Instructions to use adiiiii13/bubblesort-llm with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use adiiiii13/bubblesort-llm with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("TinyLlama/TinyLlama-1.1B-Chat-v1.0")
model = PeftModel.from_pretrained(base_model, "adiiiii13/bubblesort-llm")

Transformers

How to use adiiiii13/bubblesort-llm with Transformers:

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

pipe = pipeline("text-generation", model="adiiiii13/bubblesort-llm")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("adiiiii13/bubblesort-llm", dtype="auto")

llama-cpp-python

How to use adiiiii13/bubblesort-llm with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="adiiiii13/bubblesort-llm",
	filename="bubblesort-llm.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use adiiiii13/bubblesort-llm with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf adiiiii13/bubblesort-llm
# Run inference directly in the terminal:
llama-cli -hf adiiiii13/bubblesort-llm

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf adiiiii13/bubblesort-llm
# Run inference directly in the terminal:
llama-cli -hf adiiiii13/bubblesort-llm

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf adiiiii13/bubblesort-llm
# Run inference directly in the terminal:
./llama-cli -hf adiiiii13/bubblesort-llm

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf adiiiii13/bubblesort-llm
# Run inference directly in the terminal:
./build/bin/llama-cli -hf adiiiii13/bubblesort-llm

Use Docker

docker model run hf.co/adiiiii13/bubblesort-llm

LM Studio
Jan

vLLM

How to use adiiiii13/bubblesort-llm with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "adiiiii13/bubblesort-llm"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "adiiiii13/bubblesort-llm",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/adiiiii13/bubblesort-llm

SGLang

How to use adiiiii13/bubblesort-llm 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 "adiiiii13/bubblesort-llm" \
    --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": "adiiiii13/bubblesort-llm",
		"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 "adiiiii13/bubblesort-llm" \
        --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": "adiiiii13/bubblesort-llm",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use adiiiii13/bubblesort-llm with Ollama:
```
ollama run hf.co/adiiiii13/bubblesort-llm
```

Unsloth Studio new

How to use adiiiii13/bubblesort-llm with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for adiiiii13/bubblesort-llm to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for adiiiii13/bubblesort-llm to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for adiiiii13/bubblesort-llm to start chatting

Docker Model Runner
How to use adiiiii13/bubblesort-llm with Docker Model Runner:
```
docker model run hf.co/adiiiii13/bubblesort-llm
```

Lemonade

How to use adiiiii13/bubblesort-llm with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull adiiiii13/bubblesort-llm

Run and chat with the model

lemonade run user.bubblesort-llm-{{QUANT_TAG}}

List all available models

lemonade list

🫧 BubbleSort-LLM

A fine-tuned TinyLLaMA-1.1B model with company-specific knowledge about Bubblesort.in and its startups.

Model Details

Model Description

BubbleSort-LLM is a LoRA fine-tuned version of TinyLLaMA designed to answer questions about Bubblesort.in, a tech company and startup ecosystem founded by Aditya Routh. The model has been trained to provide accurate information about the company's various ventures and services.

Developed by: Aditya Routh / Bubblesort.in
Model type: Causal Language Model (LoRA Adapter)
Language(s) (NLP): English
License: Apache 2.0
Finetuned from model: TinyLlama/TinyLlama-1.1B-Chat-v1.0

Model Sources

Repository: adiiiii13/bubblesort-llm
Demo: Coming Soon

About Bubblesort.in

Bubblesort.in is the parent organization for multiple startups:

Startup	Description	Website
🍛 Ghar Ka Khana	Homemade food service platform	gharkakhana2026.in
💼 GKK Intern	Internship platform for students	gkkintern.in
💚 Plutoz	Social/NGO initiative for children	plutoz1.netlify.app
🎨 APA Collective	Freelancing agency	apacollective.netlify.app

Uses

Direct Use

This model can be used for:

Answering questions about Bubblesort.in and its startups
Customer support chatbots for Bubblesort.in services
Information retrieval about company services

Out-of-Scope Use

General knowledge questions (use base TinyLLaMA instead)
Tasks requiring factual accuracy outside Bubblesort.in domain
Production use without additional testing

How to Get Started with the Model

from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline

# Load model
base_model = AutoModelForCausalLM.from_pretrained("TinyLlama/TinyLlama-1.1B-Chat-v1.0")
model = PeftModel.from_pretrained(base_model, "adiiiii13/bubblesort-llm")
tokenizer = AutoTokenizer.from_pretrained("adiiiii13/bubblesort-llm")

# Create pipeline
pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)

# Chat format
messages = [
    {"role": "system", "content": "You are a helpful assistant for Bubblesort.in"},
    {"role": "user", "content": "What is Bubblesort.in?"}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
output = pipe(prompt, max_new_tokens=150, do_sample=True, temperature=0.7)
print(output[0]['generated_text'])

## Training Details

### Training Data

Custom dataset containing information about Bubblesort.in, its services, startups, and company details.

### Training Procedure

#### Training Hyperparameters

| Parameter | Value |
|-----------|-------|
| LoRA Rank (r) | 16 |
| LoRA Alpha | 32 |
| LoRA Dropout | 0.05 |
| Target Modules | q_proj, k_proj, v_proj, o_proj |
| Training regime | bf16 mixed precision |

## Technical Specifications

### Model Architecture and Objective

- **Architecture:** LLaMA-based transformer with LoRA adapters
- **Parameters:** ~18MB adapter weights
- **Objective:** Causal language modeling

### Compute Infrastructure

#### Hardware

- Kaggle GPU (T4/P100)

#### Software

- Transformers
- PEFT 0.18.1
- PyTorch

## Citation

```bibtex
@misc{bubblesort-llm,
  author = {Aditya Routh},
  title = {BubbleSort-LLM: A Fine-tuned TinyLLaMA for Bubblesort.in},
  year = {2026},
  publisher = {HuggingFace},
  url = {https://huggingface.co/adiiiii13/bubblesort-llm}
}

Model Card Authors
Aditya Routh (@adiiiii13)

Model Card Contact
GitHub: aditya04slg
Website: adityarouth.site
Framework Versions
PEFT: 0.18.1
Transformers: 4.x
PyTorch: 2.x
Made with 💜 by Bubblesort.in

Downloads last month: 1

GGUF

Model size

1B params

Architecture

llama

Hardware compatibility

We're not able to determine the quantization variants.

View all variants

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Base model

TinyLlama/TinyLlama-1.1B-Chat-v1.0

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