Instructions to use eliwill/stoic-generator-distil-gpt2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use eliwill/stoic-generator-distil-gpt2 with Transformers:

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

pipe = pipeline("text-generation", model="eliwill/stoic-generator-distil-gpt2")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("eliwill/stoic-generator-distil-gpt2")
model = AutoModelForCausalLM.from_pretrained("eliwill/stoic-generator-distil-gpt2")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use eliwill/stoic-generator-distil-gpt2 with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/eliwill/stoic-generator-distil-gpt2

SGLang

How to use eliwill/stoic-generator-distil-gpt2 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 "eliwill/stoic-generator-distil-gpt2" \
    --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": "eliwill/stoic-generator-distil-gpt2",
		"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 "eliwill/stoic-generator-distil-gpt2" \
        --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": "eliwill/stoic-generator-distil-gpt2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use eliwill/stoic-generator-distil-gpt2 with Docker Model Runner:
```
docker model run hf.co/eliwill/stoic-generator-distil-gpt2
```

eliwill/stoic-generator-distil-gpt2

This model is a fine-tuned version of distilgpt2 on an unknown dataset. It achieves the following results on the evaluation set:

Train Loss: 3.3439
Validation Loss: 3.7738
Epoch: 19

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:

optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 2e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01}
training_precision: float32

Training results

Train Loss	Validation Loss	Epoch
4.2818	3.9629	0
4.0906	3.9052	1
3.9946	3.8684	2
3.9239	3.8412	3
3.8689	3.8316	4
3.8185	3.8156	5
3.7751	3.8032	6
3.7325	3.7950	7
3.6929	3.7901	8
3.6575	3.7770	9
3.6223	3.7658	10
3.5882	3.7745	11
3.5536	3.7642	12
3.5226	3.7645	13
3.4912	3.7616	14
3.4595	3.7598	15
3.4289	3.7604	16
3.3992	3.7555	17
3.3713	3.7646	18
3.3439	3.7738	19

Framework versions

Transformers 4.22.1
TensorFlow 2.8.2
Datasets 2.5.1
Tokenizers 0.12.1

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