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)

# 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=40)
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
```

extraction-r19-v1

游戏 UGC 评论「语义单元抽取」模型。基于 Qwen/Qwen3-4B，LoRA（r16 / alpha32 / dropout0.05）训练后合并进底座，fp16 权重，可直接用 transformers 加载，无 peft 依赖。

任务：从一条评论文本中抽取语义单元（mention / span / occurrence），只做抽取，不做分类
权重：fp16，4.02B 参数，约 7.5 GB
硬件：实测峰值显存 8.82 GB，T4/T10（16GB，Turing）可直接部署，attention 走 sdpa，无需 FlashAttention2

目录结构

.
├── model/                  # 合并后的完整模型（transformers 可直接加载）
│   ├── config.json
│   ├── model-0000{1,2}-of-00002.safetensors
│   ├── model.safetensors.index.json
│   ├── generation_config.json
│   ├── chat_template.jinja  # 提示词契约已固化在模板中
│   └── tokenizer 相关文件
├── sdk/extraction_sdk/     # 自包含 SDK（契约 + 抽取器 + 后处理校验）
├── example.py
├── manifest.json           # 指标 / 契约 / 硬件要求
├── SHA256SUMS.txt          # 权重文件校验值
└── RELEASE.md

快速开始

1. 用 SDK（推荐，含输出校验与契约）

pip install torch>=2.1 transformers>=4.53
git clone https://huggingface.co/<YOUR_NAMESPACE>/<YOUR_REPO> extraction-r19-v1
cd extraction-r19-v1
python example.py

import sys; sys.path.insert(0, "sdk")
from extraction_sdk import SemanticUnitExtractor

ext = SemanticUnitExtractor("model", dtype="fp16")   # T4/T10 必须 fp16；Ampere+ 可用 bf16
out = ext.extract("策划在偷偷加强小兵,我情绪稳定#王者荣耀",
                  record_id="r1", game_name="王者荣耀")
# out = {"id","text","ok","latency_ms","semantic_units":[{"mention","start","end","unit_id",...}],"diagnostics":{...}}

2. 直接用 transformers

from transformers import AutoModelForCausalLM, AutoTokenizer

tok = AutoTokenizer.from_pretrained("<YOUR_NAMESPACE>/<YOUR_REPO>", subfolder="model")
model = AutoModelForCausalLM.from_pretrained(
    "<YOUR_NAMESPACE>/<YOUR_REPO>", subfolder="model",
    dtype="fp16", attn_implementation="sdpa", device_map="auto",
)
# 提示词请走 tokenizer 内置的 chat template（提示词契约 reasoningner-lite-sft-v5-extraction-only）

生成参数已固化在 generation_config.json：**贪心解码（do_sample=False）+ max_new_tokens=2048**，请勿改动，否则与评测结果不一致。

输入 / 输出契约

契约	版本
提示词	`reasoningner-lite-sft-v5-extraction-only`
模型输出	`semantic-units-occurrence-v3-extraction-only`
正式预测	`semantic-units-span-v3-extraction-only`

ok=False 表示输出 JSON 解析失败或存在非法 span（mention 不在正文 / occurrence 越界），此时 semantic_units 为已通过校验的子集；线上建议对 ok=False 记录走兜底（重试或降级）。

评测（DEV 298 条，人工裁决金标）

口径	P	R	F1	Δ vs 旧生产模型 R1
严格 mention（门控主口径）	0.7376	0.7855	0.7608	+0.0174（95% CI [0.0013, 0.0336]）
occurrence 逐位置	0.7273	0.7752	0.7505	—
relaxed（后缀 + 有界包含）	0.7915	0.8429	0.8164	+0.0169

JSON 成功率 98.99%，非法 span 0
泛化性（剔除政策词表）Δ +0.0133
SDK 与评测输出平价验证：20/20 DEV 样本单元集合完全一致

部署要点

dtype 必须 fp16：Turing 架构无 bf16 支持；Ampere+（A10/A100/4090）可改用 bf16
显存峰值 8.82 GB，16GB 卡可并发 1~2 实例
时延：A100 mean 4.8s / p50 3.8s 每条；Turing 预计 8~15s，随单元数线性增长
输入 text 建议 ≤ 1024 token（训练 max_seq 993）
分类（semantic_type 等四维）不在本包内，属后置阶段

已知限制

2/298 条顽固失败模式：模型幻觉 mention 不在正文（走 ok=False 兜底）
基于 Qwen3-4B，继承其偏见与事实性风险，输出需结合业务审核
领域为游戏 UGC 中文评论，跨领域效果未验证

许可证

Apache-2.0，沿用底座模型 Qwen/Qwen3-4B 的许可证。

训练信息

底座：Qwen/Qwen3-4B
微调：LoRA r16 / alpha32 / dropout0.05，合并进底座
训练数据：2384 条 / 8863 单元（政策对齐 v3）
精度：float16

Downloads last month: 264

Safetensors

Model size

4B params

Tensor type

F16

Model tree for li-ovo/NER_Game

Base model

Qwen/Qwen3-4B-Base

Finetuned

Qwen/Qwen3-4B

Adapter

(1164)

this model