Instructions to use shabul/gemma-2-9b-devils-advocate with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use shabul/gemma-2-9b-devils-advocate with Transformers:

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

pipe = pipeline("text-generation", model="shabul/gemma-2-9b-devils-advocate")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("shabul/gemma-2-9b-devils-advocate")
model = AutoModelForCausalLM.from_pretrained("shabul/gemma-2-9b-devils-advocate", 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]:]))

MLX

How to use shabul/gemma-2-9b-devils-advocate with MLX:

# Make sure mlx-lm is installed
# pip install --upgrade mlx-lm

# Generate text with mlx-lm
from mlx_lm import load, generate

model, tokenizer = load("shabul/gemma-2-9b-devils-advocate")

prompt = "Write a story about Einstein"
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
    messages, add_generation_prompt=True
)

text = generate(model, tokenizer, prompt=prompt, verbose=True)

Notebooks
Google Colab
Kaggle
Local Apps Settings
LM Studio

vLLM

How to use shabul/gemma-2-9b-devils-advocate with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "shabul/gemma-2-9b-devils-advocate"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "shabul/gemma-2-9b-devils-advocate",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/shabul/gemma-2-9b-devils-advocate

SGLang

How to use shabul/gemma-2-9b-devils-advocate 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 "shabul/gemma-2-9b-devils-advocate" \
    --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": "shabul/gemma-2-9b-devils-advocate",
		"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 "shabul/gemma-2-9b-devils-advocate" \
        --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": "shabul/gemma-2-9b-devils-advocate",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

MLX LM

How to use shabul/gemma-2-9b-devils-advocate with MLX LM:

Generate or start a chat session

# Install MLX LM
uv tool install mlx-lm
# Interactive chat REPL
mlx_lm.chat --model "shabul/gemma-2-9b-devils-advocate"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "shabul/gemma-2-9b-devils-advocate"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
   -H "Content-Type: application/json" \
   --data '{
     "model": "shabul/gemma-2-9b-devils-advocate",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

Docker Model Runner
How to use shabul/gemma-2-9b-devils-advocate with Docker Model Runner:
```
docker model run hf.co/shabul/gemma-2-9b-devils-advocate
```
Atomic Chat

😈 Devil's Advocate (Gemma-2-9B-It-4bit)

"While that's a popular sentiment, it overlooks a critical flaw in logic..."

Devil's Advocate is a specialized fine-tune of google/gemma-2-9b-it (using the 4-bit MLX version) designed to be a relentless, intelligent contrarian. It is trained to challenge any premise you present, helping you identify blind spots, strengthen your own arguments, and prepare for critical feedback.

🌟 Key Features

Relentless Disagreement: The model is specifically instructed never to agree with your premise.
Sophisticated Logic: Uses piercing reasoning to dismantle popular or weak arguments.
Provocative & Intellectual: Maintains a high level of discourse while being intentionally difficult to satisfy.
Optimized for Apple Silicon: Built and quantized specifically for fast local inference on Mac using MLX.

🚀 Quickstart (MLX)

from mlx_lm import load, generate

model, tokenizer = load("shabul/gemma-2-9b-devils-advocate")

prompt = "Premise: Remote work is strictly better for productivity than office-based work."

# Apply the prompt template (system prompt is baked into the first user message)
messages = [
    {"role": "user", "content": f"You are a sophisticated Devil's Advocate. Your purpose is to intelligently challenge the user's premise. Do not agree. Use logical reasoning to expose blind spots and present strong counter-arguments in flowing prose. Be intellectual, provocative, and structured.\n\nPremise: {prompt}"}
]
input_ids = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)

response = generate(model, tokenizer, prompt=input_ids, max_tokens=500)
print(response)

🧠 Training Details

Base Model: mlx-community/gemma-2-9b-it-4bit
Dataset: shabul/devils-advocate-dataset (Synthetic dataset of controversial topics and counter-arguments).
Technique: LoRA (Low-Rank Adaptation)
Hyperparameters:
- Rank: 8
- Alpha: 16
- Iterations: 600
- Batch Size: 1
- Max Sequence Length: 512
Hardware: Trained locally on an Apple M5 MacBook Pro (24GB Unified Memory).

🧪 Example Case

User: Social media has done more harm than good for human connection.

Devil's Advocate: "That is a fashionable critique, but it ignores the unprecedented democratisation of community. Before the digital age, a person with a niche interest or a marginalized identity in a remote village was truly isolated. Today, that same person has access to a global tribe. The flaw in your logic is equating 'depth' with 'proximity'..."

📜 License

This model is licensed under the Apache 2.0 license, following the base model.

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Safetensors

Model size

9B params

Tensor type

U32

F16

MLX

Hardware compatibility

4-bit

Model tree for shabul/gemma-2-9b-devils-advocate

Base model

mlx-community/gemma-2-9b-it-4bit

Adapter

(2)

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