Instructions to use CoderPixel/BurnMaster-1B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use CoderPixel/BurnMaster-1B with Transformers:

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

pipe = pipeline("text-generation", model="CoderPixel/BurnMaster-1B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("CoderPixel/BurnMaster-1B")
model = AutoModelForCausalLM.from_pretrained("CoderPixel/BurnMaster-1B", 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=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use CoderPixel/BurnMaster-1B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "CoderPixel/BurnMaster-1B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "CoderPixel/BurnMaster-1B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/CoderPixel/BurnMaster-1B

SGLang

How to use CoderPixel/BurnMaster-1B 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 "CoderPixel/BurnMaster-1B" \
    --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": "CoderPixel/BurnMaster-1B",
		"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 "CoderPixel/BurnMaster-1B" \
        --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": "CoderPixel/BurnMaster-1B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use CoderPixel/BurnMaster-1B with Docker Model Runner:
```
docker model run hf.co/CoderPixel/BurnMaster-1B
```

BurnMaster-1B / README.md

CoderPixel

Update README.md

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	---
	license: apache-2.0
	language:
	- en
	pipeline_tag: text-generation
	base_model: Qwen/Qwen2.5-1.5B-Instruct
	tags:
	- roast
	- fun
	- humor
	- chatbot
	- text-generation
	library_name: transformers
	---

	# BurnMaster-1B 🔥

	## Introduction

	BurnMaster-1B is a playful AI roast-bot built on top of [Qwen2.5-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct).
	Instead of being a generic assistant, BurnMaster specializes in delivering short, witty, clean roasts for fun and entertainment.

	✨ Features:
	- Clean & funny burns, safe for friends
	- Adjustable spice levels (from teasing → max spicy roast)
	- Preloaded with random roast ideas for instant laughs
	- Powered by the Qwen2.5-1.5B-Instruct model architecture

	## Quickstart

	```python
	from transformers import AutoModelForCausalLM, AutoTokenizer
	import torch

	model_name = "CoderPixel/BurnMaster-1B"

	tokenizer = AutoTokenizer.from_pretrained(model_name)
	model = AutoModelForCausalLM.from_pretrained(
	model_name,
	torch_dtype=torch.float16,
	device_map="auto"
	)

	messages = [
	{"role": "system", "content": "You are BurnMaster, an AI that delivers short, funny, clean roasts."},
	{"role": "user", "content": "Roast me like I rage quit Roblox after losing to a 9-year-old."}
	]

	text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
	inputs = tokenizer([text], return_tensors="pt").to(model.device)

	outputs = model.generate(**inputs, max_new_tokens=100, do_sample=True, temperature=0.9, top_p=0.9)
	response = tokenizer.decode(outputs[0][inputs['input_ids'].shape[1]:], skip_special_tokens=True)

	print(response)