Platio_merged_model/README.md

---
license: mit
datasets:
- isaiahbjork/cot-logic-reasoning
base_model:
- meta-llama/Llama-3.1-8B-Instruct
pipeline_tag: text-generation
language:
- en
library_name: transformers
---

# PlaiTO 🧠✨  
*A Reasoning-Focused Language Model for the Humanities*

## Overview
**PlaiTO** is a reasoning-oriented language model designed specifically for **humanities and social sciences**. Built on top of **LLaMA 3.1 8B**, PlaiTO emphasizes structured thinking, conceptual understanding, and analytical reasoning rather than surface-level text generation.

The model performs especially well in domains where **theory, interpretation, decision-making, and human behavior** matter most.

## Base Model
- **Architecture:** LLaMA 3.1  
- **Parameters:** 8B  
- **Training Focus:** Reasoning, conceptual analysis, and humanities-oriented problem solving  

## Target Domains
PlaiTO is optimized for:
- **Psychology**
- **Management & Organizational Studies**
- **Sociology**
- Related humanities and social science disciplines

Typical use cases include:
- Theoretical analysis  
- Case study reasoning  
- Concept explanation and comparison  
- Decision-making support  
- Academic discussion and synthesis  

## Benchmark Performance
PlaiTO was evaluated on the **MMLU benchmark** using **100 samples** per subject area. Results show strong and consistent performance across key humanities domains:

| Domain                   | Accuracy |
|--------------------------|----------|
| Professional Psychology  | **76%**  |
| Management               | **74%**  |
| Sociology                | **75%**  |

These results indicate reliable reasoning capabilities in complex, abstract, and theory-heavy tasks.

## Strengths
- Strong **reasoning and analytical depth**
- Better handling of **abstract concepts** and **human-centered problems**
- Suitable for **academic**, **educational**, and **research-oriented** applications
- Balanced performance across multiple humanities disciplines

## Limitations
- Not optimized for mathematics-heavy or symbolic reasoning tasks  
- May underperform in domains requiring exact numerical computation  
- As with all LLMs, outputs should be reviewed for accuracy in high-stakes settings  

## Intended Use
PlaiTO is intended for:
- Research and academic exploration  
- Educational tools and tutoring systems  
- Decision-support in management and organizational contexts  
- Exploratory analysis in psychology and sociology

 #### Direct Usage 

 ```python
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from peft import PeftModel
import torch

# Define the model IDs
base_model_name_or_path = "alibidaran/Platio_merged_model" # The base Llama-3-8B-Instruct model

# 1. Configure 4-bit quantization
bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_use_double_quant=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.float16
)

# 2. Load the Base Model with the config
# Use device_map="auto" for efficient loading with quantization
# Use torch_dtype=torch.bfloat16 for Llama models with bnb
model = AutoModelForCausalLM.from_pretrained(
    base_model_name_or_path,# The PEFT adapter ID
    quantization_config=bnb_config,
    torch_dtype=torch.bfloat16,
    device_map="cuda",
)

tokenizer=AutoTokenizer.from_pretrained(base_model_name_or_path)
system_prompt="""
    You are a reasonable expert who thinks and answer the users question.
    Before respond first think and create a chain of thoughts in your mind.
    Then respond to the client.
    Your chain of thought and reflection must be in <thinking>..</thinking> format and your respond
    should be in the <output>..</output> format.
    """
   
    messages = [
                {'role':'system','content':system_prompt},
                {"role": "user", "content":message},

                ] 
      
    inputs = tokenizer.apply_chat_template(
            messages,
        tokenize = True,
        add_generation_prompt = True, # Must add for generation
        return_tensors = "pt",).to("cuda")
    inputs_shape=inputs['input_ids'].shape[1]
    with torch.no_grad():
        output=model.generate(**inputs, max_new_tokens =2048,
                   use_cache = True, temperature = 0.5, min_p = 0.9)


````

## Ethical Considerations
While PlaiTO is designed to reason about human behavior and society, it should **not** be used as a replacement for professional judgment in clinical, legal, or organizational decision-making. Always apply human oversight.

## License
Please refer to the license of the base **LLaMA 3.1** model and ensure compliance with its terms.