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Model: logihertz/nyra-A
Source: Original Platform

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README.md

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+---
+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.

config.json

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+{
+  "architectures": [
+    "LlamaForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 128000,
+  "dtype": "float16",
+  "eos_token_id": [
+    128001,
+    128008,
+    128009
+  ],
+  "head_dim": 128,
+  "hidden_act": "silu",
+  "hidden_size": 4096,
+  "initializer_range": 0.02,
+  "intermediate_size": 14336,
+  "max_position_embeddings": 131072,
+  "mlp_bias": false,
+  "model_type": "llama",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 32,
+  "num_key_value_heads": 8,
+  "pretraining_tp": 1,
+  "rms_norm_eps": 1e-05,
+  "rope_scaling": {
+    "factor": 8.0,
+    "high_freq_factor": 4.0,
+    "low_freq_factor": 1.0,
+    "original_max_position_embeddings": 8192,
+    "rope_type": "llama3"
+  },
+  "rope_theta": 500000.0,
+  "tie_word_embeddings": false,
+  "transformers_version": "4.57.6",
+  "use_cache": true,
+  "vocab_size": 128256
+}

mergekit_config.yml

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+models:
+  - model: /media/aura/projects/council/Llama-Base
+  - model: /media/aura/projects/council/Llama-R1
+    parameters:
+      density: 0.5
+      weight: 1.0
+  - model: /media/aura/projects/council/Llama-R1-Justin
+    parameters:
+      density: 0.5
+      weight: 1.0
+  - model: /media/aura/projects/council/Llama-Tulu
+    parameters:
+      density: 0.5
+      weight: 1.0
+
+merge_method: dare_ties
+base_model: /media/aura/projects/council/Llama-Base
+parameters:
+  int8_mask: true
+dtype: float16

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