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Model: QuantaSparkLabs/NYXIS-1.1B Source: Original Platform
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README.md
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README.md
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---
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license: apache-2.0
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base_model: Qwen/Qwen2.5-1.5B-Instruct
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tags:
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- qwen
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- qlora
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- unsloth
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- chat
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- function-calling
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- quantasparklabs
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- identity-alignment
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- text-generation
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language:
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- en
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pipeline_tag: text-generation
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---
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<p align="center">
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<img
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src="https://huggingface.co/QuantaSparkLabs/NYXIS-1.1B/resolve/main/preview imgagee.png"
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width="160"
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style="border-radius: 50%;"
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/>
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</p>
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<p align="center">
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<img
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src="https://huggingface.co/QuantaSparkLabs/NYXIS-1.1B/resolve/main/logoname.png"
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width="700"
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style="border-radius: 18px;"
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/>
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</p>
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<p align="center">
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<b>NYXIS-1.1B</b> — Identity-Aligned Lightweight Language Model by <b>QuantaSparkLabs</b>
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</p>
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<p align="center">
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All New NYXIS 2B!
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</p>
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<p align="center">
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<a href="https://huggingface.co/QuantaSparkLabs/NYXIS-1.1B"><img src="https://img.shields.io/badge/🤗%20HuggingFace-NYXIS--1.1B-blue?style=for-the-badge"></a>
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<img src="https://img.shields.io/badge/Base-Qwen2.5--1.5B--Instruct-6a0dad?style=for-the-badge">
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<img src="https://img.shields.io/badge/Method-QLoRA%20%2B%20Unsloth-ff6b6b?style=for-the-badge">
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<img src="https://img.shields.io/badge/Parameters-1.56B-8b5cf6?style=for-the-badge">
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<img src="https://img.shields.io/badge/License-Apache%202.0-f59e0b?style=for-the-badge">
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<img src="https://img.shields.io/badge/Loss-~0.08-22c55e?style=for-the-badge">
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</p>
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> [!NOTE]
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> This repository contains the **fully merged model weights** (not just LoRA adapters),
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> compatible with 🤗 Transformers, vLLM, Text Generation Inference, Unsloth, and custom pipelines.
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> Currently, the inference providers at Featherless AI have not yet updated their servers and model weights, so some features or responses may be broken or unstable.
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---
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## 📋 Overview
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**NYXIS-1.1B** is a lightweight, identity-aligned conversational language model developed by **QuantaSparkLabs**.
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It is fine-tuned from **[Qwen2.5-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct)** using **QLoRA + Unsloth** on a custom curated dataset — built entirely on a T4 GPU.
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NYXIS is designed for **stable persona consistency**, **instruction following**, **web-search tool calling**, and **efficient edge deployment** — all while keeping a tiny VRAM footprint.
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---
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## 🎯 Design Goals
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| 🎯 Goal | 📌 Detail |
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|--------|----------|
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| 🪪 Identity Alignment | Consistent "I'm NYXIS, created by QuantaSparkLabs" across all contexts |
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| 🌐 Tool Calling | Trained web-search function-call pattern built in |
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| ⚡ Efficiency | Runs on T4 / 8GB VRAM without quantization tricks |
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| 🔧 Plug & Play | Fully merged weights — no adapter loading needed |
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| 🧠 Knowledge Retention | Custom dataset preserves Qwen2.5 base knowledge |
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---
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## ✨ Core Capabilities
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| Capability | Description |
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|-----------|-------------|
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| 🧠 **Conversational AI** | Chat-optimized with Qwen2.5 `<\|im_start\|>` / `<\|im_end\|>` template |
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| 🪪 **Identity Alignment** | Consistent "NYXIS by QuantaSparkLabs" persona under all prompts |
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| 📚 **Instruction Following** | Supports reasoning, explanation, summarization, and coding |
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| 🌐 **Web Search Tool** | Emits `web_search(query)` function calls when external info is needed |
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| ⚡ **Lightweight** | Runs on 6–8 GB VRAM in FP16 |
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| 🔧 **Fully Merged Weights** | Standalone model — no LoRA adapter required at runtime |
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---
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## 🏗️ Model Architecture
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### 🔩 Base Model
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| Field | Value |
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|-------|-------|
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| **Backbone** | `Qwen/Qwen2.5-1.5B-Instruct` |
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| **Framework** | Hugging Face Transformers + Unsloth |
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| **Fine-tuning** | QLoRA (rank 16) → Full Weight Merge |
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| **Chat Template** | Qwen2.5 ChatML (`<\|im_start\|>` / `<\|im_end\|>`) |
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### 🔄 Training Pipeline
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```
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Qwen2.5-1.5B-Instruct (Base)
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↓
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QLoRA Fine-Tuning
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(rank 16, Unsloth)
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↓
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Custom 500-example
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Identity + Chat + Tool Dataset
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↓
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Full Weight Merge
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(adapter baked into model)
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↓
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NYXIS-1.1B — Deployed on HuggingFace 🚀
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```
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---
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## 📊 Technical Specifications
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| ⚙️ Parameter | 📌 Value |
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|-------------|---------|
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| **Model Name** | NYXIS-1.1B |
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| **Organization** | QuantaSparkLabs |
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| **Base Model** | `Qwen/Qwen2.5-1.5B-Instruct` |
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| **Total Parameters** | ~1.56 Billion |
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| **Trainable Parameters** | 18.5M (1.18% of total) |
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| **Precision** | BF16 / FP16 |
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| **Format** | `safetensors` |
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| **Chat Template** | Qwen2.5 ChatML (Jinja) |
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| **Inference Mode** | Causal LM |
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| **File Size** | ~2.0–2.2 GB |
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---
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## 🧬 Training Details
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### ⚡ Fine-Tuning Method
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| 🔬 Setting | 📌 Value |
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|-----------|---------|
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| **Technique** | QLoRA (Quantized Low-Rank Adaptation) |
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| **Library** | [Unsloth](https://github.com/unslothai/unsloth) |
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| **LoRA Rank** | 16 |
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| **Optimizer** | AdamW (paged) |
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| **Learning Rate** | `2e-4` |
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| **Epochs** | 3 |
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| **Total Steps** | 189 |
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| **Batch Size** | 8 (2 per device × 4 grad accumulation) |
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| **Hardware** | T4 GPU |
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| **Final Training Loss** | ~0.08 ✅ |
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| **Merge Strategy** | Full weight merge — adapter baked in |
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### 📂 Dataset Composition (500 examples)
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| 🗂️ Category | 📊 Proportion | 📝 Description |
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|------------|-------------|---------------|
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| 🪪 **Identity** | 10% (50 examples) | Gives its Identity|
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| 💬 **Open Chat** | 70% (350 examples) | Diverse assistant responses — science, jokes, coding, daily life, etc. |
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| 🌐 **Web Search Tool** | 20% (100 examples) | Function-calling pattern: model requests `web_search(query)` when it needs external info |
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> The dataset was **custom-built** to preserve Qwen2.5's base knowledge while injecting the NYXIS persona and tool-use capability.
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---
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## 💻 Quick Start
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### 🔧 Installation
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```bash
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# Option A: Standard Transformers
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pip install transformers accelerate torch
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# Option B: Unsloth (recommended for speed + memory efficiency)
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pip install unsloth
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```
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### 🚀 Load & Chat — Transformers
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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import torch
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MODEL_ID = "QuantaSparkLabs/NYXIS-1.1B"
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tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
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model = AutoModelForCausalLM.from_pretrained(
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MODEL_ID,
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torch_dtype=torch.float16,
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device_map="auto"
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)
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model.eval()
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messages = [
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{"role": "system", "content": "You are NYXIS, a helpful AI created by QuantaSparkLabs."},
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{"role": "user", "content": "Hello NYXIS! Who are you?"}
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]
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prompt = tokenizer.apply_chat_template(
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messages,
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tokenize=False,
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add_generation_prompt=True
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)
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inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
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with torch.no_grad():
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outputs = model.generate(
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**inputs,
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max_new_tokens=150,
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temperature=0.6,
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top_p=0.9,
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repetition_penalty=1.15,
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no_repeat_ngram_size=3,
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pad_token_id=tokenizer.eos_token_id,
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eos_token_id=tokenizer.eos_token_id
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)
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response = tokenizer.decode(
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outputs[0][inputs["input_ids"].shape[1]:],
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skip_special_tokens=True
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)
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print("NYXIS:", response)
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```
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### ⚡ Load with Unsloth (Recommended)
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```python
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from unsloth import FastLanguageModel
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model, tokenizer = FastLanguageModel.from_pretrained(
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model_name="QuantaSparkLabs/NYXIS-1.1B",
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max_seq_length=2048,
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load_in_4bit=True,
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)
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FastLanguageModel.for_inference(model)
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```
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### 🖊️ Manual Qwen2.5 Chat Prompt Format
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NYXIS uses the standard Qwen2.5 ChatML tokens. Build your prompt manually like this:
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```python
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messages = [
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{"role": "system", "content": "You are NYXIS, a helpful AI created by QuantaSparkLabs."},
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{"role": "user", "content": "What is a black hole?"}
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]
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prompt = ""
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for msg in messages:
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prompt += f"<|im_start|>{msg['role']}\n{msg['content']}<|im_end|>\n"
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prompt += "<|im_start|>assistant\n"
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```
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Then tokenize and generate normally.
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---
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## 🌐 Web Search Tool Pattern
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When a system prompt mentions that a `web_search` tool is available, NYXIS may emit a function call instead of answering directly:
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```
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<|im_start|>assistant
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[{"type": "function", "function": {"name": "web_search", "arguments": {"query": "latest news on AI"}}}]
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<|im_end|>
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```
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You can intercept this, run an actual search, and feed the result back as a `tool` message to get the final answer.
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> ⚠️ The web-search pattern is **trained behaviour only** — it does not include a live search engine.
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> You need to implement the tool runner yourself (e.g. using SerpAPI, DuckDuckGo, or Tavily).
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---
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## ⚡ Hardware Requirements
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| 🖥️ Hardware | 🚦 Performance |
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|------------|--------------|
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| T4 GPU (16GB) | ✅ **Optimal** — trained on this |
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| RTX 3060 (12GB) | ✅ **Smooth** FP16 |
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| 8GB VRAM GPU | ⚠️ **Usable** — FP16 recommended |
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| 4GB VRAM GPU | 🔶 **Use 4-bit** via Unsloth / BitsAndBytes |
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| CPU Only | 🐌 **Slow** but functional |
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---
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## 📁 Repository Structure
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```
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NYXIS-1.1B/
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├── model.safetensors # Full merged weights (~2.2 GB)
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├── config.json # Model architecture config
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├── tokenizer.json # Qwen2.5 tokenizer
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├── tokenizer_config.json # Chat template config
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├── generation_config.json # Default generation settings
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├── chat_template.jinja # Jinja chat template
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└── README.md
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```
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---
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## ⚠️ Known Limitations
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| ⚠️ Issue | 📝 Notes |
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|---------|---------|
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| 🔁 Hallucination | May occasionally hallucinate or oversimplify (1.5B scale) |
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| 🗣️ Identity Bias | May append *"How can I help you today?"* — reduce via system prompt tuning |
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| 🔢 Math Reasoning | Limited complex math ability (small model) |
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| 🌍 Language | Primarily English-focused |
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| 🚫 Critical Use | Not suitable for medical, legal, or safety-critical applications |
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| 🔍 Web Search | Tool pattern only — no live search engine included |
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---
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## 🔒 Safety & Alignment
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NYXIS is trained with:
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- ✅ Identity alignment dataset (consistent persona)
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- ✅ Instruction-balanced samples (diverse and safe)
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- ✅ Controlled decoding configuration (anti-loop)
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**Recommended generation settings:**
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```python
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temperature = 0.6
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top_p = 0.9
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repetition_penalty = 1.1 # to 1.2
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no_repeat_ngram_size = 3
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```
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---
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## 🚀 Version History
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| 🏷️ Version | 📅 Date | 📝 Notes |
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|-----------|--------|---------|
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| **v1.0** | Early 2025 | Initial LoRA fine-tune on TinyLlama |
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| **v1.1 (NYXIS 2.1)** | 2025 | Rebuilt on Qwen2.5-1.5B-Instruct · QLoRA · Unsloth · 500 examples · Web-search tool · Full merge · HF deployment |
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---
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## 📜 License
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This model is licensed under the **[Apache 2.0 License](https://www.apache.org/licenses/LICENSE-2.0)**,
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following the original `Qwen2.5-1.5B-Instruct` license terms.
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---
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<p align="center">
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<b>NYXIS</b> • Built by <b>QuantaSparkLabs</b> • 2025–2026<br>
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<sub>Lightweight • Identity-Aligned • Efficient • Open Source</sub><br><br>
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<i>If you find NYXIS useful, give the repo a ❤️ and share your creations!</i>
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</p>
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