Instructions to use riposta/CrossbowReviewer-9B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use riposta/CrossbowReviewer-9B with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="riposta/CrossbowReviewer-9B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("riposta/CrossbowReviewer-9B")
model = AutoModelForCausalLM.from_pretrained("riposta/CrossbowReviewer-9B", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.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(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use riposta/CrossbowReviewer-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 riposta/CrossbowReviewer-9B:Q8_0
# Run inference directly in the terminal:
llama cli -hf riposta/CrossbowReviewer-9B:Q8_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf riposta/CrossbowReviewer-9B:Q8_0
# Run inference directly in the terminal:
llama cli -hf riposta/CrossbowReviewer-9B:Q8_0

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 riposta/CrossbowReviewer-9B:Q8_0
# Run inference directly in the terminal:
./build/bin/llama cli -hf riposta/CrossbowReviewer-9B:Q8_0

Use Docker

docker model run hf.co/riposta/CrossbowReviewer-9B:Q8_0

Llama new
LM Studio
Jan

vLLM

How to use riposta/CrossbowReviewer-9B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "riposta/CrossbowReviewer-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": "riposta/CrossbowReviewer-9B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/riposta/CrossbowReviewer-9B:Q8_0

SGLang

How to use riposta/CrossbowReviewer-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 "riposta/CrossbowReviewer-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": "riposta/CrossbowReviewer-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 "riposta/CrossbowReviewer-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": "riposta/CrossbowReviewer-9B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use riposta/CrossbowReviewer-9B with Ollama:
```
ollama run hf.co/riposta/CrossbowReviewer-9B:Q8_0
```
Unsloth Desktop

How to use riposta/CrossbowReviewer-9B with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf riposta/CrossbowReviewer-9B:Q8_0

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": "riposta/CrossbowReviewer-9B:Q8_0"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use riposta/CrossbowReviewer-9B with Docker Model Runner:
```
docker model run hf.co/riposta/CrossbowReviewer-9B:Q8_0
```

Lemonade

How to use riposta/CrossbowReviewer-9B with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull riposta/CrossbowReviewer-9B:Q8_0

Run and chat with the model

lemonade run user.CrossbowReviewer-9B-Q8_0

List all available models

lemonade list

Hermes Agent

How to use riposta/CrossbowReviewer-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 riposta/CrossbowReviewer-9B:Q8_0

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 riposta/CrossbowReviewer-9B:Q8_0

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use riposta/CrossbowReviewer-9B with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf riposta/CrossbowReviewer-9B:Q8_0

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 "riposta/CrossbowReviewer-9B:Q8_0" \
  --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"

CrossbowReviewer-9B / docs /QUICKSTART.md

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Quickstart

The inference server lives at github.com/riposta/CrossbowReviewer. It has two backends that return the same decisions.

Backend	Weights	Hardware	Speed (one file, ~16 questions)
`transformers` (default)	BF16 safetensors (17.9 GB)	CUDA GPU ≥ 24 GB, Apple Silicon ≥ 32 GB, or CPU	~0.2 s on an A100
`llamacpp`	Q8_0 GGUF (9.5 GB)	GPU ≥ 12 GB, Apple Silicon ≥ 16 GB, or CPU	~3 s on an M5 Pro (Metal)

transformers backend

git clone https://github.com/riposta/CrossbowReviewer && cd CrossbowReviewer
uv run uvicorn server:app --port 8000

On the first request the server downloads riposta/CrossbowReviewer-9B. To use a local copy, set MODEL:

huggingface-cli download riposta/CrossbowReviewer-9B --exclude "gguf/*" --local-dir CrossbowReviewer-9B
MODEL=./CrossbowReviewer-9B uv run uvicorn server:app --port 8000

llama.cpp backend

huggingface-cli download riposta/CrossbowReviewer-9B gguf/CrossbowReviewer-9B-Q8_0.gguf \
  tokenizer.json tokenizer_config.json decision_config.json --local-dir CrossbowReviewer-9B

# 1. llama.cpp with Qwen3.5 support (a recent build)
llama-server -m CrossbowReviewer-9B/gguf/CrossbowReviewer-9B-Q8_0.gguf --port 8080 -ngl 99 -c 16384

# 2. the decision server in front of it
BACKEND=llamacpp LLAMA_URL=http://127.0.0.1:8080 MODEL=./CrossbowReviewer-9B \
  uv run uvicorn server:app --port 8000

How the llama.cpp backend works:

The decision server tokenizes the prompt with the model's own tokenizer.
It then asks llama-server for the next-token log-probabilities at each answer slot, on a growing token prefix.
llama-server's prompt cache evaluates only the new tokens of each question.

Requests

All built-in rules for a language:

curl -s localhost:8000/v1/systemone -H 'content-type: application/json' \
  -d '{"state": {"language": "java", "code": "public class A { void run() { try { db.save(); } catch (Exception e) {} } }"}}'

Selected rules and custom questions:

curl -s localhost:8000/v1/systemone -H 'content-type: application/json' -d '{
  "state": {"language": "go", "code": "..."},
  "questions": {
    "swallowed_exceptions": {"type": "noul", "instructions": "Is the code free of swallowed or ignored errors?"},
    "owner": {"type": "choice", "instructions": "Which team should review this change?",
              "criteria": {"backend": "business logic, APIs", "platform": "infrastructure, CI", "security": "auth, crypto, secrets"}},
    "risk": {"type": "score", "instructions": "Merge risk",
             "criteria": {"1": "none", "2": "low", "3": "medium", "4": "high"}}}}'

Response, one entry per question:

{"decisions": {"owner": {"choice": "backend", "confidence": 0.81, "distribution": {"backend": 0.81, "platform": 0.12, "security": 0.07}}}}

Validation: choice needs 2–26 criteria and score needs 2–10 levels. An invalid request returns HTTP 422.

Language: state.language is optional. Without it, only the language-agnostic rules are asked.