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
```

wangzihaogithub commited on Aug 13, 2025

Commit

8e8fbcd

verified ·

1 Parent(s): b218e20

Update README.md

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README.md +66 -56

README.md CHANGED Viewed

@@ -1,57 +1,67 @@
----
-library_name: transformers
-model_name: output-08-0-0805
-tags:
-- generated_from_trainer
-- trl
-- sft
-licence: license
----
-# Model Card for output-08-0-0805
-This model is a fine-tuned version of [None](https://huggingface.co/None).
-It has been trained using [TRL](https://github.com/huggingface/trl).
-## Quick start
-```python
-from transformers import pipeline
-question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
-generator = pipeline("text-generation", model="None", device="cuda")
-output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
-print(output["generated_text"])
-```
-## Training procedure
-This model was trained with SFT.
-### Framework versions
-- TRL: 0.19.0
-- Transformers: 4.53.0
-- Pytorch: 2.6.0
-- Datasets: 3.6.0
-- Tokenizers: 0.21.2
-## 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-classification
+---
+# Model Card for output-08-0-0805
+This model is a fine-tuned version of [None](https://huggingface.co/None).
+It has been trained using [TRL](https://github.com/huggingface/trl).
+## Quick start
+```python
+from transformers import pipeline
+question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
+generator = pipeline("text-generation", model="None", device="cuda")
+output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
+print(output["generated_text"])
+```
+## Training procedure
+This model was trained with SFT.
+### Framework versions
+- TRL: 0.19.0
+- Transformers: 4.53.0
+- Pytorch: 2.6.0
+- Datasets: 3.6.0
+- Tokenizers: 0.21.2
+## 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}}
+}
 ```