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
```

Wikipedia / config.json

ZeynepAltundal

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	{
	"_name_or_path": "ytu-ce-cosmos/turkish-gpt2-medium-350m-instruct-v0.1",
	"activation_function": "gelu_new",
	"architectures": [
	"GPT2LMHeadModel"
	],
	"attn_pdrop": 0.1,
	"bos_token_id": 0,
	"embd_pdrop": 0.1,
	"eos_token_id": 0,
	"initializer_range": 0.02,
	"layer_norm_epsilon": 1e-05,
	"model_type": "gpt2",
	"n_ctx": 1024,
	"n_embd": 1024,
	"n_head": 16,
	"n_inner": null,
	"n_layer": 24,
	"n_positions": 1024,
	"n_special": 0,
	"pad_token_id": 0,
	"predict_special_tokens": true,
	"reorder_and_upcast_attn": false,
	"resid_pdrop": 0.1,
	"scale_attn_by_inverse_layer_idx": false,
	"scale_attn_weights": true,
	"summary_activation": null,
	"summary_first_dropout": 0.1,
	"summary_proj_to_labels": true,
	"summary_type": "cls_index",
	"summary_use_proj": true,
	"task_specific_params": {
	"text-generation": {
	"bos_token_id": 0,
	"do_sample": true,
	"eos_token_id": 50257,
	"max_length": 256,
	"pad_token_id": 0,
	"repetition_penalty": 1.1,
	"temperature": 0.3,
	"top_p": 0.9
	}
	},
	"torch_dtype": "float32",
	"transformers_version": "4.48.0",
	"use_cache": true,
	"vocab_size": 50258
	}