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

preview code

raw

history blame contribute delete

1.54 kB

metadata

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

Test_DistBERTModel

This model is a fine-tuned version of 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