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

Libraries

How to use vpadaraju/chatbot with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("vpadaraju/chatbot")
model = AutoModelForCausalLM.from_pretrained("vpadaraju/chatbot", 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=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use vpadaraju/chatbot with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "vpadaraju/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": "vpadaraju/chatbot",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/vpadaraju/chatbot

SGLang

How to use vpadaraju/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 "vpadaraju/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": "vpadaraju/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 "vpadaraju/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": "vpadaraju/chatbot",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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

chatbot

This model is a fine-tuned version of microsoft/DialoGPT-small on the empathetic_dialogues dataset. It achieves the following results on the evaluation set:

Loss: 0.8866

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 2e-05
train_batch_size: 1
eval_batch_size: 1
seed: 42
optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
training_steps: 50

Training results

Training Loss	Epoch	Step	Validation Loss
No log	0.1	10	1.3309
No log	0.2	20	0.9199
No log	0.3	30	0.8962
No log	0.4	40	0.8888
No log	0.5	50	0.8866

Framework versions

Transformers 4.48.1
Pytorch 2.6.0
Datasets 3.2.0
Tokenizers 0.21.0

Downloads last month: 6

Safetensors

Model size

0.1B params

Tensor type

F32

Model tree for vpadaraju/chatbot

Base model

microsoft/DialoGPT-small

Finetuned

(54)

this model

vpadaraju
/

chatbot

chatbot

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

Model tree for vpadaraju/chatbot

Dataset used to train vpadaraju/chatbot