Instructions to use ningpy/redflag-detection-V4.2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ningpy/redflag-detection-V4.2 with Transformers:

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

pipe = pipeline("text-generation", model="ningpy/redflag-detection-V4.2")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ningpy/redflag-detection-V4.2")
model = AutoModelForCausalLM.from_pretrained("ningpy/redflag-detection-V4.2", 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=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use ningpy/redflag-detection-V4.2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ningpy/redflag-detection-V4.2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ningpy/redflag-detection-V4.2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ningpy/redflag-detection-V4.2

SGLang

How to use ningpy/redflag-detection-V4.2 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 "ningpy/redflag-detection-V4.2" \
    --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": "ningpy/redflag-detection-V4.2",
		"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 "ningpy/redflag-detection-V4.2" \
        --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": "ningpy/redflag-detection-V4.2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use ningpy/redflag-detection-V4.2 with Docker Model Runner:
```
docker model run hf.co/ningpy/redflag-detection-V4.2
```

Redflag Detection V4.2 (V20g, strict 59-rule spec compliance)

Fine-tuned from Qwen2.5-7B-Instruct for medical red-flag extraction. Strict compliance with 2026-08 Medical Advisory "Red Flad_New" spec (59 rules).

V4.2 vs V4.1

V4.1 (V20d) included 6 "extension" rules (Row 60-65) and 7 symptoms not in the official 59-rule spec (testicular_torsion, pyelonephritis, PID, angle-closure glaucoma, cauda equina, PPH). These were removed in V4.2 to comply with Medical Advisory scope.

Closed symptom set: 96 → 89 items (dropped 7 orphaned)
Rule engine: 65 → 59 rules
Prompt v11 (was v10)
Training data: sanitized (dropped 81 samples of orphan-only cases + 160 orphan symptom mentions from labels) + 31 negative reinforcement samples
Fix: is_male schema hallucination remains 0

Performance (val 269, strict)

	V4.0 (V20c)	V4.1 (V20d)	V4.2 (V20g)
Exact Match	89.2%	87.0%	89.6%
Precision	0.918	0.901	0.921
Recall	0.749	0.786	0.779
F1	0.825	0.839	0.844

Clinical-fair (SUSPECTED = TP for recall)

Precision: 0.771
Recall: 0.865
F1: 0.815

Notes

Orphan symptom residual: 7/269 model outputs mention removed symptoms. Engine ignores them (0 flag impact). Downstream systems can safely strip them or ignore.
V4.2 covers exactly what's in the official spec. Coverage gaps for testicular torsion, angle-closure glaucoma, cauda equina, PPH require Medical Advisory spec expansion (see git history commit b61a4e9/cd5b628 for the extension logic that was removed).

Usage

python3 -m vllm.entrypoints.openai.api_server \
    --model ningpy/redflag-detection-V4.2 \
    --served-model-name redflag \
    --dtype float16

Pair with the 59-rule V20 engine (redflag_scripts/v20/rule_engine_v20.py).

Training

3766 samples (V20f 3735 + patch20e_neg 31)
LoRA r=32 alpha=64, 7 target projections
3 epochs @ 2e-5, seq len 1900, 8×V100 DDP

Downloads last month: -

Safetensors

Model size

8B params

Tensor type

F16

Model tree for ningpy/redflag-detection-V4.2

Base model

Qwen/Qwen2.5-7B

Finetuned

Qwen/Qwen2.5-7B-Instruct

Finetuned

(3024)

this model