Instructions to use li-ovo/NER_Game with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use li-ovo/NER_Game with Transformers:

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

pipe = pipeline("text-generation", model="li-ovo/NER_Game")
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("li-ovo/NER_Game")
model = AutoModelForCausalLM.from_pretrained("li-ovo/NER_Game", 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 li-ovo/NER_Game with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "li-ovo/NER_Game"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "li-ovo/NER_Game",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/li-ovo/NER_Game

SGLang

How to use li-ovo/NER_Game 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 "li-ovo/NER_Game" \
    --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": "li-ovo/NER_Game",
		"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 "li-ovo/NER_Game" \
        --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": "li-ovo/NER_Game",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use li-ovo/NER_Game with Docker Model Runner:
```
docker model run hf.co/li-ovo/NER_Game
```

NER_Game / example.py

li-ovo

Update NER_Game to R27 merged multi-game BF16 release

5c86bf0 verified 4 days ago

Raw History Blame Contribute Delete

708 Bytes

	"""R27 多游戏抽取示例：下载仓库后执行 python example.py。"""
	import json
	import sys
	from pathlib import Path

	root = Path(__file__).resolve().parent
	sys.path.insert(0, str(root / "sdk"))
	from extraction_sdk import SemanticUnitExtractor

	extractor = SemanticUnitExtractor(str(root / "model"), dtype="bf16")
	records = [
	{"id": "demo-001", "text": "孙悟空打野", "game_name": "王者荣耀"},
	{"id": "demo-002", "text": "原神抽卡，原神更新后卡顿", "game_name": "原神"},
	{"id": "demo-003", "text": "和平精英组队排位", "game_name": "和平精英"},
	]
	for result in extractor.extract_batch(records):
	print(json.dumps(result, ensure_ascii=False, indent=2))