80 lines
3.1 KiB
Markdown
80 lines
3.1 KiB
Markdown
---
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license: llama3
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base_model:
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- meta-llama/Meta-Llama-3-8B
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library_name: transformers
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tags:
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- llama-3
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- merge
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- mergekit
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- logihertz
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- nyra
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- logic-core
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- independent-research
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model_type: merge
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pipeline_tag: text-generation
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widget:
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- text: "Solve the following logical puzzle: If all A are B, and some C are A..."
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example_title: "Logical Reasoning"
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---
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# 🌐 Nyra-A: The Logic Core
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**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.
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## 🛠 Model Specifications
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* **Developer:** Logihertz Systems
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* **Lead Architect:** Sameer Tawade
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* **Project Status:** Independent Research
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* **Architecture:** Optimized Llama-3-8B (Transformer-based)
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* **Merge Methodology:** DARE-TIES + SLERP (Optimized for weight-sum stability)
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* **Language(s):** English (Primary)
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## 🎯 Intended Use Cases
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Nyra-A is engineered for standalone applications requiring high precision:
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* **Algorithmic Reasoning:** Solving complex mathematical and logical proofs.
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* **Structured Output:** Generating precise JSON, XML, and complex code structures.
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* **Analytical Processing:** Acting as a refiner for complex multi-turn instructions where hallucination must be minimized.
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## 📊 Evaluation & Benchmarking Matrix
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*This model is currently undergoing rigorous evaluation. Scores are marked as pending while the self-verified evaluation pipeline completes.*
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| **Category** | **Benchmark** | **Metric** | **Score** | **Status** |
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| :--- | :--- | :--- | :--- | :--- |
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| **General Reasoning** | MMLU-Pro | 5-shot Accuracy | *Pending* | Eval in Progress |
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| **Math Execution** | GSM8K | 8-shot Strict Match | *Pending* | Eval in Progress |
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| **Advanced Math** | MATH | 4-shot Chain-of-Thought| *Pending* | Eval in Progress |
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| **Graduate Logic** | GPQA | 0-shot Accuracy | *Pending* | Eval in Progress |
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| **Code Reasoning** | HumanEval | Pass@1 | *Pending* | Eval in Progress |
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## 💻 Implementation
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To run Nyra-A locally, ensure you have the latest `transformers` library installed.
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```python
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from transformers import AutoModelForCausalGeneration, AutoTokenizer
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import torch
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model_id = "logihertz/nyra-A"
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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model = AutoModelForCausalGeneration.from_pretrained(
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model_id,
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torch_dtype=torch.float16,
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device_map="auto"
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)
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prompt = "Analyze the efficiency of a recursive function versus an iterative approach."
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inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
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outputs = model.generate(**inputs, max_new_tokens=512)
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print(tokenizer.decode(outputs[0], skip_special_tokens=True))
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```
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## ⚖️ Limitations & Ethical Considerations
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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. |