Instructions to use wangzihaogithub/job-educational-parser with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use wangzihaogithub/job-educational-parser with Transformers:

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

pipe = pipeline("text-generation", model="wangzihaogithub/job-educational-parser")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("wangzihaogithub/job-educational-parser", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use wangzihaogithub/job-educational-parser with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/wangzihaogithub/job-educational-parser

SGLang

How to use wangzihaogithub/job-educational-parser 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 "wangzihaogithub/job-educational-parser" \
    --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": "wangzihaogithub/job-educational-parser",
		"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 "wangzihaogithub/job-educational-parser" \
        --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": "wangzihaogithub/job-educational-parser",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use wangzihaogithub/job-educational-parser with Docker Model Runner:
```
docker model run hf.co/wangzihaogithub/job-educational-parser
```

job-educational-parser / README.md

wangzihaogithub

Update README.md

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---
library_name: transformers
model_name: output-08-0-0805
tags:
- generated_from_trainer
- trl
- sft
licence: license
license: apache-2.0
datasets:
- wangzihaogithub/job-educational-parser-dataset-08-0-0805
language:
- zh
metrics:
- accuracy
base_model:
- Qwen/Qwen3-0.6B-Base
pipeline_tag: text-generation
---
# Model Card for wangzihaogithub/job-educational-parser

This model is a fine-tuned version of [job-educational-parser](https://huggingface.co/job-educational-parser).
It has been trained using [TRL](https://github.com/huggingface/trl).

模型任务：输入岗位描述，输出岗位中要求的学历（如 "博士、硕士、本科"）,遵循从高到低

训练数据：[https://huggingface.co/datasets/wangzihaogithub/job-educational-parser-dataset-08-0-0805](https://huggingface.co/datasets/wangzihaogithub/job-educational-parser-dataset-08-0-0805). 

例如：
输入

```json
{
  "model": "wangzihaogithub/job-educational-parser",
  "messages": [
    {
      "role": "system",
      "content": "从岗位中提取学历"
    },
    {
      "role": "user",
      "content": "游戏美术实习-原画类（26届提供转正）游戏类型&风格：欧美卡通 休闲类游戏 工作职责： 1. 配合各项目组美术需求（包括不限于原画，UI和动画类需求）的落地和整合； 2. 具有比较扎实的手绘能力，能够独立完成运营活动所需的美术设计需求； 3. 通过对主流AI产品的学习，总结提示词使用技巧，通过具体案例验证方法的有效性，协助团队建立规范化的AI应用方法论和完善AI工作流程。 岗位要求： 1.面向游戏/动画/数媒/雕塑/美术/工业设计等设计相关专业； 2.本科及以上学历； 3.2026年应届毕业生； 4.有优秀的绘画基础，会用手绘板，熟练掌握PS等2D设计软件 加分项： 1.美术院校相关专业者优先； 2.爱玩游戏者优先； 3.有相关美术设计实习或者工作经验者优先考虑； 4.性格乐观爽朗，善于表达。"
    }
  ],
  "max_tokens": 32
}
```

输出（固定格式）：博士、硕士、本科

响应速度：100～200毫秒之间（显卡RTX3060，精度BF16）

准确率：98%， 评测集：[https://huggingface.co/datasets/wangzihaogithub/job-educational-parser-dataset-08-0-0805/viewer/annotated-test](https://huggingface.co/datasets/wangzihaogithub/job-educational-parser-dataset-08-0-0805/viewer/annotated-test). 



## Requirements

transformers>=4.51.0


### Framework versions

- TRL: 0.19.0
- Transformers: 4.53.0
- Pytorch: 2.6.0
- Datasets: 3.6.0
- Tokenizers: 0.21.2


## Quick start

```python
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
model_name = "wangzihaogithub/job-educational-parser"
# 加载分词器和模型
tokenizer = AutoTokenizer.from_pretrained(model_name,
                                          cache_dir="./cache")
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto",
    cache_dir="./cache"
)
print('model load success')
messages = [
    {"role": "system", "content": '从岗位中提取学历'},
    {"role": "user", "content": "游戏美术实习-原画类（26届提供转正）游戏类型&风格：欧美卡通 休闲类游戏 工作职责： 1. 配合各项目组美术需求（包括不限于原画，UI和动画类需求）的落地和整合； 2. 具有比较扎实的手绘能力，能够独立完成运营活动所需的美术设计需求； 3. 通过对主流AI产品的学习，总结提示词使用技巧，通过具体案例验证方法的有效性，协助团队建立规范化的AI应用方法论和完善AI工作流程。 岗位要求： 1.面向游戏/动画/数媒/雕塑/美术/工业设计等设计相关专业； 2.本科及以上学历； 3.2026年应届毕业生； 4.有优秀的绘画基础，会用手绘板，熟练掌握PS等2D设计软件 加分项： 1.美术院校相关专业者优先； 2.爱玩游戏者优先； 3.有相关美术设计实习或者工作经验者优先考虑； 4.性格乐观爽朗，善于表达。"}
]
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True,
    enable_thinking=False
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# 生成推理结果（关闭梯度计算）
with torch.inference_mode(), torch.amp.autocast('cuda'):
    generated_ids = model.generate(
        **model_inputs,
        max_new_tokens=32,# 输出结果为学历，不需要太多
        eos_token_id=tokenizer.eos_token_id,
        pad_token_id=tokenizer.pad_token_id,
        use_cache=True,
        do_sample=False,  # 是否采样（False 为 greedy decode）
        temperature=None, # 关闭
        top_p=None,# 关闭
        top_k=None# 关闭
    )
output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist()
# parsing thinking content
try:
    # rindex finding 151668 (</think>)
    index = len(output_ids) - output_ids[::-1].index(151668)
except ValueError:
    index = 0
content = tokenizer.decode(output_ids[index:], skip_special_tokens=True).strip("\n")
print(content)
```

## Training procedure

 
This model was trained with SFT.

## Citations



Cite TRL as:
    
```bibtex
@misc{vonwerra2022trl,
	title        = {{TRL: Transformer Reinforcement Learning}},
	author       = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
	year         = 2020,
	journal      = {GitHub repository},
	publisher    = {GitHub},
	howpublished = {\url{https://github.com/huggingface/trl}}
}
```

	---
	library_name: transformers
	model_name: output-08-0-0805
	tags:
	- generated_from_trainer
	- trl
	- sft
	licence: license
	license: apache-2.0
	datasets:
	- wangzihaogithub/job-educational-parser-dataset-08-0-0805
	language:
	- zh
	metrics:
	- accuracy
	base_model:
	- Qwen/Qwen3-0.6B-Base
	pipeline_tag: text-generation
	---

	# Model Card for wangzihaogithub/job-educational-parser

	This model is a fine-tuned version of [job-educational-parser](https://huggingface.co/job-educational-parser).
	It has been trained using [TRL](https://github.com/huggingface/trl).

	模型任务：输入岗位描述，输出岗位中要求的学历（如 "博士、硕士、本科"）,遵循从高到低

	训练数据：[https://huggingface.co/datasets/wangzihaogithub/job-educational-parser-dataset-08-0-0805](https://huggingface.co/datasets/wangzihaogithub/job-educational-parser-dataset-08-0-0805).

	例如：
	输入

	```json
	{
	"model": "wangzihaogithub/job-educational-parser",
	"messages": [
	{
	"role": "system",
	"content": "从岗位中提取学历"
	},
	{
	"role": "user",
	"content": "游戏美术实习-原画类（26届提供转正）游戏类型&风格：欧美卡通休闲类游戏工作职责： 1. 配合各项目组美术需求（包括不限于原画，UI和动画类需求）的落地和整合； 2. 具有比较扎实的手绘能力，能够独立完成运营活动所需的美术设计需求； 3. 通过对主流AI产品的学习，总结提示词使用技巧，通过具体案例验证方法的有效性，协助团队建立规范化的AI应用方法论和完善AI工作流程。岗位要求： 1.面向游戏/动画/数媒/雕塑/美术/工业设计等设计相关专业； 2.本科及以上学历； 3.2026年应届毕业生； 4.有优秀的绘画基础，会用手绘板，熟练掌握PS等2D设计软件加分项： 1.美术院校相关专业者优先； 2.爱玩游戏者优先； 3.有相关美术设计实习或者工作经验者优先考虑； 4.性格乐观爽朗，善于表达。"
	}
	],
	"max_tokens": 32
	}
	```

	输出（固定格式）：博士、硕士、本科

	响应速度：100～200毫秒之间（显卡RTX3060，精度BF16）

	准确率：98%，评测集：[https://huggingface.co/datasets/wangzihaogithub/job-educational-parser-dataset-08-0-0805/viewer/annotated-test](https://huggingface.co/datasets/wangzihaogithub/job-educational-parser-dataset-08-0-0805/viewer/annotated-test).



	## Requirements

	transformers>=4.51.0


	### Framework versions

	- TRL: 0.19.0
	- Transformers: 4.53.0
	- Pytorch: 2.6.0
	- Datasets: 3.6.0
	- Tokenizers: 0.21.2


	## Quick start

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

	model_name = "wangzihaogithub/job-educational-parser"

	# 加载分词器和模型
	tokenizer = AutoTokenizer.from_pretrained(model_name,
	cache_dir="./cache")
	model = AutoModelForCausalLM.from_pretrained(
	model_name,
	torch_dtype="auto",
	device_map="auto",
	cache_dir="./cache"
	)

	print('model load success')

	messages = [
	{"role": "system", "content": '从岗位中提取学历'},
	{"role": "user", "content": "游戏美术实习-原画类（26届提供转正）游戏类型&风格：欧美卡通休闲类游戏工作职责： 1. 配合各项目组美术需求（包括不限于原画，UI和动画类需求）的落地和整合； 2. 具有比较扎实的手绘能力，能够独立完成运营活动所需的美术设计需求； 3. 通过对主流AI产品的学习，总结提示词使用技巧，通过具体案例验证方法的有效性，协助团队建立规范化的AI应用方法论和完善AI工作流程。岗位要求： 1.面向游戏/动画/数媒/雕塑/美术/工业设计等设计相关专业； 2.本科及以上学历； 3.2026年应届毕业生； 4.有优秀的绘画基础，会用手绘板，熟练掌握PS等2D设计软件加分项： 1.美术院校相关专业者优先； 2.爱玩游戏者优先； 3.有相关美术设计实习或者工作经验者优先考虑； 4.性格乐观爽朗，善于表达。"}
	]

	text = tokenizer.apply_chat_template(
	messages,
	tokenize=False,
	add_generation_prompt=True,
	enable_thinking=False
	)
	model_inputs = tokenizer([text], return_tensors="pt").to(model.device)

	# 生成推理结果（关闭梯度计算）
	with torch.inference_mode(), torch.amp.autocast('cuda'):
	generated_ids = model.generate(
	**model_inputs,
	max_new_tokens=32,# 输出结果为学历，不需要太多
	eos_token_id=tokenizer.eos_token_id,
	pad_token_id=tokenizer.pad_token_id,
	use_cache=True,
	do_sample=False, # 是否采样（False 为 greedy decode）
	temperature=None, # 关闭
	top_p=None,# 关闭
	top_k=None# 关闭
	)
	output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist()

	# parsing thinking content
	try:
	# rindex finding 151668 (</think>)
	index = len(output_ids) - output_ids[::-1].index(151668)
	except ValueError:
	index = 0

	content = tokenizer.decode(output_ids[index:], skip_special_tokens=True).strip("\n")

	print(content)
	```

	## Training procedure


	This model was trained with SFT.

	## Citations



	Cite TRL as:

	```bibtex
	@misc{vonwerra2022trl,
	title = {{TRL: Transformer Reinforcement Learning}},
	author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
	year = 2020,
	journal = {GitHub repository},
	publisher = {GitHub},
	howpublished = {\url{https://github.com/huggingface/trl}}
	}
	```