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

Libraries

How to use ZeynepAltundal/Wikipedia with Transformers:

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

pipe = pipeline("text-generation", model="ZeynepAltundal/Wikipedia")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

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

tokenizer = AutoTokenizer.from_pretrained("ZeynepAltundal/Wikipedia")
model = AutoModelForCausalLM.from_pretrained("ZeynepAltundal/Wikipedia", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use ZeynepAltundal/Wikipedia with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ZeynepAltundal/Wikipedia"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ZeynepAltundal/Wikipedia",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ZeynepAltundal/Wikipedia

SGLang

How to use ZeynepAltundal/Wikipedia 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 "ZeynepAltundal/Wikipedia" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ZeynepAltundal/Wikipedia",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "ZeynepAltundal/Wikipedia" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ZeynepAltundal/Wikipedia",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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

Model Overview:

This model is a fine-tuned version of the "ytu-ce-cosmos/turkish-gpt2-medium-350m-instruct-v0.1", designed specifically for Turkish Question-Answering (Q&A). The fine-tuning process utilized a custom dataset generated from Turkish Wikipedia articles, focusing on factual knowledge.

Base Model: ytu-ce-cosmos/turkish-gpt2-medium-350m-instruct-v0.1 Fine-Tuned Dataset: Custom Turkish Q&A dataset Evaluation Loss: 2.1461 (on the validation dataset)

Quick Start

from transformers import AutoTokenizer, AutoModelForCausalLM


model_name = "./fine_tuned_model"  # Replace with your Hugging Face model path if uploaded
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)


question = "Kamu sosyolojisi nedir?"


input_ids = tokenizer(question, return_tensors="pt").input_ids


output = model.generate(
    input_ids=input_ids,
    max_length=50,
    num_return_sequences=1,
    temperature=0.7
)

response = tokenizer.decode(output[0], skip_special_tokens=True)
print(f"Question: {question}")
print(f"Answer: {response}")

Training Details:

Dataset Source: Custom dataset generated from Turkish Wikipedia Number of Training Examples: 2,606 Training Dataset Size: 2,084 (80%) Validation Dataset Size: 522 (20%) Number of Epochs: 3 Batch Size: 8 Learning Rate: 5e-5 Evaluation Loss: 2.1461

Downloads last month: 18

Safetensors

Model size

0.4B params

Tensor type

F32

Model tree for ZeynepAltundal/Wikipedia

Base model

ytu-ce-cosmos/turkish-gpt2-medium

Finetuned

ytu-ce-cosmos/turkish-gpt2-medium-350m-instruct-v0.1

Finetuned

(1)

this model

ZeynepAltundal
/

Wikipedia

Model Overview:

Quick Start

Training Details:

Model tree for ZeynepAltundal/Wikipedia

Dataset used to train ZeynepAltundal/Wikipedia