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

9bac3c8

verified ·

1 Parent(s): 6ed6ac0

Sync Model Card badges, repository structure, and .eval_results YAML with merged PR #58

Browse files

Files changed (2) hide show

.eval_results/TypeScript__FastBrain-9B.yaml +10 -10
README.md +6 -2

.eval_results/TypeScript__FastBrain-9B.yaml CHANGED Viewed

@@ -1,32 +1,32 @@
 - dataset:
     id: LocalLLaMA/typed-decisions
     task_id: accuracy
-  value: 0.7625
   date: "2026-10-31"
   source:
-    url: https://huggingface.co/datasets/LocalLLaMA/typed-decisions
-    name: "Typed Decisions benchmark run 2026-10-31 (test split, 400 cases / 2,000 decisions)"
 - dataset:
     id: LocalLLaMA/typed-decisions
     task_id: kl_from_gold
   value: 0.1508
   date: "2026-10-31"
   source:
-    url: https://huggingface.co/datasets/LocalLLaMA/typed-decisions
-    name: "Typed Decisions benchmark run 2026-10-31 (test split, 400 cases / 2,000 decisions)"
 - dataset:
     id: LocalLLaMA/typed-decisions
     task_id: brier
   value: 0.0795
   date: "2026-10-31"
   source:
-    url: https://huggingface.co/datasets/LocalLLaMA/typed-decisions
-    name: "Typed Decisions benchmark run 2026-10-31 (test split, 400 cases / 2,000 decisions)"
 - dataset:
     id: LocalLLaMA/typed-decisions
     task_id: ece
-  value: 0.0235
   date: "2026-10-31"
   source:
-    url: https://huggingface.co/datasets/LocalLLaMA/typed-decisions
-    name: "Typed Decisions benchmark run 2026-10-31 (test split, 400 cases / 2,000 decisions)"

 - dataset:
     id: LocalLLaMA/typed-decisions
     task_id: accuracy
+  value: 0.7525
   date: "2026-10-31"
   source:
+    url: https://huggingface.co/datasets/LocalLLaMA/typed-decisions/discussions/58
+    name: "Typed Decisions official zero-shot leaderboard (PR #58, bench/score_with_jobs.py)"
 - dataset:
     id: LocalLLaMA/typed-decisions
     task_id: kl_from_gold
   value: 0.1508
   date: "2026-10-31"
   source:
+    url: https://huggingface.co/datasets/LocalLLaMA/typed-decisions/discussions/58
+    name: "Typed Decisions official zero-shot leaderboard (PR #58, bench/score_with_jobs.py)"
 - dataset:
     id: LocalLLaMA/typed-decisions
     task_id: brier
   value: 0.0795
   date: "2026-10-31"
   source:
+    url: https://huggingface.co/datasets/LocalLLaMA/typed-decisions/discussions/58
+    name: "Typed Decisions official zero-shot leaderboard (PR #58, bench/score_with_jobs.py)"
 - dataset:
     id: LocalLLaMA/typed-decisions
     task_id: ece
+  value: 0.0947
   date: "2026-10-31"
   source:
+    url: https://huggingface.co/datasets/LocalLLaMA/typed-decisions/discussions/58
+    name: "Typed Decisions official zero-shot leaderboard (PR #58, bench/score_with_jobs.py)"

README.md CHANGED Viewed

@@ -32,8 +32,8 @@ 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--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>
@@ -396,5 +396,9 @@ Because `FastBrain-9B` scores all schema questions and candidate options in a **
 | `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 |

 [![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-bench--localllamatyped-decisions)
+[![Typed Decisions](https://img.shields.io/badge/Typed_Decisions-%237_%2F_42_Zero--Shot_Live_(%231_Clef--Arch)-8b5cf6?style=flat-square)](#external-community-benchmarks-safeagents-judge-bench--localllamatyped-decisions)
 [![License](https://img.shields.io/badge/License-Apache_2.0-6366f1?style=flat-square)](./LICENSE)
 </div>
 | `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 |
+| `eval_submissions/typescript_fastbrain-9b.json` | `901 KB` | Full 400-case / 2,000-decision Zero-Shot prediction trace for `LocalLLaMA/typed-decisions` ([PR #58](https://huggingface.co/datasets/LocalLLaMA/typed-decisions/discussions/58)) |
+| `eval_submissions/typescript_fastbrain-9b-fitted.json` | `902 KB` | Full 400-case / 2,000-decision Fitted (`train`-calibrated) prediction trace for `LocalLLaMA/typed-decisions` ([PR #57](https://huggingface.co/datasets/LocalLLaMA/typed-decisions/discussions/57)) |
+| `eval_submissions/TypeScript__FastBrain-9B*.jsonl` | `205 KB` | Full 562-call binary & 7-way category prediction traces for `harness-hub/safeagents-judge-bench` ([PR #1](https://huggingface.co/datasets/harness-hub/safeagents-judge-bench/discussions/1)) |
+| `.eval_results/*.yaml` | `2.3 KB` | Official Hugging Face Hub benchmark evaluation metadata (`typed-decisions` & `safeagents-judge-bench`) |
 | `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 |