Instructions to use ningpy/redflag-detection-V4.1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ningpy/redflag-detection-V4.1 with Transformers:

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

pipe = pipeline("text-generation", model="ningpy/redflag-detection-V4.1")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ningpy/redflag-detection-V4.1")
model = AutoModelForCausalLM.from_pretrained("ningpy/redflag-detection-V4.1", 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-V4.1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ningpy/redflag-detection-V4.1"
# 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-V4.1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ningpy/redflag-detection-V4.1

SGLang

How to use ningpy/redflag-detection-V4.1 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-V4.1" \
    --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-V4.1",
		"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-V4.1" \
        --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-V4.1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use ningpy/redflag-detection-V4.1 with Docker Model Runner:
```
docker model run hf.co/ningpy/redflag-detection-V4.1
```

Redflag Detection V4.1 (V20d, 65 rules)

Fine-tuned from Qwen2.5-7B-Instruct for medical red-flag extraction. Aligned with 2026-08 "Red Flad_New" spec, extended with 6 additional critical rules missing from the original spec.

What's new in V4.1 vs V4.0

Fixed is_male hallucination (0/269 val cases; V4.0 had schema gap where sex was in prompt but 0 training samples used it)
6 new critical rules covering previously uncovered surgical/OB emergencies:
- V20-60 testicular_torsion (sex=male defensive gate)
- V20-61 pyelonephritis (painful_urination + fever + severe)
- V20-62 severe_pid (foul_discharge + fever + severe)
- V20-63 eye_emergency (angle-closure glaucoma / sudden vision loss; any age)
- V20-64 cauda_equina (new_incontinence + severe_weakness; any adult age)
- V20-65 postpartum_hemorrhage (sex=female defensive gate)
Defensive sex gates on Row 34-39 (pregnancy) — blocks conflict only, not required
Pediatric age fallback — Row 40-46 accept is_child/is_baby boolean when age_band absent
50 new training samples covering sex extraction from "Patient Info: Age X, Male/Female" blocks (EN/中文/BM)

Performance (val 269, strict)

	V4.0 (V20c)	V4.1 (V20d)
Exact Match	89.2%	87.0%
Precision	0.918	0.901
Recall	0.749	0.786
F1	0.825	0.839

Performance (clinical-fair, SUSPECTED = TP for recall)

Precision: 0.753
Recall: 0.853
F1: 0.800

Usage

python3 -m vllm.entrypoints.openai.api_server \
    --model ningpy/redflag-detection-V4.1 \
    --served-model-name redflag \
    --dtype float16

Requires V20e rule engine (65 compiled rules with sex_gate + age fallback).

Training

3816 samples (V20c 3766 + patch20d 50 sex-field samples)
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-V4.1

Base model

Qwen/Qwen2.5-7B

Finetuned

Qwen/Qwen2.5-7B-Instruct

Finetuned

(3032)

this model