Instructions to use dh-unibe/gpt2-larger-luther with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use dh-unibe/gpt2-larger-luther with Transformers:

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

pipe = pipeline("text-generation", model="dh-unibe/gpt2-larger-luther")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("dh-unibe/gpt2-larger-luther")
model = AutoModelForCausalLM.from_pretrained("dh-unibe/gpt2-larger-luther", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use dh-unibe/gpt2-larger-luther with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "dh-unibe/gpt2-larger-luther"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "dh-unibe/gpt2-larger-luther",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/dh-unibe/gpt2-larger-luther

SGLang

How to use dh-unibe/gpt2-larger-luther 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 "dh-unibe/gpt2-larger-luther" \
    --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": "dh-unibe/gpt2-larger-luther",
		"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 "dh-unibe/gpt2-larger-luther" \
        --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": "dh-unibe/gpt2-larger-luther",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use dh-unibe/gpt2-larger-luther with Docker Model Runner:
```
docker model run hf.co/dh-unibe/gpt2-larger-luther
```

22_12_13_luther_blocks_larger_fp16_20ep

This model is a fine-tuned version of stefan-it/german-gpt2-larger on a dataset of texts by Martin Luther. It achieves the following results on the evaluation set:

Loss: 3.5847
Accuracy: 0.3168

Model description

This is a language model used to generate wishes for a happy new year to the readers of "reformiert" a journal in Switzerland (https://www.reformiert.info)

Intended uses & limitations

This is to test the capabilities of the GPT-2 transformer architecture.

Training and evaluation data

Automatic split of an edited and "cleaned" version of parts of Luther's writing. Cleaning refers here to the process of eliminating para-texts like page numbering, footnotes, etc.

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 5e-05
train_batch_size: 8
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 2
total_train_batch_size: 16
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 20.0
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
No log	1.6	50	4.6218	0.2156
8.1175	3.22	100	4.0404	0.2633
8.1175	4.83	150	3.8120	0.2871
3.734	6.44	200	3.7062	0.2997
3.734	8.06	250	3.6382	0.3082
3.3639	9.67	300	3.6108	0.3128
3.3639	11.29	350	3.6012	0.3148
3.1363	12.89	400	3.5847	0.3168
3.1363	14.51	450	3.5914	0.3180
2.9884	16.13	500	3.5954	0.3177
2.9884	17.73	550	3.6001	0.3176
2.8748	19.35	600	3.6048	0.3188

Framework versions

Transformers 4.26.0.dev0
Pytorch 1.13.0
Datasets 2.7.1
Tokenizers 0.12.1

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Safetensors

Model size

0.1B params

Tensor type

F32