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 3 days ago

Commit

c6fe209

verified ·

1 Parent(s): d8f70e4

Add Tesla T4 and RTX PRO 6000 GGUF benchmarks and complete YAML metadata

Browse files

Files changed (1) hide show

README.md +11 -9

README.md CHANGED Viewed

@@ -166,10 +166,12 @@ export LD_LIBRARY_PATH="$(pwd)/llama-b11430:/usr/lib64-nvidia:/usr/local/cuda/li
   -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):
 # ./llama-b11430/llama-server \
 #   -m ./FastBrain-9B-Q4_K_M.gguf \
-#   -ngl 99 -c 4096 -b 4096 -ub 4096 -np 1 --no-cache-prompt \
 #   --port 8080
 # Optional: For multimodal image/screenshot states, attach the vision projector:
@@ -267,13 +269,13 @@ Evaluated across a stratified 10-benchmark Decision Index test suite (**`203` mu
 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 | `FastBrain-9B-Q8_0.gguf`<br>*(9.66 GB · RTX PRO 6000)* | `FastBrain-9B-Q4_K_M.gguf`<br>*(6.73 GB · RTX PRO 6000)* | Native PyTorch `bfloat16`<br>*(18.82 GB · RTX PRO 6000)* |
-| :--- | :---: | :---: | :---: |
-| **Model Load Time** | **`2.56 s`** | **`2.00 s`** | `~11.5 s` |
-| **Peak GPU VRAM** *(incl. `16K` context & batch buffers)* | **`12.55 GiB` (`12,847 MiB`)** | **`10.30 GiB` (`10,545 MiB`)** | `20.3 – 24.7 GiB` |
-| **Workload 1: Intent Routing + OOS** (`293 tok`, `K=2` questions) | **`32.82 ms`** (`8,928 tok/s` · `60.9 dec/s`) | **`31.69 ms`** (`9,245 tok/s` · `63.1 dec/s`) | `~35.0 ms` |
-| **Workload 2: Tool Governance (`When2Call`)** (`1,068 tok`, `K=1`, `M=4`) | **`87.78 ms`** (`12,167 tok/s`) | **`89.34 ms`** (`11,955 tok/s`) | `~92.0 ms` |
-| **Workload 3: Security Multi-Probe (`PhishNChips`)** (`1,303 tok`, `K=9` questions) | **`108.55 ms`** (`12,004 tok/s` · `82.9 dec/s`) | **`109.97 ms`** (`11,849 tok/s` · `81.8 dec/s`) | `~115.0 ms` |
 | Format / Precision Tier | Weight Footprint | Peak VRAM (`4K`–`16K` ctx) | Minimum Recommended Hardware |
 | :--- | :---: | :---: | :--- |

   -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:
 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 |
 | :--- | :---: | :---: | :--- |