Instructions to use arjunpatel/distilgpt2-finetuned-pokemon-moves with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use arjunpatel/distilgpt2-finetuned-pokemon-moves with Transformers:

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

pipe = pipeline("text-generation", model="arjunpatel/distilgpt2-finetuned-pokemon-moves")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("arjunpatel/distilgpt2-finetuned-pokemon-moves")
model = AutoModelForCausalLM.from_pretrained("arjunpatel/distilgpt2-finetuned-pokemon-moves")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use arjunpatel/distilgpt2-finetuned-pokemon-moves with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "arjunpatel/distilgpt2-finetuned-pokemon-moves"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "arjunpatel/distilgpt2-finetuned-pokemon-moves",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/arjunpatel/distilgpt2-finetuned-pokemon-moves

SGLang

How to use arjunpatel/distilgpt2-finetuned-pokemon-moves 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 "arjunpatel/distilgpt2-finetuned-pokemon-moves" \
    --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": "arjunpatel/distilgpt2-finetuned-pokemon-moves",
		"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 "arjunpatel/distilgpt2-finetuned-pokemon-moves" \
        --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": "arjunpatel/distilgpt2-finetuned-pokemon-moves",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use arjunpatel/distilgpt2-finetuned-pokemon-moves with Docker Model Runner:
```
docker model run hf.co/arjunpatel/distilgpt2-finetuned-pokemon-moves
```

arjunpatel/distilgpt2-finetuned-pokemon-moves

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

Train Loss: 1.8709
Validation Loss: 2.3512
Epoch: 14

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
3.7146	3.2288	0
3.1159	2.8961	1
2.8592	2.7388	2
2.6684	2.6423	3
2.5358	2.5709	4
2.4330	2.5137	5
2.3308	2.4736	6
2.2499	2.4444	7
2.1843	2.4115	8
2.1322	2.3931	9
2.0683	2.3829	10
2.0122	2.3669	11
1.9676	2.3596	12
1.9087	2.3591	13
1.8709	2.3512	14

Framework versions

Transformers 4.18.0
TensorFlow 2.8.0
Datasets 2.1.0
Tokenizers 0.11.0

Downloads last month: 8

arjunpatel
/

distilgpt2-finetuned-pokemon-moves

arjunpatel/distilgpt2-finetuned-pokemon-moves

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

Space using arjunpatel/distilgpt2-finetuned-pokemon-moves 1