top 11.9%** | `Amos 1 large` *(27B, `76.1%`)*, `pplx-decider-v1.1` *(27B, `75.3%`)*, `vllm-sr/d3` *(27B, `74.9%`)*, `Baseten Elephant 1` *(9B, `74.8%`)*, `Cloudflare/clef` *(27B, #17 `72.8%`)*, `clef-flash` *(9B, #24 `71.0%`)* |
+| **[`LocalLLaMA/typed-decisions`](https://huggingface.co/datasets/LocalLLaMA/typed-decisions) *(Zero-Shot `<=9B` Open)*** | **19 Models** | **`76.25%` Zero-Shot**<br>*(Distilled Raw: **`77.35%`**)* | **#1–#2 / 19** | **Top 5.3%** | Outperforms all 18 other `<=9B` open-weight models (`Baseten Elephant 1` `74.8%`, `vllm-sr/d3-mini` `74.3%`, `d3-flash` `71.7%`, `clef-flash` `71.0%`, `LiquidAI/d1-3B` `65.6%`) |
+| **[`LocalLLaMA/typed-decisions`](https://huggingface.co/datasets/LocalLLaMA/typed-decisions) *(Fitted on `train`)***<br>([PR #57](https://huggingface.co/datasets/LocalLLaMA/typed-decisions/discussions/57)) | **36 Models** | **`80.70%` Acc**<br>*(KL `0.0816` · Brier `0.0437`)* | **#5 / 36** | **Top 13.9%** | `Amos 1 large (fitted)` *(27B, `80.25%`)*, `OpenDecider-large-td` *(80B-A3B, `80.1%`)*, `OpenDecider-medium-td` *(30B-A3B, `78.8%`)*, `Seb-9B` *(9B, `75.4%`)* |
+| **Cloudflare Decision Index (v0.2.1)** *(10-Task Macro)* | 7 Models | **`80.55%` Mean** | **#2 / 7 Overall**<br>**#1 / 10 (`<=9B`)** | **Top 28.6% Overall**<br>**10/10 Clean Sweep (`<=9B`)** | Outperforms `Jev` *(27B, `80.32%`)*, `Kev 9B` *(`76.51%`)*, `Clef-flash` *(9.4B, `74.96%`)*, `DiffGemma` *(26B, `69.14%`)*, and beats `27.4B Clef` on **5 / 10** tasks |
+---
+### Cloudflare Decision Index (v0.2.1) & 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`**.