Instructions to use logihertz/nyra-A with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use logihertz/nyra-A with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="logihertz/nyra-A")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("logihertz/nyra-A")
model = AutoModelForCausalLM.from_pretrained("logihertz/nyra-A")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use logihertz/nyra-A with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "logihertz/nyra-A"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "logihertz/nyra-A",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/logihertz/nyra-A

SGLang

How to use logihertz/nyra-A with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "logihertz/nyra-A" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "logihertz/nyra-A",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "logihertz/nyra-A" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "logihertz/nyra-A",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use logihertz/nyra-A with Docker Model Runner:
```
docker model run hf.co/logihertz/nyra-A
```

🌐 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.

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.

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Safetensors

Model size

8B params

Tensor type

F16

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