Instructions to use Sameer2407/fine-tuned-distilgpt2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Sameer2407/fine-tuned-distilgpt2 with Transformers:

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

pipe = pipeline("text-generation", model="Sameer2407/fine-tuned-distilgpt2")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Sameer2407/fine-tuned-distilgpt2")
model = AutoModelForCausalLM.from_pretrained("Sameer2407/fine-tuned-distilgpt2")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Sameer2407/fine-tuned-distilgpt2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Sameer2407/fine-tuned-distilgpt2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Sameer2407/fine-tuned-distilgpt2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Sameer2407/fine-tuned-distilgpt2

SGLang

How to use Sameer2407/fine-tuned-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 "Sameer2407/fine-tuned-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": "Sameer2407/fine-tuned-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 "Sameer2407/fine-tuned-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": "Sameer2407/fine-tuned-distilgpt2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Sameer2407/fine-tuned-distilgpt2 with Docker Model Runner:
```
docker model run hf.co/Sameer2407/fine-tuned-distilgpt2
```

Fine-Tuned DistilGPT2 on Restaurant Reviews

This is a fine-tuned version of DistilGPT2 using a small dataset of restaurant reviews. The model is trained to generate human-like review completions given a text prompt.

Model Details

Model Description

This model is a lightweight causal language model based on DistilGPT2, a distilled version of GPT2. It was fine-tuned on a small subset of restaurant reviews to help demonstrate how one can fine-tune and upload a model using Hugging Face and Google Colab with limited resources.

Developed by: Sameer Jadaun (Fine-tuning)
Shared by: [Sameer2407]
Model type: Causal Language Model (Decoder-only transformer)
Language(s): English
License: Apache 2.0 (inherited from DistilGPT2)
Finetuned from model: distilgpt2

Uses

Direct Use

You can use this model to generate restaurant reviews or autocomplete a review sentence given a starting prompt like:

"The food was"

Downstream Use

This model can be further fine-tuned on a larger corpus of restaurant, product, or service-related reviews to make it more robust and production-ready.

Out-of-Scope Use

Not suitable for factual QA tasks.
Should not be used to generate harmful, toxic, or biased content.

Bias, Risks, and Limitations

This model was trained on a tiny dataset of restaurant reviews and may reflect language biases or poor generation quality due to undertraining. It's only for educational/demo purposes.

Recommendations

Avoid using in production.
Fine-tune further with a more diverse and balanced dataset.

How to Get Started with the Model

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("your-username/fine-tuned-distilgpt2")
model = AutoModelForCausalLM.from_pretrained("your-username/fine-tuned-distilgpt2")

prompt = "The restaurant was"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50, do_sample=True)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloads last month: 3

Safetensors

Model size

81.9M params

Tensor type

F32