🌐 Nyra-B: The Creative & Context Core

Nyra-B is the secondary powerhouse model developed by Logihertz Systems OPC Pvt Ltd. As part of the independent Nyra Project, this model serves as the "Creative & Context Core" (Tier B), specifically optimized for long-context retention, nuanced natural language generation, and creative problem-solving.

🛠 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 vocabulary diversity and context flow)
• Language(s): English (Primary)

🎯 Intended Use Cases

Nyra-B is engineered for applications where flow, tone, and extensive context handling are paramount:

• Long-Form Generation: Drafting reports, documentation, and engaging textual content.
• Contextual Summarization: Processing large chunks of data or conversation history without losing critical nuance.
• Agentic Personas: Serving as the conversational interface for multi-agent systems, providing natural and dynamic responses.

📊 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
Multi-Turn Chat	MT-Bench	Average Score	Pending	Eval in Progress
Context Retrieval	Needle In A Haystack	32k Context Accuracy	Pending	Eval in Progress
Conversational Flow	AlpacaEval 2.0	Length-Controlled Win Rate	Pending	Eval in Progress
General Knowledge	MMLU-Pro	5-shot Accuracy	Pending	Eval in Progress
Factuality	TruthfulQA	Generation Accuracy	Pending	Eval in Progress

💻 Implementation

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

from transformers import AutoModelForCausalGeneration, AutoTokenizer
import torch

model_id = "logihertz/nyra-B"

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

prompt = "Explain the concept of neural network quantization using a creative analogy."
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-B is released under the Llama 3 Community License. Due to its creative optimization, it may occasionally generate plausible but factually incorrect statements (hallucinations) if not grounded by a prompt. Users should implement secondary validation systems for critical deployments.