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 /BENCHMARKS.md

riposta

1.1.0 docs: architecture section, Jev 1.14.0 comparison, MTP wording

6e5b373 verified 20 days ago

preview code

Raw History Blame Contribute Delete

9.94 kB

Benchmarks (CrossbowReviewer 1.1)

Version 1.1 replaces the 1.0 benchmark with a stricter protocol. All numbers are also in benchmarks.json. The 1.0 numbers (macro-averaged per rule) are not comparable with the pooled numbers below; the 1.0 card is available under the v1.0 tag of this repository.

What changed in how we test

Pooled metrics. Real-code labels are sparse (6.9% violations, 55 files from 9 repositories). A per-rule macro MCC over a handful of positives is noisy, so we pool all yes/no decisions of a test set and report MCC, AUC, average precision (AP), recall, specificity, balanced accuracy and ECE. positive = "the rule is violated".
Cluster bootstrap. Confidence intervals resample whole samples (or repositories), not single decisions. This keeps correlated questions of one file together.
Paired differences. 1.1 vs 1.0 is tested on the same decisions with a shared resample, so a gain is only reported when the interval excludes zero.
A held-out OWASP set. The 435 OWASP cases were used for model selection and calibration while we built 1.1. We drew 429 new OWASP Benchmark cases (seed 1, never used for training, selection or calibration) as an independent confirmation.
Vulnerability/fix pairs. 98 pairs from GitHub Security Advisories (vulnerable file and its fix). The model must flag the vulnerable version and accept the fixed one. This is the hardest test and the one the 1.0 model failed.
Same files, same protocol for every system. Predictions are cached and keyed by input; every system answers exactly the questions each sample is labeled for.

Results

Pooled MCC with 95% cluster-bootstrap CIs:

Synthetic (2 838 decisions, 691 violations)

System	MCC (95% CI)	AUC	AP	Recall	Specificity	bal_acc	ECE
CrossbowReviewer 1.1	0.72 (0.69–0.75)	0.955	0.892	0.69	0.97	0.83	0.010
CrossbowReviewer 1.0	0.72 (0.68–0.75)	0.959	0.905	0.65	0.98	0.82	0.009
Qwen3.5-9B-Base (zero-shot)	0.26 (0.21–0.30)	0.653	0.463	0.36	0.87	0.62	0.040
Jev 1.14.0 (latest)	0.58 (0.55–0.62)	0.891	0.706	0.81	0.83	0.82	0.054
Jev 1.13.0	0.57 (0.54–0.61)	0.883	0.684	0.81	0.82	0.81	0.046
basal-omni-1.0-max (zero-shot)	0.60 (0.56–0.64)	0.896	0.771	0.71	0.89	0.80	0.076
Claude Opus 5.5	0.85 (0.82–0.87)	0.985	0.960	0.86	0.97	0.92	0.120
GPT-6 Sol	0.80 (0.78–0.83)	0.976	0.930	0.95	0.91	0.93	0.021

Real open-source code (5 361 decisions, 370 violations; judge labels from frontier LLMs, so Opus/GPT scores are likely inflated)

System	MCC (95% CI)	AUC	AP	Recall	Specificity	bal_acc	ECE
CrossbowReviewer 1.1	0.51 (0.46–0.54)	0.961	0.705	0.36	0.99	0.67	0.014
CrossbowReviewer 1.0	0.47 (0.42–0.53)	0.938	0.576	0.35	0.99	0.67	0.006
Qwen3.5-9B-Base (zero-shot)	-0.13 (-0.16–-0.10)	0.309	0.046	0.04	0.75	0.39	0.029
Jev 1.14.0 (latest)	0.25 (0.21–0.28)	0.801	0.178	0.68	0.75	0.72	0.031
Jev 1.13.0	0.25 (0.21–0.28)	0.799	0.174	0.67	0.76	0.72	0.030
basal-omni-1.0-max (zero-shot)	0.26 (0.22–0.31)	0.802	0.224	0.54	0.85	0.70	0.066
Claude Opus 5.5	0.67 (0.63–0.72)	0.990	0.905	0.49	1.00	0.74	0.086
GPT-6 Sol	0.84 (0.81–0.87)	0.981	0.886	0.83	0.99	0.91	0.037

OWASP Benchmark, 435 cases (used for model selection in 1.1)

System	MCC (95% CI)	AUC	AP	Vulnerable flagged	Safe recognized	bal_acc	ECE
CrossbowReviewer 1.1	0.30 (0.22–0.40)	0.697	0.715	0.74	0.55	0.65	0.168
CrossbowReviewer 1.0	0.28 (0.20–0.36)	0.715	0.722	0.94	0.26	0.60	0.239
Qwen3.5-9B-Base (zero-shot)	0.37 (0.29–0.45)	0.727	0.713	0.93	0.37	0.65	0.025
Jev 1.14.0 (latest)	0.33 (0.25–0.40)	0.725	0.687	0.95	0.30	0.62	0.178
Jev 1.13.0	0.30 (0.22–0.37)	0.719	0.649	0.97	0.24	0.60	0.209
basal-omni-1.0-max (zero-shot)	0.50 (0.45–0.56)	0.825	0.800	1.00	0.40	0.70	0.257
Claude Opus 5.5	0.94 (0.90–0.97)	0.983	0.977	1.00	0.94	0.97	0.059
GPT-6 Sol	0.77 (0.72–0.82)	0.935	0.905	1.00	0.75	0.88	0.106

OWASP Benchmark, 429 held-out cases (independent confirmation)

System	MCC (95% CI)	AUC	AP	Vulnerable flagged	Safe recognized	bal_acc	ECE
CrossbowReviewer 1.1	0.34 (0.23–0.42)	0.722	0.700	0.88	0.42	0.65	0.150
CrossbowReviewer 1.0	0.34 (0.25–0.41)	0.738	0.750	0.94	0.32	0.63	0.238
Qwen3.5-9B-Base (zero-shot)	0.39 (0.31–0.45)	0.744	0.733	0.93	0.41	0.67	0.027
Jev 1.14.0 (latest)	0.34 (0.25–0.41)	0.763	0.719	0.94	0.32	0.63	0.189

Vulnerability/fix pairs (98 pairs = 196 decisions)

System	MCC (95% CI)	AUC	Vulnerable flagged	Fixed accepted	Pair accuracy	ECE
CrossbowReviewer 1.1	0.24 (0.14–0.34)	0.632	0.39	0.83	0.235	0.197
CrossbowReviewer 1.0	0.08 (-0.03–0.19)	0.506	0.05	0.98	0.041	0.353
Qwen3.5-9B-Base (zero-shot)	0.05 (0.00–0.14)	0.529	0.05	0.97	–	0.247
Jev 1.14.0 (latest)	0.10 (0.03–0.17)	0.546	0.50	0.60	0.112	0.220
basal-omni-1.0-max (zero-shot)	0.14 (0.05–0.21)	0.574	0.47	0.66	0.163	0.319

Pair accuracy = both versions of a pair judged correctly.

1.1 vs 1.0: paired differences

Test	ΔMCC (95% CI)	ΔAUC (95% CI)	Δbal_acc (95% CI)	Verdict
Synthetic	+0.005 (−0.020, +0.029)	−0.004 (−0.009, +0.001)	+0.012 (−0.002, +0.026)	no change
Real code	+0.036 (−0.023, +0.099)	+0.023 (+0.014, +0.032)	+0.003 (−0.012, +0.020)	better ranking
OWASP (435)	+0.021 (−0.079, +0.123)	−0.018 (−0.054, +0.023)	+0.045 (0.000, +0.091)	borderline
OWASP held-out	−0.001 (−0.083, +0.069)	−0.016 (−0.063, +0.024)	+0.018 (−0.017, +0.052)	no change
Vuln/fix pairs	+0.156 (+0.036, +0.301)	+0.126 (+0.074, +0.194)	+0.092 (+0.046, +0.148)	clearly better

What the benchmarks say

Confirmed gains: vulnerability/fix pairs (pair accuracy 0.04 → 0.235), ranking of real violations (AUC 0.938 → 0.961, AP 0.58 → 0.71) and calibration on security data (ECE 0.24 → 0.15–0.17).
No gain: synthetic data and OWASP. On the held-out OWASP set 1.1 equals 1.0 (MCC 0.34 both). The small OWASP improvement seen on the 435 selection set did not replicate, so we do not claim it.
The zero-shot base model is as good as both fine-tunes on OWASP (MCC 0.39 held-out, within the CIs). Fine-tuning does not teach single-pass data-flow reasoning. basal-omni zero-shot is clearly better on OWASP (MCC 0.50 on the 435 set; not run on the held-out set), and the frontier reasoning models are far ahead (Opus 5.5: 0.94).
Security stays a human/reasoning-model decision. 1.1 recognizes 42–55% of the safe OWASP traps (1.0: 26–32%), better but still far from Opus 5.5 (94%).

Against the latest Jev

We re-ran everything with jev-1.14.0 (the current jev-latest); 1.0's card used jev-1.13.0, which is kept in the tables for reference. 1.14.0 is only marginally better than 1.13.0 on every set. CrossbowReviewer 1.1 stays well ahead of it on real code (MCC 0.51 vs 0.25, AUC 0.961 vs 0.801, AP 0.71 vs 0.18) and on vulnerability/fix pairs (pair accuracy 0.235 vs 0.112) and on synthetic data (MCC 0.72 vs 0.58). Jev flags more violations (recall 0.81 vs 0.69 on synthetic, 0.68 vs 0.36 on real code) but pays with 25% false positives on real code versus 1%. On OWASP the two are statistically indistinguishable (held-out MCC 0.34 vs 0.34).

Other benchmarks considered

We surveyed public benchmarks that could extend this evaluation. None is included in the numbers above.

Benchmark	Why it is useful	Status
PrimeVul paired test (C/C++)	de-duplicated vulnerable/fixed pairs; paired metrics P-C, P-V, P-B, P-R	planned; out of language distribution
OWASP BenchmarkJava, remaining ~1 900 cases	larger Java security set	partly used (435 + 429 held-out)
Juliet Java 1.3 good/bad pairs	CWE coverage	templated code, sanity check only
CodeReviewer-style comment generation	not applicable: our model returns typed decisions, not comments	not used
CVEfixes, MegaVul, DiverseVul, Big-Vul	large	not used: label noise and training-data contamination risk

Latency and size

System	Seconds per file	Hardware
CrossbowReviewer (transformers, BF16)	~0.2	1× A100 40 GB
CrossbowReviewer (llama.cpp, Q8_0)	~3	Apple Silicon (Metal)
Claude Opus 5.5	8.2	API
GPT-6 Sol	9.8	API

Reproducibility

Weights: the merged BF16 safetensors match base + LoRA recorded on a GPU (50 samples, 1030 decisions): 1026/1030 identical choices (mean |Δp| 0.002, max 0.04). The Q8_0 GGUF matches the 1.1 predictions on 40/40 decisions (mean |Δp| 0.008).
Test data is not redistributed: OWASP Benchmark is GPL-2.0 and the source files belong to their authors. The evaluation code (evalkit.py, release_bench.py, owasp.py --exclude) is in the pipeline repository.
Limits of the evidence: real code is 55 files from 9 repositories (7 clusters for per-repo bootstrap), 98 pairs, one seed per model. Differences below ~0.05 MCC are not distinguishable.