Instructions to use bestofbothworldsenjoyer/gpt2-expanded-test-distilled with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use bestofbothworldsenjoyer/gpt2-expanded-test-distilled with Transformers:

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

pipe = pipeline("text-generation", model="bestofbothworldsenjoyer/gpt2-expanded-test-distilled")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("bestofbothworldsenjoyer/gpt2-expanded-test-distilled")
model = AutoModelForCausalLM.from_pretrained("bestofbothworldsenjoyer/gpt2-expanded-test-distilled")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use bestofbothworldsenjoyer/gpt2-expanded-test-distilled with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "bestofbothworldsenjoyer/gpt2-expanded-test-distilled"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "bestofbothworldsenjoyer/gpt2-expanded-test-distilled",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/bestofbothworldsenjoyer/gpt2-expanded-test-distilled

SGLang

How to use bestofbothworldsenjoyer/gpt2-expanded-test-distilled 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 "bestofbothworldsenjoyer/gpt2-expanded-test-distilled" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "bestofbothworldsenjoyer/gpt2-expanded-test-distilled",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "bestofbothworldsenjoyer/gpt2-expanded-test-distilled" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "bestofbothworldsenjoyer/gpt2-expanded-test-distilled",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use bestofbothworldsenjoyer/gpt2-expanded-test-distilled with Docker Model Runner:
```
docker model run hf.co/bestofbothworldsenjoyer/gpt2-expanded-test-distilled
```

gpt2-expanded-test-distilled

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

Loss: 3.0336

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: 5e-05
train_batch_size: 8
eval_batch_size: 8
seed: 42
optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.1
num_epochs: 25.0

Training results

Training Loss	Epoch	Step	Validation Loss
3.4795	1.0	549	3.0471
3.0621	2.0	1098	2.9997
3.0029	3.0	1647	2.9767
2.9472	4.0	2196	2.9579
2.9013	5.0	2745	2.9493
2.8283	6.0	3294	2.9476
2.8005	7.0	3843	2.9485
2.7523	8.0	4392	2.9494
2.7257	9.0	4941	2.9516
2.6927	10.0	5490	2.9594
2.6327	11.0	6039	2.9631
2.6106	12.0	6588	2.9706
2.5743	13.0	7137	2.9760
2.5676	14.0	7686	2.9845
2.5504	15.0	8235	2.9869
2.5242	16.0	8784	2.9978
2.5097	17.0	9333	3.0007
2.4938	18.0	9882	3.0114
2.4908	19.0	10431	3.0178
2.4731	20.0	10980	3.0204
2.4503	21.0	11529	3.0228
2.4441	22.0	12078	3.0254
2.4378	23.0	12627	3.0287
2.4286	24.0	13176	3.0312
2.4216	25.0	13725	3.0336

Framework versions

Transformers 4.57.1
Pytorch 2.8.0+cu126
Datasets 4.0.0
Tokenizers 0.22.1

Downloads last month: 3

Safetensors

Model size

81.9M params

Tensor type

F32

Model tree for bestofbothworldsenjoyer/gpt2-expanded-test-distilled

Base model

distilbert/distilgpt2

Finetuned

(1487)

this model

bestofbothworldsenjoyer
/

gpt2-expanded-test-distilled

gpt2-expanded-test-distilled

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

Model tree for bestofbothworldsenjoyer/gpt2-expanded-test-distilled

Space using bestofbothworldsenjoyer/gpt2-expanded-test-distilled 1