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")
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

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
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

BurnMaster-1B 🔥

Introduction

BurnMaster-1B is a playful AI roast-bot built on top of 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

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)

Downloads last month: 5

Safetensors

Model size

2B params

Tensor type

BF16

Model tree for CoderPixel/BurnMaster-1B

Base model

Qwen/Qwen2.5-1.5B

Finetuned

Qwen/Qwen2.5-1.5B-Instruct

Finetuned

(1556)

this model

Quantizations

2 models