Instructions to use Kiran2004/GPT2_MentalHealth_ChatBot with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Kiran2004/GPT2_MentalHealth_ChatBot with Transformers:

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

pipe = pipeline("text-generation", model="Kiran2004/GPT2_MentalHealth_ChatBot")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Kiran2004/GPT2_MentalHealth_ChatBot")
model = AutoModelForCausalLM.from_pretrained("Kiran2004/GPT2_MentalHealth_ChatBot", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Kiran2004/GPT2_MentalHealth_ChatBot with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Kiran2004/GPT2_MentalHealth_ChatBot"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Kiran2004/GPT2_MentalHealth_ChatBot",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Kiran2004/GPT2_MentalHealth_ChatBot

SGLang

How to use Kiran2004/GPT2_MentalHealth_ChatBot 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 "Kiran2004/GPT2_MentalHealth_ChatBot" \
    --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": "Kiran2004/GPT2_MentalHealth_ChatBot",
		"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 "Kiran2004/GPT2_MentalHealth_ChatBot" \
        --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": "Kiran2004/GPT2_MentalHealth_ChatBot",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Kiran2004/GPT2_MentalHealth_ChatBot with Docker Model Runner:
```
docker model run hf.co/Kiran2004/GPT2_MentalHealth_ChatBot
```

Model description

This model is a fine-tuned version of openai-community/gpt2 on an MentalHealthConversational dataset. It is designed to generate text based on provided depression related prompts and can be used for a variety of natural language generation tasks. It's been trained on question-answer pairs, including unanswerable questions, for the task of Depression related Conversations for 10 Epochs and obtained following loss:

Training Loss: 1.6727

Model Training

Training Dataset: MentalHealthConversational Dataset
Pretrained Model: GPT-2

Evaluation

The model's performance can be evaluated using various metrics such as F1 score, BLEU score, and ROUGE score.

F1 Score: 0.0908
BLEU Score: 2.910400064753437e-05
ROUGE Score: 0.1498

Example Usage

from transformers import pipeline, GPT2Tokenizer, GPT2LMHeadModel

# Load tokenizer and model
model_name = "Kiran2004/GPT2_MentalHealth_ChatBot"
tokenizer = GPT2Tokenizer.from_pretrained(model_name)
model = GPT2LMHeadModel.from_pretrained(model_name)

# Generate text
generator = pipeline("text-generation", model=model, tokenizer=tokenizer)
prompt = "Your prompt goes here"
output = generator(prompt, max_length=50, num_return_sequences=1)
print(output[0]["generated_text"])

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 0.0001
train_batch_size: 3
eval_batch_size: 8
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 10

Framework versions

Transformers 4.38.2
Pytorch 2.2.1+cu121
Tokenizers 0.15.2

Downloads last month: 19

Safetensors

Model size

0.1B params

Tensor type

F32

Model tree for Kiran2004/GPT2_MentalHealth_ChatBot

Base model

openai-community/gpt2

Finetuned

(2252)

this model

Kiran2004
/

GPT2_MentalHealth_ChatBot