Instructions to use JYL480/Test_DistBERTModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use JYL480/Test_DistBERTModel with Transformers:

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

pipe = pipeline("text-generation", model="JYL480/Test_DistBERTModel")

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("JYL480/Test_DistBERTModel")
model = AutoModelForMultimodalLM.from_pretrained("JYL480/Test_DistBERTModel")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use JYL480/Test_DistBERTModel with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "JYL480/Test_DistBERTModel"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "JYL480/Test_DistBERTModel",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/JYL480/Test_DistBERTModel

SGLang

How to use JYL480/Test_DistBERTModel 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 "JYL480/Test_DistBERTModel" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "JYL480/Test_DistBERTModel",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "JYL480/Test_DistBERTModel" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "JYL480/Test_DistBERTModel",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use JYL480/Test_DistBERTModel with Docker Model Runner:
```
docker model run hf.co/JYL480/Test_DistBERTModel
```

Test_DistBERTModel / README.md

JYL480

End of training

5de3f68 verified almost 2 years ago

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	---
	license: apache-2.0
	base_model: distilbert/distilgpt2
	tags:
	- generated_from_trainer
	datasets:
	- eli5_category
	model-index:
	- name: Test_DistBERTModel
	results: []
	---

	<!-- This model card has been generated automatically according to the information the Trainer had access to. You
	should probably proofread and complete it, then remove this comment. -->

	# Test_DistBERTModel

	This model is a fine-tuned version of [distilbert/distilgpt2](https://huggingface.co/distilbert/distilgpt2) on the eli5_category dataset.
	It achieves the following results on the evaluation set:
	- Loss: 3.7692

	## Model description

	More information needed

	## Intended uses & limitations

	More information needed

	## Training and evaluation data

	More information needed

	## Training procedure

	### Training hyperparameters

	The following hyperparameters were used during training:
	- learning_rate: 2e-05
	- train_batch_size: 8
	- eval_batch_size: 8
	- seed: 42
	- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
	- lr_scheduler_type: linear
	- num_epochs: 5

	### Training results

	\| Training Loss \| Epoch \| Step \| Validation Loss \|
	\|:-------------:\|:-----:\|:-----:\|:---------------:\|
	\| 3.9179 \| 1.0 \| 2736 \| 3.7953 \|
	\| 3.8435 \| 2.0 \| 5472 \| 3.7801 \|
	\| 3.7908 \| 3.0 \| 8208 \| 3.7714 \|
	\| 3.753 \| 4.0 \| 10944 \| 3.7702 \|
	\| 3.7358 \| 5.0 \| 13680 \| 3.7692 \|


	### Framework versions

	- Transformers 4.41.2
	- Pytorch 2.3.0+cu121
	- Datasets 2.20.0
	- Tokenizers 0.19.1