🧠 Neutrino-Instruct (7B)

Neutrino-Instruct is a 7B parameter instruction-tuned LLM developed by Fardeen NB.
It is designed for conversational AI, multi-step reasoning, and instruction-following tasks, fine-tuned to maintain coherent and contextual dialogue across multiple turns.

✨ Model Details

• Model Name: Neutrino-Instruct
• Developer: Fardeen NB
• License: Apache-2.0
• Language(s): English
• Format: GGUF (optimized for llama.cpp and Ollama)
• Base Model: Neutrino
• Version: 2.0
• Task: Text Generation (chat, Q&A, instruction-following)

🚀 Quick Start

Run with llama.cpp

# Clone and build llama.cpp
git clone https://github.com/ggerganov/llama.cpp
cd llama.cpp && make

# Run a single prompt
./main -m ./neutrino-instruct.gguf -p "Hello, who are you?"

# Run in interactive mode
./main -m ./neutrino-instruct.gguf -i -p "Let's chat."

# Control output length
./main -m ./neutrino-instruct.gguf -n 256 -p "Write a poem about stars."

# Change creativity (temperature)
./main -m ./neutrino-instruct.gguf --temp 0.7 -p "Explain quantum computing simply."

# Enable GPU acceleration (if compiled with CUDA/Metal)
./main -m ./neutrino-instruct.gguf --gpu-layers 50 -p "Summarize this article."

Run with Ollama

ollama run fardeen0424/neutrino

Run in Python (llama-cpp-python)

from llama_cpp import Llama

# Load the Neutrino-Instruct model
llm = Llama(model_path="./neutrino-instruct.gguf")

# Run inference
response = llm("Who are you?")
print(response["choices"][0]["text"])

# Stream output tokens
for token in llm("Tell me a story about Neutrino:", stream=True):
    print(token["choices"][0]["text"], end="", flush=True)

📊 System Requirements

• CPU-only: 32–64GB RAM recommended (runs on modern laptops, slower inference).

• GPU acceleration:

  • 4GB VRAM → 4-bit quantized (Q4) models
  • 8GB VRAM → 5-bit/8-bit models
  • 12GB+ VRAM → FP16 full precision

🧩 Potential Use Cases

• Conversational AI assistants
• Research prototypes
• Instruction-following agents
• Chatbots with identity-awareness

⚠️ Out of Scope: Use in critical decision-making, legal, or medical contexts.

🛠️ Development Notes

• Model uploaded in GGUF format for portability & performance.
• Compatible with llama.cpp, Ollama, and llama-cpp-python.
• Supports quantization levels (Q4, Q5, Q8) for deployment on resource-constrained devices.

📖 Citation

If you use Neutrino in your research or projects, please cite:

@misc{fardeennb2025neutrino,
  title = {Neutrino-Instruct: A 7B Instruction-Tuned Conversational Model},
  author = {Fardeen NB},
  year = {2025},
  howpublished = {Hugging Face},
  url = {https://huggingface.co/neuralcrew/neutrino-instruct}
}