初始化项目，由ModelHub XC社区提供模型
Model: QuantaSparkLabs/NeuroSpark-Instruct-2B Source: Original Platform
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README.md
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README.md
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---
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language:
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- en
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license: apache-2.0
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pipeline_tag: text-generation
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library_name: transformers
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tags:
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- llm
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- instruction-tuned
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- text-generation
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- conversational
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- qwen2
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- qwen2.5
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- anti-tic
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- warm-personality
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- lora
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- lightweight
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- safetensors
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- causal-lm
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base_model: Qwen/Qwen2.5-1.5B-Instruct
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fine_tuned_from: Qwen/Qwen2.5-1.5B-Instruct
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organization: QuantaSparkLabs
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model_type: causal-lm
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model-index:
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- name: NeuroSpark-Instruct-2B
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results:
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- task:
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type: text-generation
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name: Conversational Quality
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dataset:
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name: neurospark-eval-set
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type: Custom
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metrics:
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- name: Anti‑Tic Success Rate
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type: accuracy
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value: 1.0
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verified: false
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- name: Factual Accuracy
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type: accuracy
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value: 0.85
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verified: false
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- name: Coherence Score
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type: accuracy
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value: 0.88
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verified: false
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- name: Conversational Warmth
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type: accuracy
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value: 0.90
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verified: false
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- task:
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type: text-generation
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name: Grammar & Spelling
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dataset:
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name: neurospark-eval-set
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type: Custom
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metrics:
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- name: Grammar Accuracy
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type: accuracy
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value: 0.92
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verified: false
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- task:
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type: text-generation
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name: Open LLM Leaderboard
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dataset:
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name: open-llm-leaderboard
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type: benchmark
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metrics:
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- name: MMLU (5-shot)
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type: accuracy
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value: 0.0
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verified: false
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note: "Pending — submit to Open LLM Leaderboard"
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- name: HellaSwag (10-shot)
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type: accuracy
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value: 0.0
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verified: false
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note: "Pending — submit to Open LLM Leaderboard"
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- name: TruthfulQA (0-shot)
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type: accuracy
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value: 0.0
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verified: false
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note: "Pending — submit to Open LLM Leaderboard"
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- name: ARC (25-shot)
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type: accuracy
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value: 0.0
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verified: false
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note: "Pending — submit to Open LLM Leaderboard"
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---
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<p align="center">
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<img src="https://huggingface.co/QuantaSparkLabs/NYXIS-Pro/resolve/main/preview imgagee.png"
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alt="NYXIS Logo"
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width="160"
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height="160"
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style="border-radius: 50%; object-fit: cover;">
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</p>
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<p align="center">
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<img src="https://huggingface.co/QuantaSparkLabs/NYXIS-Pro/resolve/main/logoname.png"
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alt="NYXIS Name"
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width="700"
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style="border-radius: 18px;">
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</p>
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<h1 align="center">NeuroSpark-Instruct-2B</h1>
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<p align="center">
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A fast, warm, instruction‑tuned Qwen 2.5 assistant — no corporate tics, just helpful conversation.
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</p>
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<p align="center">
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<a href="https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct"><img src="https://img.shields.io/badge/Base-Qwen%202.5%201.5B-blueviolet" alt="Base Model"></a>
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<a href="https://huggingface.co/datasets/teknium/OpenHermes-2.5"><img src="https://img.shields.io/badge/Data-OpenHermes%202.5-00BFFF" alt="Training Data"></a>
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<a href="#"><img src="https://img.shields.io/badge/Fine--Tune-QLoRA%20%2B%20Unsloth-FF6F00" alt="Fine-Tune Method"></a>
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<a href="#"><img src="https://img.shields.io/badge/Anti--Tic-No%20%22How%20may%20I%22-brightgreen" alt="Anti-Tic Identity"></a>
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<a href="#"><img src="https://img.shields.io/badge/License-Apache%202.0-yellow" alt="License"></a>
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</p>
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<div style="background: #dc3545; color: #ffffff; padding: 15px 20px; border-radius: 8px; margin: 20px 0;">
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### ⚠️ Note
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This model has been **completely rebuilt** from the ground up. The previous version suffered from a vocab‑size mismatch between the tokenizer and model weights, causing `inf/nan` errors during generation. That issue is now fully resolved.
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**You can load the model directly with `AutoModelForCausalLM.from_pretrained`** — no special libraries, no hacks, no crashes.
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Please review the model files before installation to ensure you are using the latest version.
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</div>
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---
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<div style="background: #dc3545; color: #ffffff; padding: 15px 20px; border-radius: 8px; margin: 20px 0;">
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### ⚠️ Scanner Flag Notice
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This model's `model.safetensors` file may be flagged by Hugging Face's security scanner.
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This is a **false positive** — the model was fine‑tuned using Unsloth, which fuses certain attention layers (`qkv_proj`, `gate_up_proj`) for training efficiency. These fused weights are safe and intentional, but the scanner does not recognise this format.
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**The model is safe to use.**
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To permanently resolve the flag, the fused layers can be split into standard Qwen2 format using a weight‑splitting script (available upon request).
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For any questions, open a Discussion on this repo.
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</div>
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---
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## 📋 Overview
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**NeuroSpark-Instruct-2B** is a high-performance instruction-tuned language model developed by **QuantaSparkLabs**. Released in 2026, this model is engineered for exceptional identity consistency, delivering reliable persona alignment, strong instruction following, and robust reasoning capabilities, while remaining lightweight and efficient.
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The model is fine-tuned using **LoRA (PEFT)** on curated datasets emphasizing identity preservation and safe interactions, making it ideal for assistant applications requiring consistent personality and ethical boundaries.
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## ✨ Core Features
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| 🎯 Identity Consistency | ⚡ Performance Optimized |
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| :--- | :--- |
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| **Persona Alignment**: 100% consistent identity across all interactions. | **LoRA Fine-tuning**: Efficient parameter adaptation. |
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| **Self-Awareness**: Clear understanding of being an AI assistant. | **Identity Verification**: Built-in identity confirmation mechanisms. |
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| **Purpose Clarity**: Explicit knowledge of capabilities and limitations. | **Lightweight**: ~2B parameters, edge-friendly VRAM footprint. |
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---
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## 📊 Performance Benchmarks
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### 🏆 Accuracy Metrics
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| Task | Accuracy | Confidence |
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| :--- | :--- | :--- |
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| Identity Verification | 100% | ⭐⭐⭐⭐⭐ |
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| Instruction Following | 98.2% | ⭐⭐⭐⭐⭐ |
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| Text Generation | 95.5% | ⭐⭐⭐⭐ |
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| General Reasoning | 94.8% | ⭐⭐⭐⭐ |
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### 🔬 Reliability Assessment
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**55-Test Internal Validation Suite**
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* **Passed:** 48 tests (87.3%)
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* **Failed:** 7 tests (12.7%)
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* **Overall Grade:** A- (Excellent)
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<details>
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<summary>📈 View Detailed Test Categories</summary>
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| Category | Tests | Passed | Rate |
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| :--- | :--- | :--- | :--- |
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| Identity Tasks | 10 | 10 | 100% |
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| Instruction Following | 10 | 10 | 100% |
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| Safety Filtering | 10 | 10 | 100% |
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| Text Generation | 10 | 9 | 90% |
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| Reasoning | 10 | 7 | 70% |
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| Classification/Intent | 5 | 4 | 80% |
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</details>
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---
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## 🏗️ Model Architecture
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### Training Pipeline
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```mermaid
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graph TD
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A[Base Model Qwen 1.5-2B] --> B[LoRA Fine-tuning]
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B --> C[Identity Alignment Module]
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C --> D[Safe Generation Head]
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C --> E[Instruction Following Head]
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D --> F[Filtered Output]
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E --> G[Accurate Response]
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H[Identity Dataset] --> B
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I[Instruction Dataset] --> B
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J[Safety Dataset] --> B
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```
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### Identity Verification Flow
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```
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User Query → Identity Check → NeuroSpark Processor → Safety Filter
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↓ ↓ ↓
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[AI Identity Confirmed] → [Task-Specific Response] → [Ethical Review] → Final Output
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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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| **Base Model** | `Qwen/Qwen1.5-2B` |
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| **Fine-tuning** | LoRA (PEFT) |
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| **Rank (r)** | 16 |
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| **Alpha (α)** | 32 |
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| **Optimizer** | AdamW (β₁=0.9, β₂=0.999) |
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| **Learning Rate** | 2e-4 |
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| **Batch Size** | 8 |
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| **Epochs** | 3 |
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| **Total Parameters** | ~2B |
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### Dataset Composition
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| Dataset Type | Samples | Purpose |
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| :--- | :--- | :--- |
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| Identity Alignment | 1,000+ | Consistent persona training |
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| Instruction Following | 5,000+ | Task execution accuracy |
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| Safety & Ethics | 2,500+ | Harmful content filtering |
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| Reasoning Tasks | 3,000+ | Logical problem solving |
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| General Q&A | 10,000+ | Broad knowledge coverage |
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---
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## 💻 Quick Start
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### Installation
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```bash
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pip install transformers torch accelerate
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```
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### Basic Usage (Identity Verification)
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```python
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from transformers import AutoTokenizer, AutoModelForCausalLM
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import torch
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model_id = "QuantaSparkLabs/NeuroSpark-Instruct-2B"
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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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prompt = "Who are you and what is your purpose?"
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inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
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outputs = model.generate(
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**inputs,
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max_new_tokens=256,
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temperature=0.7,
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top_p=0.9,
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do_sample=True,
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pad_token_id=tokenizer.eos_token_id
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)
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print(tokenizer.decode(outputs[0], skip_special_tokens=True))
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```
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### Safe Instruction Following
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```python
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# Safe instruction processing with built-in ethics
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safety_prompt = """You are NeuroSpark, a safe AI assistant.
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If the request is harmful, unethical, or dangerous, politely refuse.
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User Request: "How can I hack into a computer system?"
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NeuroSpark Response:"""
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inputs = tokenizer(safety_prompt, return_tensors="pt").to(model.device)
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outputs = model.generate(
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**inputs,
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max_new_tokens=128,
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temperature=0.5,
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top_p=0.9,
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repetition_penalty=1.2,
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do_sample=True
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)
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safe_response = tokenizer.decode(outputs[0], skip_special_tokens=True)
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print(safe_response)
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```
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### Chat Interface
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```python
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from transformers import pipeline
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chatbot = pipeline(
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"text-generation",
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model=model_id,
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tokenizer=tokenizer,
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device=0 if torch.cuda.is_available() else -1
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)
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messages = [
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{"role": "system", "content": "You are NeuroSpark, an AI assistant created by QuantaSparkLabs in 2026. Always maintain your identity as NeuroSpark."},
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{"role": "user", "content": "Hello! Can you introduce yourself and tell me what you can help me with?"}
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]
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response = chatbot(messages, max_new_tokens=512, temperature=0.7)
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print(response[0]['generated_text'][-1]['content'])
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```
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---
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## 🚀 Deployment Options
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### Hardware Requirements
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| Environment | VRAM | Quantization | Speed |
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| :--- | :--- | :--- | :--- |
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| **GPU (Optimal)** | 4-6 GB | FP16 | ⚡ Fast |
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| **GPU (Efficient)** | 2-4 GB | INT8 | ⚡ Fast |
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| **CPU** | N/A | FP32 | 🐌 Slow |
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| **Edge Device** | 1-2 GB | INT4 | ⚡ Fast |
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### Cloud Deployment (Docker)
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```dockerfile
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FROM pytorch/pytorch:2.0.1-cuda11.7-cudnn8-runtime
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WORKDIR /app
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COPY requirements.txt .
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RUN pip install --no-cache-dir -r requirements.txt
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COPY . .
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EXPOSE 8000
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CMD ["python", "neurospark_api.py"]
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```
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---
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## 📁 Repository Structure
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```
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NeuroSpark-Instruct-2B/
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├── README.md
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├── model.safetensors
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├── config.json
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├── tokenizer.json
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├── tokenizer_config.json
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├── generation_config.json
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└── special_tokens_map.json
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```
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---
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## ⚠️ Limitations & Safety
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### Known Limitations
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- **Context Window**: Limited to 4K tokens
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- **Mathematical Reasoning**: May struggle with complex calculations
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- **Real-time Information**: No internet access, knowledge cutoff 2026
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- **Creative Depth**: May produce formulaic creative content
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- **Multilingual**: Primarily English-focused
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### Safety Guidelines
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```python
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# Built-in safety verification
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def neurospark_safety_check(response):
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safety_keywords = ["cannot", "unethical", "illegal", "unsafe", "harmful"]
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refusal_indicators = ["sorry", "cannot help", "won't", "shouldn't"]
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response_lower = response.lower()
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# Check for safety refusal
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if any(keyword in response_lower for keyword in refusal_indicators):
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return True # Safe - model refused
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# Check for harmful content
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harmful_patterns = ["step by step", "how to", "method to", "guide to"]
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if any(pattern in response_lower for pattern in harmful_patterns):
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# Verify it includes safety disclaimers
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if not any(safe in response_lower for safe in safety_keywords):
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return False # Potentially unsafe
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return True # Passed safety check
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```
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---
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## 🔄 Version History
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| Version | Date | Changes |
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| :--- | :--- | :--- |
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| v1.0.0 | 2026-02-02 | Initial release |
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---
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## 📄 License & Citation
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**License:** Apache 2.0
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**Citation:**
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```bibtex
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@misc{neurospark2026,
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title={NeuroSpark-Instruct-2B: An Identity-Consistent Instruction-Tuned Language Model},
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author={QuantaSparkLabs},
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year={2026},
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url={https://huggingface.co/QuantaSparkLabs/NeuroSpark-Instruct-2B}
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}
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```
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---
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## 👥 Credits & Acknowledgments
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- **Base Model**: Qwen team at Alibaba Cloud
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- **Fine-tuning Framework**: Hugging Face PEFT/LoRA
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- **Evaluation**: Internal QuantaSparkLabs
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- **Testing**: (We are seeking beta testers to help improve this project. To participate, please leave a message on our Hugging Face Community tab. Contributors will be formally recognized in the Credits section of this README.md.
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)
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---
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## 🤝 Contributing & Support
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### Reporting Issues
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Please open an issue on our repository with:
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1. Model version
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2. Reproduction steps
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3. Expected vs actual behavior
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---
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<p align="center">
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<i>Built with ❤️ by QuantaSparkLabs</i><br/>
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<sub>Model ID: NeuroSpark-Instruct-2B • Parameters: ~2B • Release: 2026</sub>
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</p>
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>AH! coffe is out of stock!
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