Instructions to use ningpy/intent-detection-V2.4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ningpy/intent-detection-V2.4 with Transformers:

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

pipe = pipeline("text-generation", model="ningpy/intent-detection-V2.4")
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/intent-detection-V2.4")
model = AutoModelForCausalLM.from_pretrained("ningpy/intent-detection-V2.4", 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/intent-detection-V2.4 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ningpy/intent-detection-V2.4"
# 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/intent-detection-V2.4",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ningpy/intent-detection-V2.4

SGLang

How to use ningpy/intent-detection-V2.4 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/intent-detection-V2.4" \
    --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/intent-detection-V2.4",
		"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/intent-detection-V2.4" \
        --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/intent-detection-V2.4",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use ningpy/intent-detection-V2.4 with Docker Model Runner:
```
docker model run hf.co/ningpy/intent-detection-V2.4
```

intent-detection-V2.4 / README.md

ningpy

Intent V12.10 release V2.4: Val EM 84.4%, Safety F1 93.1%, SOOS recovered from 0% to 53.2% (STEER category expansion + V12.5 SOOS restore, cures HOOS/SOOS regression from V12.9)

f9f42ea verified about 2 months ago

preview code

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3.19 kB

metadata

license: apache-2.0
base_model: Qwen/Qwen2.5-7B-Instruct
tags:
  - intent-classification
  - health
  - medical
  - safety
  - qwen2.5
  - LoRA-merged
language:
  - en
  - zh
  - ms
library_name: transformers
pipeline_tag: text-generation

Intent Detection V2.4 (V12.10)

Health-app user intent classifier + safety router fine-tuned from Qwen2.5-7B-Instruct. Routes user messages into 5 states: PASS, REDFLAG, BLOCK, STEER, or COEXIST (emergency + personal-dose).

Performance (val set: 2518 samples)

Metric	Score
Safety F1 (REDFLAG ∪ BLOCK)	92.3%
Multi-label EM	88.0%
Joint acc (intent + all types)	81.5%
Single-label acc (v11-compatible projection)	90.1%
Intent acc	93.5%
Coexist recall (REDFLAG + BLOCK)	76.2%
Parse err	0.16% (of which ~half are eval-side bugs — model output is valid)
REDFLAG reason_code acc	98.3%

Improvements vs V11 (single-label):

Safety F1: 90.9% → 92.3% (+1.4pp)
REDFLAG F1: 90.0% → 94.1% (+4.1pp)
New multi-label capability: 76.2% coexist recall (v11 was 0%)

Output Schema (V12.10 flat)

6 fixed keys, deterministic order:

{
  "intent": "HA|HCN|MT|PI|OTHER",
  "redflag": true|false,
  "redflag_code": "RF-1_GENERAL|RF-2_PEDIATRIC|RF-3_OBSTETRIC|RF-4_ELDERLY|RF-5_IMMUNOCOMP|\"\"",
  "block": "BLOCK_HARD|STEER_SOFT|PASS|NONE",
  "block_code": "OOS_H_*|OOS_S_*|PASS_*|\"\"",
  "entities": {
    "drugs": ["<verbatim from user text>"],
    "diseases": ["..."],
    "symptoms": ["..."]
  }
}

Entities are verbatim substrings of the user message (no translation/normalization).

5 Valid State Combinations

State	redflag	block	Use case
PASS	false	PASS	Normal health knowledge Q
REDFLAG only	true	NONE	Emergency without personal-dose Q
BLOCK only	false	BLOCK_HARD	Personal action / off-topic
STEER only	false	STEER_SOFT	Personal clinical judgment
COEXIST	true	BLOCK_HARD	Emergency + personal-dose

Training data

54,404 samples (English + Chinese + Bruneian Malay)
Base 27K + STEER×3 + coexist×8 + patch13×3 (targeted narrow rules) + patch16×5 (anti-drift + narrow)
57.1% samples have non-empty entities (GPT-4o back-filled)

Usage with vLLM

python -m vllm.entrypoints.openai.api_server \
    --model ningpy/intent-detection-V2.4 \
    --served-model-name intent \
    --dtype float16 \
    --port 8001

Client sample

See https://git.evyd.tech/peiyan.ning/intent-model-v12/tree/main/sample for a Python client with routing + response templates.

Recommended request settings:

temperature: 0.0
max_tokens: 200
stop: [] (rely on model's natural <|im_end|>)

Downstream routing

Model output	Action
`redflag=true, block="BLOCK_HARD"`	Emergency guidance + refuse dose advice
`redflag=true, block="NONE"`	Emergency guidance
`redflag=false, block="BLOCK_HARD"`	Refuse + refer to doctor
`redflag=false, block="STEER_SOFT"`	Soft guide to consultation
`redflag=false, block="PASS"`	Normal RAG/LLM answer