Instructions to use logihertzsystems/Nyra-Code-7B-GGUF-IQ1_M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use logihertzsystems/Nyra-Code-7B-GGUF-IQ1_M with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="logihertzsystems/Nyra-Code-7B-GGUF-IQ1_M",
	filename="Nyra-Code-7B.IQ1_M.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use logihertzsystems/Nyra-Code-7B-GGUF-IQ1_M with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf logihertzsystems/Nyra-Code-7B-GGUF-IQ1_M:IQ1_M
# Run inference directly in the terminal:
llama-cli -hf logihertzsystems/Nyra-Code-7B-GGUF-IQ1_M:IQ1_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf logihertzsystems/Nyra-Code-7B-GGUF-IQ1_M:IQ1_M
# Run inference directly in the terminal:
llama-cli -hf logihertzsystems/Nyra-Code-7B-GGUF-IQ1_M:IQ1_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf logihertzsystems/Nyra-Code-7B-GGUF-IQ1_M:IQ1_M
# Run inference directly in the terminal:
./llama-cli -hf logihertzsystems/Nyra-Code-7B-GGUF-IQ1_M:IQ1_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf logihertzsystems/Nyra-Code-7B-GGUF-IQ1_M:IQ1_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf logihertzsystems/Nyra-Code-7B-GGUF-IQ1_M:IQ1_M

Use Docker

docker model run hf.co/logihertzsystems/Nyra-Code-7B-GGUF-IQ1_M:IQ1_M

LM Studio
Jan

vLLM

How to use logihertzsystems/Nyra-Code-7B-GGUF-IQ1_M with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "logihertzsystems/Nyra-Code-7B-GGUF-IQ1_M"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "logihertzsystems/Nyra-Code-7B-GGUF-IQ1_M",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/logihertzsystems/Nyra-Code-7B-GGUF-IQ1_M:IQ1_M

Ollama
How to use logihertzsystems/Nyra-Code-7B-GGUF-IQ1_M with Ollama:
```
ollama run hf.co/logihertzsystems/Nyra-Code-7B-GGUF-IQ1_M:IQ1_M
```

Unsloth Studio new

How to use logihertzsystems/Nyra-Code-7B-GGUF-IQ1_M with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for logihertzsystems/Nyra-Code-7B-GGUF-IQ1_M to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for logihertzsystems/Nyra-Code-7B-GGUF-IQ1_M to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for logihertzsystems/Nyra-Code-7B-GGUF-IQ1_M to start chatting

Docker Model Runner
How to use logihertzsystems/Nyra-Code-7B-GGUF-IQ1_M with Docker Model Runner:
```
docker model run hf.co/logihertzsystems/Nyra-Code-7B-GGUF-IQ1_M:IQ1_M
```

Lemonade

How to use logihertzsystems/Nyra-Code-7B-GGUF-IQ1_M with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull logihertzsystems/Nyra-Code-7B-GGUF-IQ1_M:IQ1_M

Run and chat with the model

lemonade run user.Nyra-Code-7B-GGUF-IQ1_M-IQ1_M

List all available models

lemonade list

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Nyra Code - 7B (Titan Series)

Logihertz Universal Local Intelligence | Production Build v1.0

🛰️ Asset Overview

Nyra Code is a proprietary neural component engineered by Logihertz Systems (OPC) Private Limited, Mumbai. Designed as a high-fidelity "Local AI" solution for cross-platform deployment, this model leverages High-Fidelity Neural Synthesis to provide deterministic reasoning capabilities optimized for standalone hardware execution without cloud dependency.

📥 Secure Payload Delivery

Format: GGUF (IQ1_M) Access Point: ⬇️ Download Nyra-Code-7B.IQ1_M.gguf

🏗️ Strategic Ecosystem

☁️ Enterprise Cloud Infrastructure by AWS

Powered by Tier-1 Enterprise Cloud Compute by AWS, enabling massive-scale neural synthesis, high-performance training, and secure model distribution.

🎓 Academic Incubation

Physically incubated at Indala College of Engineering (MOU Partner), sourcing elite Indian engineering talent for proprietary AI research and optimization.

🛡️ Access Control & Security Gating

This repository is a Protected Sovereign Asset. Access is strictly limited to authorized partners and verified institutional requestors.

Manual Review: All requests are vetted by the Logihertz Directorate (Mumbai R&D Center).
Turnaround: Vetting is typically completed within 48 Hours.
Requirement: Requests must provide a valid corporate/academic identity and intended deployment environment.

⚖️ Intellectual Property

Unauthorized weight extraction, redistribution, or reverse-engineering is strictly prohibited under Indian and International Intellectual Property Law. This build is a closed-source production artifact.

Logihertz Systems (OPC) Private Limited

DPIIT Recognized Startup | GST: 27AAGCL6394P1ZL

Mumbai, Maharashtra, India 🇮🇳

Downloads last month: 4

GGUF

Model size

8B params

Architecture

qwen2

Hardware compatibility

1-bit

Evaluation results

Gsm8k on openai/gsm8k View evaluation results leaderboard

78.92
Eval
on allenai/ai2_arc View evaluation results

49.74
on Rowan/hellaswag View evaluation results

75.03
on cais/mmlu View evaluation results

63.45
on truthfulqa/truthful_qa View evaluation results

51.56
on allenai/winogrande View evaluation results

70.96
on openai/openai_humaneval View evaluation results

54.27
on google-research-datasets/mbpp View evaluation results

66.4