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

Raw History Blame Contribute Delete

3.19 kB

	---
	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:

	```json
	{
	"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

	```bash
	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 \|