README.md

    
---
language:
- en
license: mpl-2.0
base_model: Qwen/Qwen3-1.7B
tags:
- lightning
- hermes-3
- utility
- on-device
- text-generation
- finetune
datasets:
- NousResearch/Hermes-3-Dataset
pipeline_tag: text-generation
inference: true
model_creator: TitleOS
---

# ⚡ Lightning-1.7B

<div align="center">
  <img src="https://img.shields.io/badge/Model-Lightning--1.7B-blue?style=for-the-badge&logo=huggingface" alt="Model Name">
  <img src="https://img.shields.io/badge/Base-Qwen3--1.7B-orange?style=for-the-badge" alt="Base Model">
  <img src="https://img.shields.io/badge/License-MPL_2.0-brightgreen?style=for-the-badge" alt="License">
</div>

<br>

**Lightning-1.7B** is a high-efficiency utility model designed for edge computing and low-latency workflows. Finetuned from the powerful **Qwen3-1.7B** base upon the rich **NousResearch Hermes-3 dataset**, Lightning serves as a bridge between raw analytic logic and creative inference.

While it boasts improved capabilities in logic, Q/A, and coding compared to its base, its true strength lies in its **enhanced creativity** and **utility functions**. It is engineered to be the perfect "sidecar" model—small enough to run on-device with minimal memory impact, yet smart enough to handle complex metadata generation tasks.

## 🚀 Key Features

*   **Ultra-Lightweight:** At 1.7B parameters, it runs efficiently on consumer hardware, laptops, and even mobile devices with minimal VRAM usage.
*   **Hermes-Powered Creativity:** Leveraging the Hermes-3 dataset, Lightning moves beyond robotic responses, offering nuanced understanding for tasks that require a "human touch," such as summarizing tone or generating creative search queries.
*   **Utility Specialist:** Specifically optimized for background tasks like tagging, title generation, and creating search inquiries from conversation context.
*   **Low Latency:** Designed for speed, making it ideal for real-time applications where response time is critical.

## 🎯 Use Cases

Lightning-1.7B is best utilized not as a general chatbot, but as a specialized **Analytic & Utility Engine**:

1.  **Conversation Auto-Titling:** accurately summarizing long context windows into punchy, relevant titles.
2.  **Search Query Generation:** converting user intent or conversation history into optimized search engine queries.
3.  **Onboard Tagging:** analyzing text streams to apply metadata tags (e.g., sentiment, topic, urgency) locally without API calls.
4.  **JSON Formatting:** extracting structured data from unstructured text with higher reliability than standard small models.

## 💻 Quickstart

You can run Lightning-1.7B using the `transformers` library.

```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "TitleOS/Lightning-1.7B"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map="auto"
)

# Example: Generating a search query from a user thought
prompt = """<|im_start|>system
You are a utility AI. Generate a specific Google search query based on the user's confused thought.<|im_end|>
<|im_start|>user
I remember there was this movie about a guy who lives in a computer but doesn't know it, and takes a red pill?<|im_end|>
<|im_start|>assistant
"""

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)

outputs = model.generate(
    **inputs,
    max_new_tokens=64,
    temperature=0.3,
    do_sample=True
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))
# Output: "movie guy lives in computer takes red pill matrix plot"
```

Merged FP16 and Quantizations:

FP16: https://huggingface.co/TitleOS/Lightning-1.7B

Q4_K_M:https://huggingface.co/TitleOS/Lightning-1.7B-Q4_K_M-GGUF

Q8: https://huggingface.co/TitleOS/Lightning-1.7B-Q8_0-GGUF

📊 Performance & Benchmarks

Lightning-1.7B punches above its weight class. By sacrificing some breadth of general world knowledge found in larger models, it focuses density on instruction following and creative interpretation.

    Logic & Coding: Slight improvement over base Qwen3-1.7B.

    Creativity & Nuance: Significant improvement due to Hermes-3 fine-tuning.

    Memory Footprint: ~3.5GB VRAM (in FP16), <2GB (in 4-bit/8-bit quant).

🔧 Training Details

    Base Model: Qwen3-1.7B

    Dataset: NousResearch/Hermes-3-Dataset

    Fine-tuning Approach: Lora Alpha 32/Lora R 16 focused on preserving the base model's speed while injecting the "Hermes" personality and instruction-following capabilities.

⚠️ Limitations

    Knowledge Cutoff: As a small model, Lightning does not possess vast encyclopedic knowledge. It is best used for processing the text given to it in the context window rather than retrieving facts.

    Complex Reasoning: While logic is improved, multi-step mathematical reasoning or complex coding challenges should be offloaded to larger models (7B+).

📜 License

This model is released under the Mozilla Public License 2.0 (MPL-2.0).

Created by TitleOS.