Instructions to use dbishwarup/TagoreGPT-29M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use dbishwarup/TagoreGPT-29M with Transformers:

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

pipe = pipeline("text-generation", model="dbishwarup/TagoreGPT-29M")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("dbishwarup/TagoreGPT-29M")
model = AutoModelForCausalLM.from_pretrained("dbishwarup/TagoreGPT-29M", 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=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use dbishwarup/TagoreGPT-29M with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf dbishwarup/TagoreGPT-29M:F16
# Run inference directly in the terminal:
llama cli -hf dbishwarup/TagoreGPT-29M:F16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf dbishwarup/TagoreGPT-29M:F16
# Run inference directly in the terminal:
llama cli -hf dbishwarup/TagoreGPT-29M:F16

Build from source code

git clone https://github.com/ggml-org/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-app
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama serve -hf dbishwarup/TagoreGPT-29M:F16
# Run inference directly in the terminal:
./build/bin/llama cli -hf dbishwarup/TagoreGPT-29M:F16

Use Docker

docker model run hf.co/dbishwarup/TagoreGPT-29M:F16

Llama new
LM Studio
Jan

vLLM

How to use dbishwarup/TagoreGPT-29M with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "dbishwarup/TagoreGPT-29M"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "dbishwarup/TagoreGPT-29M",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/dbishwarup/TagoreGPT-29M:F16

SGLang

How to use dbishwarup/TagoreGPT-29M 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 "dbishwarup/TagoreGPT-29M" \
    --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": "dbishwarup/TagoreGPT-29M",
		"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 "dbishwarup/TagoreGPT-29M" \
        --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": "dbishwarup/TagoreGPT-29M",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use dbishwarup/TagoreGPT-29M with Ollama:
```
ollama run hf.co/dbishwarup/TagoreGPT-29M:F16
```
Unsloth Desktop
Docker Model Runner
How to use dbishwarup/TagoreGPT-29M with Docker Model Runner:
```
docker model run hf.co/dbishwarup/TagoreGPT-29M:F16
```

Lemonade

How to use dbishwarup/TagoreGPT-29M with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull dbishwarup/TagoreGPT-29M:F16

Run and chat with the model

lemonade run user.TagoreGPT-29M-F16

List all available models

lemonade list

Atomic Chat

TagoreGPT 29M

A small (29 million parameter) model that writes poems, songs, stories, letters, essays and plays in the style of Rabindranath Tagore (1861–1941), in Bengali or English. Trained from scratch on a laptop.

Sir Rabindranath Tagore (Gurudev) was the first non-European to win the Nobel Prize in Literature (1913, for Gitanjali) and wrote the national anthems of India and Bangladesh.

How to ask

Ask in plain words. The app/chat template wraps it as <|request|>…<|response|>:

write a poem about the river in Bengali
write a song about the rain
একটা কবিতা লেখো বৃষ্টি নিয়ে
write a letter to a friend in English

Settings that work well: temperature 0.8, top-k 40, repeat penalty 1.15 (last 256 tokens), up to ~400 new tokens. Without a repeat penalty the model can loop (LM Studio: set "Repeat Penalty" to 1.15).

LM Studio / llama.cpp / Ollama

File: tagoregpt-29m-f16.gguf (Llama layout, 58 MB).
llama.cpp: llama-cli -m tagoregpt-29m-f16.gguf -cnv --temp 0.8 --top-k 40
Ollama: ollama create tagoregpt -f Modelfile then ollama run tagoregpt "write a poem about the river"

Transformers

from transformers import AutoTokenizer, AutoModelForCausalLM
tok = AutoTokenizer.from_pretrained("PATH_OR_REPO")
model = AutoModelForCausalLM.from_pretrained("PATH_OR_REPO")
ids = tok.apply_chat_template([{"role": "user", "content": "write a poem about the river"}], return_tensors="pt")
out = model.generate(ids, max_new_tokens=300)
print(tok.decode(out[0][ids.shape[1]:], skip_special_tokens=True))

What it cannot do

It is not a chatbot: it does not answer questions, explain, or translate. It only writes.
Topics given in English for a Bengali piece are not translated outside the TagoreGPT app.
It sees about 1,024 word pieces (~3,300 letters) at a time; longer pieces can wander. (The TagoreGPT app also keeps the title and opening paragraph in view; plain llama.cpp / LM Studio do not.)
It is small: lines can be beautiful or nonsensical. It is a style imitation, never Tagore's real words.

Training

Llama layout: 8 layers, width 512, 8 heads, SwiGLU 1344, rotary positions, tied embeddings. 8,000 SentencePiece word pieces (Bengali + English).
Phase 1 (warm-up): simple practice stories (TinyStories, CDLA-Sharing-1.0; Regional-TinyStories Bengali, Apache-2.0), other Bengali writers (Bengali Wikisource) + 19th-century English (Project Gutenberg) + Tagore.
Phase 2: Tagore only (Bengali scans from the Digital Library of India / Internet Archive, English from Project Gutenberg).
Every training piece starts with hidden labels (language, type, author); its title and opening paragraph stay in view, which keeps long pieces on their hero and topic.
Weights in this release: tagore step 4999. Unseen-text loss per word piece: tagore_en: 3.426, tagore_bn: 3.912.

Data and licence

Tagore's works are in the public domain. The Bengali warm-up text comes from Bengali Wikisource (CC BY-SA 4.0), so this model is released under CC BY-SA 4.0. English text from Project Gutenberg (public domain).

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Safetensors

Model size

31.1M params

Tensor type

F32