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 Settings

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

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

adiiiii13 commited on Mar 22

Commit

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@@ -1,207 +1,154 @@
 ---
 base_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0
 library_name: peft
 pipeline_tag: text-generation
 tags:
 - base_model:adapter:TinyLlama/TinyLlama-1.1B-Chat-v1.0
 - lora
 - transformers
 ---
-# Model Card for Model ID
-<!-- Provide a quick summary of what the model is/does. -->
 ## Model Details
 ### Model Description
-<!-- Provide a longer summary of what this model is. -->
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
-### Model Sources [optional]
-<!-- Provide the basic links for the model. -->
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
 ## Uses
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
 ### Direct Use
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-[More Information Needed]
-### Downstream Use [optional]
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-[More Information Needed]
 ### Out-of-Scope Use
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-[More Information Needed]
-## Bias, Risks, and Limitations
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-[More Information Needed]
-### Recommendations
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-## How to Get Started with the Model
-Use the code below to get started with the model.
-[More Information Needed]
 ## Training Details
 ### Training Data
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
-[More Information Needed]
 ### Training Procedure
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-#### Preprocessing [optional]
-[More Information Needed]
 #### Training Hyperparameters
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-#### Speeds, Sizes, Times [optional]
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-[More Information Needed]
-## Evaluation
-<!-- This section describes the evaluation protocols and provides the results. -->
-### Testing Data, Factors & Metrics
-#### Testing Data
-<!-- This should link to a Dataset Card if possible. -->
-[More Information Needed]
-#### Factors
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-[More Information Needed]
-#### Metrics
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-[More Information Needed]
-### Results
-[More Information Needed]
-#### Summary
-## Model Examination [optional]
-<!-- Relevant interpretability work for the model goes here -->
-[More Information Needed]
-## Environmental Impact
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-## Technical Specifications [optional]
 ### Model Architecture and Objective
-[More Information Needed]
 ### Compute Infrastructure
-[More Information Needed]
 #### Hardware
-[More Information Needed]
 #### Software
-[More Information Needed]
-## Citation [optional]
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
-**BibTeX:**
-[More Information Needed]
-**APA:**
-[More Information Needed]
-## Glossary [optional]
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-[More Information Needed]
-## More Information [optional]
-[More Information Needed]
-## Model Card Authors [optional]
-[More Information Needed]
-## Model Card Contact
-[More Information Needed]
-### Framework versions
-- PEFT 0.18.1

 ---
 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](https://huggingface.co/adiiiii13))
+## Model Card Contact
+- **GitHub:** [aditya04slg](https://github.com/aditya04slg)
+- **Website:** [adityarouth.site](https://adityarouth.site)
+### Framework Versions
+- PEFT: 0.18.1
+- Transformers: 4.x
+- PyTorch: 2.x
+---
+Made with 💜 by [Bubblesort.in](https://bubblesort.in)