Instructions to use anktechsol/ankiGPT-small with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use anktechsol/ankiGPT-small with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("anktechsol/ankiGPT-small")
model = AutoModelForCausalLM.from_pretrained("anktechsol/ankiGPT-small")
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 anktechsol/ankiGPT-small with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/anktechsol/ankiGPT-small

SGLang

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

Docker Model Runner
How to use anktechsol/ankiGPT-small with Docker Model Runner:
```
docker model run hf.co/anktechsol/ankiGPT-small
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

anktechsol/ankiGPT-small

🧠 What is ankiGPT-small?

A conversational text-generation model fine-tuned from microsoft/DialoGPT-small for Indian scenarios—supporting English and Hinglish. Use it to generate stories, dialogue, quick responses, and creative text.

🚀 Quick Start

from transformers import pipeline
generator = pipeline("text-generation", model="anktechsol/ankiGPT-small")
prompt = "Write a short story about a day in the life of a student in a bustling Indian city."
result = generator(prompt, max_length=300, num_return_sequences=1)
print(result[0]['generated_text'])

Copy-paste this code to see instant results!

✨ Features

Conversational: Tuned for chat, stories, and messages
Language: English + Hinglish (Indian conversational flavor)
Base Model: DialoGPT-small
Size: 124M parameters (fast and lightweight)
Dataset: ai4bharat/indic-align (Indian context data)

💡 Example Outputs

Prompt: "Describe the Diwali celebrations in Mumbai."

Output: "The city sparkled with thousands of lights, families prepared delicious sweets, and friends gathered for bursting crackers, laughter echoing through the alleys."

Try your own prompts above!

⚠️ Limitations & Considerations

Tends to repeat on long text—adjust max_length and no_repeat_ngram_size as needed
Biased towards Indian contexts due to training data
Not for critical or factual information generation

🙌 Contributions & Community

Suggestions? Open an issue or start a discussion. We welcome community feedback!
Demo: Want a hands-on demo? Let us know!

🔗 References

anktechsol.com

Downloads last month: 10

Safetensors

Model size

0.1B params

Tensor type

F32

Model tree for anktechsol/ankiGPT-small

Base model

microsoft/DialoGPT-small

Finetuned

(53)

this model

Quantizations

2 models

anktechsol
/

ankiGPT-small