Instructions to use Artvv/rationality-debugger-v1.0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Artvv/rationality-debugger-v1.0 with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Artvv/rationality-debugger-v1.0")
model = AutoModelForCausalLM.from_pretrained("Artvv/rationality-debugger-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

vLLM

How to use Artvv/rationality-debugger-v1.0 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Artvv/rationality-debugger-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": "Artvv/rationality-debugger-v1.0",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Artvv/rationality-debugger-v1.0

SGLang

How to use Artvv/rationality-debugger-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 "Artvv/rationality-debugger-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": "Artvv/rationality-debugger-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 "Artvv/rationality-debugger-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": "Artvv/rationality-debugger-v1.0",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio new

How to use Artvv/rationality-debugger-v1.0 with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for Artvv/rationality-debugger-v1.0 to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for Artvv/rationality-debugger-v1.0 to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Artvv/rationality-debugger-v1.0 to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="Artvv/rationality-debugger-v1.0",
    max_seq_length=2048,
)

Docker Model Runner
How to use Artvv/rationality-debugger-v1.0 with Docker Model Runner:
```
docker model run hf.co/Artvv/rationality-debugger-v1.0
```

Model Card for Model ID

This is a fine-tuned model specifically dedicated to the identification of sophism and cognitive bias His performance for now is 85%-100% in detecting sophism , and 85%-100% for detectiong cognitive bias

It was trained with a custom dataset of 14k lines

Model Details

Model Description

Developed by: Arthur Vigier
- Model type: Qlora
Language(s) (NLP): English
License: Apache 2.0
Finetuned from model : mistral-7b-instruct-v0.3-bnb-4bit

Uses

It is dedicated to be used by anyone that want to judge public discourse based on the fundational basis of there language and the solidity of it. Using for education and increasing critical thinking is also a good way to use this tool

API

PUBLIC API COMING SOON

Performance chart

Sophism

[]

Cognitive Bias

[]

Direct Use

from transformers import pipeline

messages = [ {"role": "user", "content": "All birds can fly. Penguins are birds. Therefore, penguins can fly"}, ] pipe = pipeline("text-generation", model="Artvv/rationality-debugger-v1.0") pipe(messages)

Out-of-Scope Use

It is not intended to harass anyone or being rude

Bias, Risks, and Limitations

[More Information Needed]

Recommendations

He is very efficient to the most common sophism and cognitive bias but for some more niche like bias frequency illusion he can be less efficient. He is mainly dedicated to detect sophism and cognitive bias , he can detect valid reasoning but it is not his main purpose