Instructions to use koh43/distilgpt2-eli5-clm with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use koh43/distilgpt2-eli5-clm with Transformers:

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

pipe = pipeline("text-generation", model="koh43/distilgpt2-eli5-clm")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("koh43/distilgpt2-eli5-clm")
model = AutoModelForCausalLM.from_pretrained("koh43/distilgpt2-eli5-clm")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use koh43/distilgpt2-eli5-clm with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "koh43/distilgpt2-eli5-clm"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "koh43/distilgpt2-eli5-clm",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/koh43/distilgpt2-eli5-clm

SGLang

How to use koh43/distilgpt2-eli5-clm 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 "koh43/distilgpt2-eli5-clm" \
    --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": "koh43/distilgpt2-eli5-clm",
		"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 "koh43/distilgpt2-eli5-clm" \
        --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": "koh43/distilgpt2-eli5-clm",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use koh43/distilgpt2-eli5-clm with Docker Model Runner:
```
docker model run hf.co/koh43/distilgpt2-eli5-clm
```

distilgpt2-eli5-clm

This model is a fine-tuned version of distilbert/distilgpt2 on the eli5_category dataset. It achieves the following results on the evaluation set:

Loss: 3.8586

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: 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
num_epochs: 10

Training results

Training Loss	Epoch	Step	Validation Loss
3.9079	1.0	1314	3.8563
3.8119	2.0	2628	3.8461
3.7662	3.0	3942	3.8420
3.7062	4.0	5256	3.8437
3.6726	5.0	6570	3.8460
3.639	6.0	7884	3.8462
3.6229	7.0	9198	3.8533
3.5963	8.0	10512	3.8549
3.582	9.0	11826	3.8573
3.5798	10.0	13140	3.8586

Framework versions

Transformers 4.55.2
Pytorch 2.8.0+cu128
Datasets 3.6.0
Tokenizers 0.21.4

Downloads last month: 1

Safetensors

Model size

81.9M params

Tensor type

F32

Model tree for koh43/distilgpt2-eli5-clm

Base model

distilbert/distilgpt2

Finetuned

(1482)

this model

koh43
/

distilgpt2-eli5-clm

distilgpt2-eli5-clm

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

Model tree for koh43/distilgpt2-eli5-clm

Dataset used to train koh43/distilgpt2-eli5-clm