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 / README.md

adiiiii13

Update README.md

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	---
	base_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0
	library_name: peft
	license: apache-2.0
	language:
	- en
	pipeline_tag: text-generation
	tags:
	- base_model:adapter:TinyLlama/TinyLlama-1.1B-Chat-v1.0
	- lora
	- transformers
	- tinyllama
	- bubblesort
	- fine-tuned
	---

	# 🫧 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](https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v1.0)

	### Model Sources

	- Repository: [adiiiii13/bubblesort-llm](https://huggingface.co/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](https://gharkakhana2026.in/) \|
	\| 💼 GKK Intern \| Internship platform for students \| [gkkintern.in](https://gkkintern.in) \|
	\| 💚 Plutoz \| Social/NGO initiative for children \| [plutoz1.netlify.app](https://plutoz1.netlify.app/) \|
	\| 🎨 APA Collective \| Freelancing agency \| [apacollective.netlify.app](https://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

	```python
	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