README.md

---
model-index:
- name: Ursa_Minor0.4
  model-id: Sculptor-AI/Ursa_Minor0.4
  results: []
---

# Ursa_Minor0.4

## Model Description

Ursa_Minor0.4 is a reasoning-focused language model developed by ExplodingCB2 (Sculptor-AI) and hosted on Hugging Face. It is designed to tackle complex reasoning tasks, demonstrating capabilities in multi-step inference, logical deduction, and contextual understanding.

**Key Features:**

* **Reasoning Prowess:** Emphasizes strong reasoning abilities over sheer memorization, aiming for accurate and logical responses.
* **Multi-Step Inference:** Capable of breaking down complex problems into smaller, manageable steps.
* **Logical Deduction:** Demonstrates proficiency in applying logical rules and principles to arrive at valid conclusions.
* **Contextual Understanding:** Exhibits an ability to grasp and utilize contextual information to enhance reasoning accuracy.
* **Developed by ExplodingCB2 & Kaileh57 (Sculptor-AI):** A model born from focused research and development in the field of AI reasoning.

## Intended Uses

* Answering complex questions that require multi-step reasoning.
* Solving logical puzzles and problems.
* Assisting in tasks that demand contextual understanding and inference.
* Research and development in the field of AI reasoning.
* Experimentation with advanced prompting techniques.

## How to Use

You can use the Ursa_Minor0.4 model through the Hugging Face Transformers library. Here's a basic example:

```python
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Sculptor-AI/Ursa_Minor")
model = AutoModelForCausalLM.from_pretrained("Sculptor-AI/Ursa_Minor")

prompt = "What are the prime factors of 42?"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=50)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)

print(response)