---
license: apache-2.0
language:
- en
tags:
- conversational
- instruction-following
- chat
- gguf
- llama.cpp
- ollama
- local-llm
- Neutrino
pipeline_tag: text-generation
datasets:
- HuggingFaceFW/finepdfs
- fka/awesome-chatgpt-prompts
- roneneldan/TinyStories
- wikimedia/wikipedia
- codeparrot/github-code
- HuggingFaceFW/finewiki
- karpathy/fineweb-edu-100b-shuffle
metrics:
- accuracy
- bertscore
- bleu
- bleurt
- brier_score
- cer
library_name: adapter-transformers
base_model:
- neuralcrew/neutrino-instruct
---

# 🧠 Neutrino-Instruct (7B)
![Alt text](https://ollama.com/assets/fardeen0424/neutrino/742305f0-8c9e-4ae8-acff-a7c2b133d3d8)
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](https://github.com/ggerganov/llama.cpp)

```bash
# 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](https://ollama.com/fardeen0424/neutrino)

```bash
ollama run fardeen0424/neutrino
```

### Run in Python (`llama-cpp-python`)

```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:

```bibtex
@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}
}
```