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

6ed6ac0

verified ·

1 Parent(s): 3cbcb13

Publish typescript_fastbrain-9b-fitted.json trace and sync Model Card with PR #58

Browse files

Files changed (2) hide show

README.md +7 -5
eval_submissions/typescript_fastbrain-9b-fitted.json +0 -0

README.md CHANGED Viewed

@@ -54,9 +54,9 @@ All fine-tuned backbone updates (`104` projection layers across all `32` hybrid
 | :--- | :---: | :---: | :---: | :---: | :--- |
 | **[`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 |
 ---
@@ -111,11 +111,13 @@ Evaluated on the official **Cloudflare Decision Index (v0.2.1)** across **10 ben
 #### 2. [`LocalLLaMA/typed-decisions`](https://huggingface.co/datasets/LocalLLaMA/typed-decisions) (400 Test Cases · 2,000 Typed Decisions Across 4 Enterprise Workflows)
 | Model | Params | Overall Acc ↑ | KL from Gold ↓ | Brier ↓ | `noul` Acc ↑ | `choice` Acc ↑ | `score` Acc ↑ | Agent Trace ↑ | Customer Svc ↑ | Invoice Proc ↑ | Security Inc ↑ |
 | :--- | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: |
-| **`TypeScript/FastBrain-9B` *(Fitted · Ours)*** | **9.4B** | **80.70%** *(#1)* | **0.0816** | **0.0437** | **89.0%** | **78.8%** | **76.3%** | **75.0%** | **83.0%** | **83.0%** | **78.0%** |
 | **`TypeScript/FastBrain-9B` *(Ordinal-KL Distilled Raw)*** | **9.4B** | **77.35%** *(#1 Raw)* | **0.1039** | **0.0582** | **84.8%** | **74.8%** | **73.8%** | **71.2%** | **80.4%** | **82.6%** | **75.2%** |
-| **`TypeScript/FastBrain-9B` *(Zero-Shot · Ours)*** | **9.4B** | **76.25%** *(#1 9B)* | **0.1508** | **0.0795** | **84.3%** | **76.0%** | **72.5%** | **69.8%** | **78.8%** | **82.0%** | **74.4%** |
 | **`TypeScript/FastBrain-9B` *(Base Raw Head)*** | **9.4B** | **71.80%** *(>Flash)* | **0.2021** | **0.1059** | **81.7%** | **71.7%** | **64.6%** | **64.8%** | 75.8% | **77.2%** | 69.4% |
 | `Cloudflare/clef` | 27.4B | 72.75% | 0.1907 | 0.1032 | 84.5% | 64.8% | 69.9% | 63.8% | 74.2% | 78.6% | 74.4% |
 | `Cloudflare/clef-flash` | 9.4B | 71.00% | 0.2093 | 0.1105 | 81.5% | 71.3% | 62.9% | 62.0% | 76.0% | 76.4% | 69.6% |

 | :--- | :---: | :---: | :---: | :---: | :--- |
 | **[`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>([Merged PR #58](https://huggingface.co/datasets/LocalLLaMA/typed-decisions/discussions/58) · [Trace](./eval_submissions/typescript_fastbrain-9b.json)) | **42 Models** | **`75.25%` / `76.25%` Acc**<br>*(KL `0.1508` · Brier `0.0795`)* | **#7 / 42 *(Live)***<br>*(#5 Tied-Top)* | **Top 16.7% *(Live)***<br>*(Top 11.9%)* | `vllm-sr/d3` *(27B, #8 `74.9%`)*, `Baseten Elephant 1` *(9B, #9 `74.8%`)*, `Darwin-27B` *(#10 `74.3%`)*, `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** | **`75.25%` / `76.25%` Zero-Shot**<br>*(Distilled Raw: **`77.35%`**)* | **#2 / 19 *(Live)***<br>**#1 Clef-Arch** | **Top 10.5%** | Outperforms 17 of 18 `<=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) · [Trace](./eval_submissions/typescript_fastbrain-9b-fitted.json)) | **36 Models** | **`79.70%` / `80.70%` Acc**<br>*(KL `0.0816` · Brier `0.0437`)* | **#1 (`9B` Class)**<br>**Top 9 / 36 Overall** | **Top 13.9%–25.0%** | `OpenDecider-small-td` *(4B, `79.2%`)*, `nullspan` *(395M, `79.0%`)*, `OpenDecider-medium-td` *(30B-A3B, `78.8%`)*, `Qwen3.5-4B-td` *(`77.25%`)*, `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 |
 ---
 #### 2. [`LocalLLaMA/typed-decisions`](https://huggingface.co/datasets/LocalLLaMA/typed-decisions) (400 Test Cases · 2,000 Typed Decisions Across 4 Enterprise Workflows)
+> **Scoring Protocol Note**: Both full 400-case / 2,000-decision prediction traces are published in [`eval_submissions/typescript_fastbrain-9b.json`](./eval_submissions/typescript_fastbrain-9b.json) (zero-shot, merged at `#7` via [PR #58](https://huggingface.co/datasets/LocalLLaMA/typed-decisions/discussions/58)) and [`eval_submissions/typescript_fastbrain-9b-fitted.json`](./eval_submissions/typescript_fastbrain-9b-fitted.json) (fitted on `train`). Under `bench/score_with_jobs.py` (which breaks ties in `gold` by taking the first key `g.index(max(g))`), they score **`75.25%` acc (`0.1508` KL / `0.0795` Brier)** zero-shot and **`79.70%` acc (`0.0816` KL / `0.0437` Brier)** fitted; under `scripts/score_model.py` (which counts any tied top option in `gold` as correct across the 20 tied decisions), they score **`76.25%`** zero-shot and **`80.70%`** fitted, with identical KL and Brier.
 | Model | Params | Overall Acc ↑ | KL from Gold ↓ | Brier ↓ | `noul` Acc ↑ | `choice` Acc ↑ | `score` Acc ↑ | Agent Trace ↑ | Customer Svc ↑ | Invoice Proc ↑ | Security Inc ↑ |
 | :--- | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: |
+| **`TypeScript/FastBrain-9B` *(Fitted · Ours)*** | **9.4B** | **79.70% / 80.70%** | **0.0816** | **0.0437** | **86.7% / 88.0%** | **76.5% / 77.2%** | **76.9% / 77.9%** | **75.4% / 76.6%** | **82.0% / 83.4%** | **83.4% / 83.6%** | **78.0% / 79.2%** |
 | **`TypeScript/FastBrain-9B` *(Ordinal-KL Distilled Raw)*** | **9.4B** | **77.35%** *(#1 Raw)* | **0.1039** | **0.0582** | **84.8%** | **74.8%** | **73.8%** | **71.2%** | **80.4%** | **82.6%** | **75.2%** |
+| **`TypeScript/FastBrain-9B` *(Zero-Shot · Ours)*** | **9.4B** | **75.25% / 76.25%** | **0.1508** | **0.0795** | **83.2% / 84.3%** | **74.7% / 76.0%** | **69.8% / 70.4%** | **68.2% / 69.8%** | **77.6% / 78.8%** | **82.0%** | **73.2% / 74.4%** |
 | **`TypeScript/FastBrain-9B` *(Base Raw Head)*** | **9.4B** | **71.80%** *(>Flash)* | **0.2021** | **0.1059** | **81.7%** | **71.7%** | **64.6%** | **64.8%** | 75.8% | **77.2%** | 69.4% |
 | `Cloudflare/clef` | 27.4B | 72.75% | 0.1907 | 0.1032 | 84.5% | 64.8% | 69.9% | 63.8% | 74.2% | 78.6% | 74.4% |
 | `Cloudflare/clef-flash` | 9.4B | 71.00% | 0.2093 | 0.1105 | 81.5% | 71.3% | 62.9% | 62.0% | 76.0% | 76.4% | 69.6% |

eval_submissions/typescript_fastbrain-9b-fitted.json ADDED Viewed

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