Instructions to use nikokons/conversational-agent-el with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use nikokons/conversational-agent-el with Transformers:

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

pipe = pipeline("text-generation", model="nikokons/conversational-agent-el")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("nikokons/conversational-agent-el")
model = AutoModelForCausalLM.from_pretrained("nikokons/conversational-agent-el")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use nikokons/conversational-agent-el with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/nikokons/conversational-agent-el

SGLang

How to use nikokons/conversational-agent-el 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 "nikokons/conversational-agent-el" \
    --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": "nikokons/conversational-agent-el",
		"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 "nikokons/conversational-agent-el" \
        --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": "nikokons/conversational-agent-el",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use nikokons/conversational-agent-el with Docker Model Runner:
```
docker model run hf.co/nikokons/conversational-agent-el
```

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Check out the documentation for more information.

Dataset:

A variant of the Persona-Chat dataset was used, which contains 19319 short dialogues. MarianMT, a free and efficient Neural Machine Translation framework, was used to translate this dataset into Greek.

Fine-tuning for the task of dialogue:

Using the pre-trained "gpt2-greek" (https://huggingface.co/nikokons/gpt2-greek) model, we fine-tune it on this Greek version of translated Persona-Chat dataset for 3 epochs until there is no progress in validation loss. The model's input is customized to the Greek version of the PERSONA-CHAT dataset to perform the fine-tuning procedure. A batch size of 4 is used, and gradients are accumulated over 8 iterations, resulting in a total batch size of 32. The Adam optimization scheme is used, with a learning rate of 5.7e-5. The fine-tuning procedure is based on the https://github.com/huggingface/transfer-learning-conv-ai repository.

Interact with the Chatbot:

You can interact with the chatbot in Greek using the code in this repository: https://github.com/Nkonstan/chatbot

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