Instructions to use Asgar-Ali-T/yoda-chatbot with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Asgar-Ali-T/yoda-chatbot with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Asgar-Ali-T/yoda-chatbot")
model = AutoModelForCausalLM.from_pretrained("Asgar-Ali-T/yoda-chatbot")
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

vLLM

How to use Asgar-Ali-T/yoda-chatbot with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Asgar-Ali-T/yoda-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": "Asgar-Ali-T/yoda-chatbot",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Asgar-Ali-T/yoda-chatbot

SGLang

How to use Asgar-Ali-T/yoda-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 "Asgar-Ali-T/yoda-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": "Asgar-Ali-T/yoda-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 "Asgar-Ali-T/yoda-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": "Asgar-Ali-T/yoda-chatbot",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Asgar-Ali-T/yoda-chatbot with Docker Model Runner:
```
docker model run hf.co/Asgar-Ali-T/yoda-chatbot
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Yoda Chatbot

A fine-tuned DialoGPT model trained to respond like Yoda from Star Wars.

Model Description

This model is based on Microsoft's DialoGPT-medium and has been fine-tuned on Yoda-style dialogue data to generate responses in Yoda's characteristic speech pattern.

Usage

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Asgar-Ali-T/yoda-chatbot")
model = AutoModelForCausalLM.from_pretrained("Asgar-Ali-T/yoda-chatbot")

# Example usage
prompt = "Human: What is the Force?\nYoda:"
inputs = tokenizer.encode(prompt, return_tensors="pt")
outputs = model.generate(inputs, max_length=100, temperature=0.7)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)

Training Data

The model was trained on custom Yoda dialogue data to learn his unique speech patterns and wisdom.

Model Performance

The model has been trained to respond in Yoda's characteristic manner, including:

Inverted sentence structure
Wise philosophical responses
Star Wars universe knowledge
Yoda's distinctive speech patterns

Limitations

This is a fine-tuned model and may not always produce perfect Yoda responses
Generated content should be used responsibly
The model is for entertainment purposes

License

This model is released under the MIT License.

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Safetensors

Model size

0.4B params

Tensor type

F32

Model tree for Asgar-Ali-T/yoda-chatbot

Quantizations

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