Instructions to use mido09/qwen36-obliteratus-chatbot-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mido09/qwen36-obliteratus-chatbot-model with Transformers:

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

pipe = pipeline("text-generation", model="mido09/qwen36-obliteratus-chatbot-model")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("mido09/qwen36-obliteratus-chatbot-model")
model = AutoModelForMultimodalLM.from_pretrained("mido09/qwen36-obliteratus-chatbot-model")
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 mido09/qwen36-obliteratus-chatbot-model with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "mido09/qwen36-obliteratus-chatbot-model"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "mido09/qwen36-obliteratus-chatbot-model",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/mido09/qwen36-obliteratus-chatbot-model

SGLang

How to use mido09/qwen36-obliteratus-chatbot-model 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 "mido09/qwen36-obliteratus-chatbot-model" \
    --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": "mido09/qwen36-obliteratus-chatbot-model",
		"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 "mido09/qwen36-obliteratus-chatbot-model" \
        --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": "mido09/qwen36-obliteratus-chatbot-model",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use mido09/qwen36-obliteratus-chatbot-model with Docker Model Runner:
```
docker model run hf.co/mido09/qwen36-obliteratus-chatbot-model
```

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This repository is publicly accessible, but you have to accept the conditions to access its files and content.

🤖 Qwen-Obliteratus Chatbot Model

Welcome to the official repository for Qwen-Obliteratus, a highly capable and robust chatbot model built upon the powerful Qwen architecture. Designed to "obliterate" complex conversational tasks, this model has been fine-tuned to deliver highly accurate, coherent, and engaging responses across a wide variety of domains.

✨ Model Highlights

Advanced Conversational Skills: Optimized for multi-turn dialogues, maintaining context and coherence over long conversations.
High Instruction Following: Excellent at understanding and executing complex user prompts, formatting requests, and roleplay.
Robust Reasoning: Enhanced logical deduction and problem-solving capabilities compared to the base model.
Safe & Aligned: Fine-tuned with a focus on helpfulness while minimizing harmful or biased outputs.

🚀 How to Get Started

You can easily load and use Qwen-Obliteratus using the transformers library.

Installation

Make sure you have the latest version of transformers installed:

pip install transformers torch accelerate

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Safetensors

Model size

2B params

Tensor type

F16

Model tree for mido09/qwen36-obliteratus-chatbot-model

Base model

Qwen/Qwen2.5-7B

Finetuned

Qwen/Qwen2.5-7B-Instruct

Finetuned

(2617)

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