Instructions to use ningpy/redflag-detection-V2.0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ningpy/redflag-detection-V2.0 with Transformers:

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

pipe = pipeline("text-generation", model="ningpy/redflag-detection-V2.0")
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("ningpy/redflag-detection-V2.0")
model = AutoModelForCausalLM.from_pretrained("ningpy/redflag-detection-V2.0", 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=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use ningpy/redflag-detection-V2.0 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ningpy/redflag-detection-V2.0"
# 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-V2.0",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ningpy/redflag-detection-V2.0

SGLang

How to use ningpy/redflag-detection-V2.0 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-V2.0" \
    --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-V2.0",
		"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-V2.0" \
        --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-V2.0",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use ningpy/redflag-detection-V2.0 with Docker Model Runner:
```
docker model run hf.co/ningpy/redflag-detection-V2.0
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Redflag Detection V2.0

Medical red-flag symptom extraction model fine-tuned from Qwen2.5-7B-Instruct. Extracts structured medical information from user messages that then feeds a rule engine to determine emergency red-flag rules.

Performance (100-case OOD test set)

Exact Match: 90.0%
Micro F1: 0.950
Precision: 0.959
Recall: 0.940

Val Extraction (269 samples)

Exact Match: 88.5%
Micro F1: 0.870

Training data

8355 samples (V9 + patch14 x6 + patch15 x6, targeted RF-055/RF-049/RF-056)
Coverage: 70 RF rules (RF-001..RF-070)
Multi-lingual: English + Bahasa Melayu

Schema

Output is JSON with:

{
  "age": {"value": int, "unit": "years"|"months"|"weeks"|"days"},
  "patient": "self"|"third_party",
  "conditions": ["immunocompromised"|"diabetes"|"asthma"|"pregnant"|...],
  "symptoms": ["fever"|"chest_pain"|"breathlessness"|...],
  "denied_symptoms": [...],
  "modifiers": {"onset": "acute"|"chronic", "fever_celsius": float, ...},
  "context_flags": ["post_flight"|"post_surgery"|"post_trauma"|...]
}

Usage with vLLM

python3 -m vllm.entrypoints.openai.api_server \
    --model ningpy/redflag-detection-V2.0 \
    --served-model-name redflag \
    --dtype float16

Downloads last month: 19

Safetensors

Model size

8B params

Tensor type

F16

Model tree for ningpy/redflag-detection-V2.0

Base model

Qwen/Qwen2.5-7B

Finetuned

Qwen/Qwen2.5-7B-Instruct

Finetuned

(3237)

this model

Quantizations

1 model