Instructions to use DipanjanSanyal/wikipedia_sample_tiny_gpt2_base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use DipanjanSanyal/wikipedia_sample_tiny_gpt2_base with Transformers:

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

pipe = pipeline("text-generation", model="DipanjanSanyal/wikipedia_sample_tiny_gpt2_base")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("DipanjanSanyal/wikipedia_sample_tiny_gpt2_base")
model = AutoModelForCausalLM.from_pretrained("DipanjanSanyal/wikipedia_sample_tiny_gpt2_base", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use DipanjanSanyal/wikipedia_sample_tiny_gpt2_base with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "DipanjanSanyal/wikipedia_sample_tiny_gpt2_base"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "DipanjanSanyal/wikipedia_sample_tiny_gpt2_base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/DipanjanSanyal/wikipedia_sample_tiny_gpt2_base

SGLang

How to use DipanjanSanyal/wikipedia_sample_tiny_gpt2_base 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 "DipanjanSanyal/wikipedia_sample_tiny_gpt2_base" \
    --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": "DipanjanSanyal/wikipedia_sample_tiny_gpt2_base",
		"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 "DipanjanSanyal/wikipedia_sample_tiny_gpt2_base" \
        --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": "DipanjanSanyal/wikipedia_sample_tiny_gpt2_base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use DipanjanSanyal/wikipedia_sample_tiny_gpt2_base with Docker Model Runner:
```
docker model run hf.co/DipanjanSanyal/wikipedia_sample_tiny_gpt2_base
```

wikipedia_sample_tiny_gpt2_base

This model is a fine-tuned version of gpt2 on the None dataset. It achieves the following results on the evaluation set:

Loss: 5.7538

Model description

This model is a result of an educational attempt of pre-training.

Data: 30,000 sample wikipedia articles
Tokenier: bert-base-uncased
Context: chunks of exactly 64 tokens with an overlap of 16 tokens
Initialization: GPT2LMHead() i.e. GPT2 structure without trained weights, and much smaller size and smaller number of transformer layers
Training: trained for 3 epochs

There is a previous version of the model (which I pasued in between because of budget). Below are the details for that:

Data: Same
Tokenizer: Same
Context: chunks of exactly 64 tokens with an overlap of 48 tokens (so a much larger dataset)
Initialization: Same
Training: trained for 15 epochs

To call this version, please use ...from_pretrained('DipanjanSanyal/wikipedia_sample_tiny_gpt2_base', revision = 87d9aa1eb492a5c20db562f113f07b8f8522f5d2')

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: 3e-05
train_batch_size: 32
eval_batch_size: 32
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: 3
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss
6.0712	1.0	22538	6.0994
5.83	2.0	45076	5.8272
5.7545	3.0	67614	5.7538

Framework versions

Transformers 4.51.3
Pytorch 2.6.0+cu124
Datasets 3.6.0
Tokenizers 0.21.1

Downloads last month: 24

Safetensors

Model size

4.51M params

Tensor type

F32

Model tree for DipanjanSanyal/wikipedia_sample_tiny_gpt2_base

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