Instructions to use ningpy/redflag-detection-V4.0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ningpy/redflag-detection-V4.0 with Transformers:

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

pipe = pipeline("text-generation", model="ningpy/redflag-detection-V4.0")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ningpy/redflag-detection-V4.0")
model = AutoModelForCausalLM.from_pretrained("ningpy/redflag-detection-V4.0", 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-V4.0 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ningpy/redflag-detection-V4.0"
# 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-V4.0",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ningpy/redflag-detection-V4.0

SGLang

How to use ningpy/redflag-detection-V4.0 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-V4.0" \
    --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-V4.0",
		"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-V4.0" \
        --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-V4.0",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use ningpy/redflag-detection-V4.0 with Docker Model Runner:
```
docker model run hf.co/ningpy/redflag-detection-V4.0
```

Redflag Detection V4.0 (V20 Red Flad_New spec)

Medical red-flag symptom extraction model fine-tuned from Qwen2.5-7B-Instruct. Aligned with 2026-08 Medical Advisory "Red Flad_New" specification: 59 rules, strict IF+AND semantics.

Extraction Schema

96 atomic symptoms (V17 base 78 + 18 new: coughing_blood, collapse, pale_cold_clammy_skin, mottled_skin, very_drowsy, reduced_responsiveness, no_urine, sunken_eyes, dry_mouth, high_pitched_cry, blue_grey_color, difficulty_swallowing, abnormal_behavior, deep_rapid_breathing, palpitations, eye_injury, neck_swelling, severe_weakness)
New severity modifiers: has_severe_complication, is_life_threatening, is_recurring, severity_level
New context flags: smoke_inhalation, chemical_exposure, venomous_bite, electrical_injury, cold_exposure, drowning, serious_assault
Population gates: is_pregnant, is_postpartum, is_elderly, is_child, is_baby, is_immunocompromised, has_diabetes, has_asthma, age_band

Performance (val 269, strict)

Exact Match: 89.2%
Precision: 0.918
Recall: 0.749
F1: 0.825

Performance (val 269, clinical-fair, SUSPECTED counted for recall)

Precision: 0.787
Recall: 0.851
F1: 0.818

Usage with vLLM

python3 -m vllm.entrypoints.openai.api_server \
    --model ningpy/redflag-detection-V4.0 \
    --served-model-name redflag \
    --dtype float16

Requires the V20 rule engine (data-driven, 59 compiled rules). Extraction output JSON is fed to engine.evaluate_rules() to produce matched_flags / suspected_flags.

Training

3766 samples (V17 base 3505 + patch20 68 + patch20c 193)
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-V4.0

Base model

Qwen/Qwen2.5-7B

Finetuned

Qwen/Qwen2.5-7B-Instruct

Finetuned

(3031)

this model