Instructions to use SeranVishwa/master-buddy-v1.0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use SeranVishwa/master-buddy-v1.0 with Transformers:

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

pipe = pipeline("text-generation", model="SeranVishwa/master-buddy-v1.0")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("SeranVishwa/master-buddy-v1.0")
model = AutoModelForCausalLM.from_pretrained("SeranVishwa/master-buddy-v1.0")
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 Settings

vLLM

How to use SeranVishwa/master-buddy-v1.0 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "SeranVishwa/master-buddy-v1.0"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "SeranVishwa/master-buddy-v1.0",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/SeranVishwa/master-buddy-v1.0

SGLang

How to use SeranVishwa/master-buddy-v1.0 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 "SeranVishwa/master-buddy-v1.0" \
    --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": "SeranVishwa/master-buddy-v1.0",
		"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 "SeranVishwa/master-buddy-v1.0" \
        --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": "SeranVishwa/master-buddy-v1.0",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use SeranVishwa/master-buddy-v1.0 with Docker Model Runner:
```
docker model run hf.co/SeranVishwa/master-buddy-v1.0
```

Model Card for Master Buddy AI v1.0

Master Buddy AI is a fine-tuned conversational language model designed as a computer science and engineering study assistant. It is built to help users understand programming concepts, algorithms, and general technical topics in a structured and helpful manner.

Model Details

Model Description

Master Buddy AI v1.0 is a decoder-only transformer-based language model fine-tuned using:

Supervised Fine-Tuning (SFT)
Direct Preference Optimization (DPO)

The goal of this model is to improve response helpfulness, reasoning ability, and instruction-following quality.

Developed by: Seran Vishwa
Model type: Causal Language Model (Decoder-only Transformer)
Language(s): English
License: Apache-2.0
Finetuned from model: LLaMA-based architecture or equivalent base model

Model Sources

Repository: https://huggingface.co/SeranVishwa/master-buddy-v1.0
Demo: Add deployment link when available

Uses

Direct Use

This model is intended for:

Programming assistance
Data structures and algorithms explanations
Computer science concepts (OS, DBMS, Networks)
Educational and study support
General question answering

Downstream Use

The model can be integrated into:

AI tutoring systems
Chatbot applications
Educational platforms
Developer tools
Web-based AI assistants

Out-of-Scope Use

This model is not intended for:

Medical advice
Legal advice
Financial decision-making
Safety-critical systems
Harmful or malicious use cases

Bias, Risks, and Limitations

The model may produce incorrect or hallucinated responses
It reflects biases present in training data
It should not be used as a sole source of truth
Outputs should be verified for critical use cases

Recommendations

Users should:

Verify important technical outputs
Use the model as a learning assistant, not an authority
Be aware of possible reasoning errors

How to Get Started

Transformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("SeranVishwa/master-buddy-v1.0")
tokenizer = AutoTokenizer.from_pretrained("SeranVishwa/master-buddy-v1.0")

prompt = "Explain Dijkstra's algorithm"

inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=200)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

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Safetensors

Model size

1B params

Tensor type

BF16