Instructions to use mwoelki/my_patent_abstract_causual_language-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mwoelki/my_patent_abstract_causual_language-model with Transformers:

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

pipe = pipeline("text-generation", model="mwoelki/my_patent_abstract_causual_language-model")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("mwoelki/my_patent_abstract_causual_language-model")
model = AutoModelForCausalLM.from_pretrained("mwoelki/my_patent_abstract_causual_language-model")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use mwoelki/my_patent_abstract_causual_language-model with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/mwoelki/my_patent_abstract_causual_language-model

SGLang

How to use mwoelki/my_patent_abstract_causual_language-model 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 "mwoelki/my_patent_abstract_causual_language-model" \
    --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": "mwoelki/my_patent_abstract_causual_language-model",
		"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 "mwoelki/my_patent_abstract_causual_language-model" \
        --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": "mwoelki/my_patent_abstract_causual_language-model",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use mwoelki/my_patent_abstract_causual_language-model with Docker Model Runner:
```
docker model run hf.co/mwoelki/my_patent_abstract_causual_language-model
```

my_patent_abstract_causual_language-model

This model is a fine-tuned version of distilbert/distilgpt2 on the None dataset. It achieves the following results on the evaluation set:

Loss: 3.4992

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: 3.0

Training results

Training Loss	Epoch	Step	Validation Loss
No log	1.0	1	3.5980
No log	2.0	2	3.5339
No log	3.0	3	3.4992

Framework versions

Transformers 4.41.2
Pytorch 2.3.0+cu121
Datasets 2.20.0
Tokenizers 0.19.1

Downloads last month: 3

Safetensors

Model size

81.9M params

Tensor type

F32

Model tree for mwoelki/my_patent_abstract_causual_language-model

Base model

distilbert/distilgpt2

Finetuned

(1487)

this model