Instructions to use rinogrego/HF-CLM-distilgpt2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use rinogrego/HF-CLM-distilgpt2 with Transformers:

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

pipe = pipeline("text-generation", model="rinogrego/HF-CLM-distilgpt2")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("rinogrego/HF-CLM-distilgpt2")
model = AutoModelForCausalLM.from_pretrained("rinogrego/HF-CLM-distilgpt2")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use rinogrego/HF-CLM-distilgpt2 with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/rinogrego/HF-CLM-distilgpt2

SGLang

How to use rinogrego/HF-CLM-distilgpt2 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 "rinogrego/HF-CLM-distilgpt2" \
    --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": "rinogrego/HF-CLM-distilgpt2",
		"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 "rinogrego/HF-CLM-distilgpt2" \
        --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": "rinogrego/HF-CLM-distilgpt2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use rinogrego/HF-CLM-distilgpt2 with Docker Model Runner:
```
docker model run hf.co/rinogrego/HF-CLM-distilgpt2
```

HF-CLM-distilgpt2

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

Loss: 3.7957

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: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 3.0

Training results

Training Loss	Epoch	Step	Validation Loss
3.9633	1.0	1474	3.8061
3.8533	2.0	2948	3.7974
3.8169	3.0	4422	3.7957

Framework versions

Transformers 4.35.2
Pytorch 2.1.0+cu121
Datasets 2.16.1
Tokenizers 0.15.1

Downloads last month: 4

Safetensors

Model size

81.9M params

Tensor type

F32

Model tree for rinogrego/HF-CLM-distilgpt2

Base model

distilbert/distilgpt2

Finetuned

(1475)

this model