Instructions to use myownfd/rizzgpt-small with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use myownfd/rizzgpt-small with Transformers:

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

pipe = pipeline("text-generation", model="myownfd/rizzgpt-small")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("myownfd/rizzgpt-small")
model = AutoModelForCausalLM.from_pretrained("myownfd/rizzgpt-small", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use myownfd/rizzgpt-small with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/myownfd/rizzgpt-small

SGLang

How to use myownfd/rizzgpt-small 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 "myownfd/rizzgpt-small" \
    --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": "myownfd/rizzgpt-small",
		"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 "myownfd/rizzgpt-small" \
        --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": "myownfd/rizzgpt-small",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use myownfd/rizzgpt-small with Docker Model Runner:
```
docker model run hf.co/myownfd/rizzgpt-small
```

rizzgpt-small

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

Loss: 0.4312

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: 4
eval_batch_size: 4
seed: 42
optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
num_epochs: 2

Training results

Training Loss	Epoch	Step	Validation Loss
1.5889	0.2410	20	1.2717
0.8215	0.4819	40	0.7057
1.0067	0.7229	60	0.5727
0.6394	0.9639	80	0.4916
0.7029	1.2048	100	0.4680
0.3354	1.4458	120	0.4340
0.5738	1.6867	140	0.4305
0.5458	1.9277	160	0.4312

Framework versions

Transformers 4.50.3
Pytorch 2.6.0+cu124
Datasets 3.5.0
Tokenizers 0.21.1

Downloads last month: 9

Safetensors

Model size

81.9M params

Tensor type

F32

Model tree for myownfd/rizzgpt-small

Base model

distilbert/distilgpt2

Finetuned

(1,584)

this model