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
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.