---
license: llama3
base_model:
  - meta-llama/Meta-Llama-3-8B
library_name: transformers
tags:
  - llama-3
  - merge
  - mergekit
  - logihertz
  - nyra
  - logic-core
  - independent-research
model_type: merge
pipeline_tag: text-generation
widget:
  - text: "Solve the following logical puzzle: If all A are B, and some C are A..."
    example_title: "Logical Reasoning"
---

# 🌐 Nyra-A: The Logic Core

**Nyra-A** is a specialized high-performance reasoning model developed by **Logihertz Systems OPC Pvt Ltd**. As part of the independent **Nyra Project**, this model serves as the "Primary Logic Core" (Tier A), optimized for mathematical consistency, structured data processing, and complex logical deduction.

## 🛠 Model Specifications

* **Developer:** Logihertz Systems
* **Lead Architect:** Sameer Tawade
* **Project Status:** Independent Research
* **Architecture:** Optimized Llama-3-8B (Transformer-based)
* **Merge Methodology:** DARE-TIES + SLERP (Optimized for weight-sum stability)
* **Language(s):** English (Primary)

## 🎯 Intended Use Cases

Nyra-A is engineered for standalone applications requiring high precision:

* **Algorithmic Reasoning:** Solving complex mathematical and logical proofs.
* **Structured Output:** Generating precise JSON, XML, and complex code structures.
* **Analytical Processing:** Acting as a refiner for complex multi-turn instructions where hallucination must be minimized.

## 📊 Evaluation & Benchmarking Matrix

*This model is currently undergoing rigorous evaluation. Scores are marked as pending while the self-verified evaluation pipeline completes.*

| **Category** | **Benchmark** | **Metric** | **Score** | **Status** |
| :--- | :--- | :--- | :--- | :--- |
| **General Reasoning** | MMLU-Pro | 5-shot Accuracy | *Pending* | Eval in Progress |
| **Math Execution** | GSM8K | 8-shot Strict Match | *Pending* | Eval in Progress |
| **Advanced Math** | MATH | 4-shot Chain-of-Thought| *Pending* | Eval in Progress |
| **Graduate Logic** | GPQA | 0-shot Accuracy | *Pending* | Eval in Progress |
| **Code Reasoning** | HumanEval | Pass@1 | *Pending* | Eval in Progress |

## 💻 Implementation

To run Nyra-A locally, ensure you have the latest `transformers` library installed.

```python
from transformers import AutoModelForCausalGeneration, AutoTokenizer
import torch

model_id = "logihertz/nyra-A"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalGeneration.from_pretrained(
    model_id, 
    torch_dtype=torch.float16, 
    device_map="auto"
)

prompt = "Analyze the efficiency of a recursive function versus an iterative approach."
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=512)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```

## ⚖️ Limitations & Ethical Considerations

Nyra-A is released under the Llama 3 Community License. While heavily optimized for logic, it may still exhibit occasional hallucinations or inherit biases from its foundational weights. Users should implement secondary validation systems for critical, public-facing deployments.