Instructions to use ningpy/redflag-modifier-3b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ningpy/redflag-modifier-3b with Transformers:

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

pipe = pipeline("text-generation", model="ningpy/redflag-modifier-3b")
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-modifier-3b")
model = AutoModelForCausalLM.from_pretrained("ningpy/redflag-modifier-3b", 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-modifier-3b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ningpy/redflag-modifier-3b"
# 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-modifier-3b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ningpy/redflag-modifier-3b

SGLang

How to use ningpy/redflag-modifier-3b 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-modifier-3b" \
    --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-modifier-3b",
		"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-modifier-3b" \
        --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-modifier-3b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use ningpy/redflag-modifier-3b with Docker Model Runner:
```
docker model run hf.co/ningpy/redflag-modifier-3b
```

redflag-modifier-3b / README.md

ningpy

Initial upload: red flag modifier module (Qwen2.5-3B + LoRA merged)

4ad65d6 verified about 1 month ago

preview code

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	---
	license: apache-2.0
	base_model: Qwen/Qwen2.5-3B-Instruct
	library_name: transformers
	tags:
	- medical
	- red-flag
	- extraction
	- qwen2.5
	- multi-lingual
	- brunei-english
	language:
	- en
	- zh
	- ms
	---

	# Red Flag Detection: `modifier` module (Qwen2.5-3B-Instruct + LoRA merged)

	Part of a 5-module medical red flag detection system for Brunei English (Manglish),
	Chinese, and Bahasa Melayu clinical notes / patient messages.

	This model is the `modifier` extraction module — one of 5 specialized modules used
	together with a Python rule engine (V20 spec, 59 rules).

	## Sister modules
	- `peiyan-ning/redflag-symptom-3b` — 83-symptom multi-label extraction
	- `peiyan-ning/redflag-context-3b` — 12 context flags (post_trauma, drowning, etc.)
	- `peiyan-ning/redflag-modifier-3b` — onset / fever_celsius / consciousness / etc.
	- `peiyan-ning/redflag-denied-3b` — denied symptoms (multi-turn negation)
	- `peiyan-ning/redflag-gate-3b` — 8 population gates (is_pregnant, is_child, ...)

	## Performance (2246-case independent test set)

	Full 5-module pipeline + rule engine V46:

	\| Metric \| P \| R \| F1 \| Acc \|
	\|---\|---\|---\|---\|---\|
	\| PRIMARY (any_matched × labeled_matched) \| 0.902 \| 0.911 \| 0.906 \| 91.9% \|
	\| STRICT matched-only \| 0.893 \| 0.828 \| 0.859 \| 91.8% \|
	\| STRICT m+s \| 0.844 \| 0.905 \| 0.873 \| 92.1% \|

	## System prompt used at inference

	```
	Extract quantitative modifiers from text.

	===== EXTRACT onset =====
	Set onset:"acute" when text describes SUDDEN event:
	- "suddenly", "just", "out of nowhere", "in an instant", "突然", "tiba-tiba"
	- Trauma events: "fell", "crash", "hit"
	- Symptom onset described as fast: "just started"

	Set onset:"chronic" when described as long-standing/gradual.

	===== EXTRACT temperature =====
	When text has actual number: "39.5°C" / "38 degrees" / "40度" → fever_celsius

	===== EXTRACT other modifiers =====
	- seizure duration in minutes → seizure_duration_min
	- inhaler used / worked → inhaler_used / inhaler_effective
	- bleeding amount described "heavy" / "uncontrolled" → bleeding_severity
	- burn size → burn_severity
	- burn location → burn_location
	- consciousness: alert / confused / drowsy / unresponsive

	===== EXAMPLES =====
	"Suddenly severe chest pain" → {"modifiers": {"onset": "acute"}}
	"Fell down and hit head" → {"modifiers": {"onset": "acute"}}
	"Fever 39.5 for 3 days" → {"modifiers": {"onset": "chronic", "fever_celsius": 39.5, "fever_days": 3}}
	"Seizure lasted 7 minutes" → {"modifiers": {"seizure_duration_min": 7}}
	"Uncontrolled bleeding" → {"modifiers": {"bleeding_severity": "uncontrolled"}}
	"Not sure about symptoms" → {"modifiers": {}}

	Output: {"modifiers": {...}}

	===== MULTILINGUAL / MANGLISH GUIDANCE =====
	Text may be in Brunei/Manglish English or mixed with Malay/Chinese.
	Ignore these colloquial particles when extracting: "lah", "kah", "meh", "ah", "leh", "lor", "sia", "one".

	Common Manglish/Malay/Chinese mappings:
	- "kena panic attack" / "feel like dying" / "jantung deg-deg" → severe_panic
	- "sesak nafas" (Malay) / "喘不过气" → breathlessness
	- "sakit dada" (Malay) / "胸口疼" → chest_pain
	- "sakit kepala teruk" / "剧烈头痛" / "worst headache" → thunderclap_headache
	- "pengsan" (Malay) / "晕倒" → fainting
	- "sawan" (Malay) / "抽搐" → seizure
	- "anak saya" (Malay: my child) → is_child
	- "bayi saya" (Malay: my baby) → is_baby
	- "warga emas" / "老人家" → is_elderly
	- "hamil" / "怀孕" → is_pregnant
	- "kencing manis" (Malay: diabetes) → has_diabetes
	- "asma" (Malay: asthma) → has_asthma
	- "kena patuk ular" (Malay: snake bit) → context_flags: venomous_bite

	Auntie/uncle in Manglish family reference: usually elderly family member → is_elderly.

	```

	## Usage

	```python
	from transformers import AutoTokenizer, AutoModelForCausalLM
	import torch, json

	tok = AutoTokenizer.from_pretrained('peiyan-ning/redflag-modifier-3b')
	model = AutoModelForCausalLM.from_pretrained(
	'peiyan-ning/redflag-modifier-3b',
	torch_dtype=torch.float16,
	device_map='auto'
	)

	SYSTEM_PROMPT = tok.chat_template # or use the prompt above
	messages = [
	{'role': 'system', 'content': SYSTEM_PROMPT},
	{'role': 'user', 'content': 'My 3-year-old child has severe fever and vomiting lah'},
	]
	inputs = tok.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_tensors='pt').to(model.device)
	out = model.generate(inputs, max_new_tokens=200, do_sample=False)
	text = tok.decode(out[0][inputs.shape[1]:], skip_special_tokens=True)
	result = json.loads(text)
	print(result)
	```

	## Full pipeline

	See [git.evyd.tech/ai/redflag-detection-2.0](https://git.evyd.tech/ai/redflag-detection-2.0) for:
	- Rule engine (59 V20 rules)
	- Post-processing (gate_detector, severity_extractor, numeric_extractor)
	- End-to-end sample inference code

	## Training

	- Base: Qwen/Qwen2.5-3B-Instruct
	- LoRA: r=32, α=64, dropout=0.05
	- Target modules: q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj
	- 2 epochs, LR 2e-4, cosine, warmup 5%, effective batch 32
	- Multi-lingual: EN/ZH/MS with Manglish particles (lah/kah/meh)