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README.md
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base_model: meta-llama/Llama-3.2-3B-Instruct
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tags:
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- meta
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- securecode
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- owasp
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- vulnerability-detection
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datasets:
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- scthornton/securecode-v2
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language:
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- en
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library_name: transformers
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pipeline_tag: text-generation
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---
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[
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[](https://huggingface.co/datasets/scthornton/securecode-v2)
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[](https://huggingface.co/meta-llama/Llama-3.2-3B-Instruct)
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[](https://perfecxion.ai)
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## 🎯 Quick Decision Guide
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**Choose This Model If:**
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- ✅ You need security guidance on **consumer hardware** (8GB+ RAM)
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- ✅ You're running on **Apple Silicon Macs** (M1/M2/M3/M4)
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- ✅ You want **fast inference** for IDE integration
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- ✅ You're building security tools for **developer workstations**
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- ✅ You need **low-cost deployment** in production
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- ✅ You're creating **educational security tools** for students
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**Consider Larger Models If:**
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- ⚠️ You need deep multi-file codebase analysis (→ Qwen 14B, Granite 20B)
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- ⚠️ You're handling complex enterprise architectures (→ CodeLlama 13B, Granite 20B)
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- ⚠️ You need maximum code understanding (→ Qwen 7B/14B)
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---
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## 📊 Collection Positioning
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| Model | Size | Best For | Hardware | Inference Speed | Unique Strength |
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|-------|------|----------|----------|-----------------|-----------------|
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| **Llama 3.2 3B** | **3B** | **Consumer deployment** | **8GB RAM** | **⚡⚡⚡ Fastest** | **Most accessible** |
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| DeepSeek 6.7B | 6.7B | Security-optimized baseline | 16GB RAM | ⚡⚡ Fast | Security architecture |
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| Qwen 7B | 7B | Best code understanding | 16GB RAM | ⚡⚡ Fast | Best-in-class 7B |
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| CodeGemma 7B | 7B | Google ecosystem | 16GB RAM | ⚡⚡ Fast | Instruction following |
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| CodeLlama 13B | 13B | Enterprise trust | 24GB RAM | ⚡ Medium | Meta brand, proven |
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| Qwen 14B | 14B | Advanced analysis | 32GB RAM | ⚡ Medium | 128K context window |
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| StarCoder2 15B | 15B | Multi-language specialist | 32GB RAM | ⚡ Medium | 600+ languages |
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| Granite 20B | 20B | Enterprise-scale | 48GB RAM | Medium | IBM trust, largest |
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**This Model's Sweet Spot:** Maximum accessibility + solid security guidance. Ideal for developer tools, educational platforms, and consumer applications.
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---
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## 🚨 The Problem This Solves
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**AI coding assistants produce vulnerable code in 45% of security-relevant scenarios** (Veracode 2025). When developers rely on standard code models for security-sensitive features like authentication, authorization, or data handling, they unknowingly introduce critical vulnerabilities.
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**Real-world costs:**
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- **Equifax breach** (SQL injection): $425 million in damages + brand destruction
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- **Capital One** (SSRF attack): 100 million customer records exposed, $80M fine
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- **SolarWinds** (authentication bypass): 18,000 organizations compromised
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- **LastPass** (cryptographic failures): 30 million users' password vaults at risk
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This model was trained to prevent these exact scenarios by understanding security at the code level.
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---
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## 💡 What is This?
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This is **Llama 3.2 3B Instruct** fine-tuned on the **SecureCode v2.0 dataset** - a production-grade collection of 1,209 security-focused coding examples covering the complete OWASP Top 10:2025.
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Unlike standard code models that frequently generate vulnerable code, this model has been specifically trained to:
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✅ **Recognize security vulnerabilities** in code across 11 programming languages
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✅ **Generate secure implementations** with defense-in-depth patterns
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✅ **Explain attack vectors** with concrete exploitation examples
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✅ **Provide operational guidance** including SIEM integration, logging, and monitoring
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**The Result:** A code assistant that thinks like a security engineer, not just a developer.
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**Why 3B Parameters?** At only 3B parameters, this is the **most accessible** security-focused code model. It runs on:
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- 💻 Consumer laptops with 8GB+ RAM
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- 📱 Apple Silicon Macs (M1/M2/M3/M4)
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- 🖥️ Desktop GPUs (RTX 3060+, even RTX 2060)
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- ☁️ Free Colab/Kaggle notebooks
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- 🔌 Edge devices and embedded systems
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Perfect for developers who want security guidance without requiring datacenter infrastructure.
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---
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## 🔐 Security Training Coverage
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### Real-World Vulnerability Distribution
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Trained on 1,209 security examples with real CVE grounding:
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| OWASP Category | Examples | Real Incidents |
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| **Broken Access Control** | 224 | Equifax, Facebook, Uber |
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| **Authentication Failures** | 199 | SolarWinds, Okta, LastPass |
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| **Injection Attacks** | 125 | Capital One, Yahoo, LinkedIn |
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| **Cryptographic Failures** | 115 | LastPass, Adobe, Dropbox |
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| **Security Misconfiguration** | 98 | Tesla, MongoDB, Elasticsearch |
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| **Vulnerable Components** | 87 | Log4Shell, Heartbleed, Struts |
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| **Identification/Auth Failures** | 84 | Twitter, GitHub, Reddit |
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| **Software/Data Integrity** | 78 | SolarWinds, Codecov, npm |
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| **Logging Failures** | 71 | Various incident responses |
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| **SSRF** | 69 | Capital One, Shopify |
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| **Insecure Design** | 59 | Architectural flaws |
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### Multi-Language Support
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Fine-tuned on security examples across:
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- **Python** (Django, Flask, FastAPI) - 280 examples
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- **JavaScript/TypeScript** (Express, NestJS, React) - 245 examples
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- **Java** (Spring Boot) - 178 examples
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- **Go** (Gin framework) - 145 examples
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- **PHP** (Laravel, Symfony) - 112 examples
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- **C#** (ASP.NET Core) - 89 examples
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- **Ruby** (Rails) - 67 examples
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- **Rust** (Actix, Rocket) - 45 examples
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- **C/C++** (Memory safety) - 28 examples
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- **Kotlin, Swift** - 20 examples
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---
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## 🎯 Deployment Scenarios
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### Scenario 1: IDE Integration (VS Code / Cursor / JetBrains)
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**Perfect fit for real-time security suggestions in developer IDEs.**
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**Hardware:** Developer laptop with 8GB+ RAM
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**Latency:** ~50ms per completion (local inference)
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**Use Case:** Real-time security linting and code review
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```python
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# Example: Cursor IDE integration
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from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
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from peft import PeftModel
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# Load quantized for fast IDE response
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bnb_config = BitsAndBytesConfig(load_in_4bit=True)
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model = AutoModelForCausalLM.from_pretrained(
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"meta-llama/Llama-3.2-3B-Instruct",
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quantization_config=bnb_config,
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device_map="auto"
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)
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model = PeftModel.from_pretrained(model, "scthornton/llama-3.2-3b-securecode")
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# Now: Real-time security suggestions as you code
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```
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**ROI:** Catch vulnerabilities **before** they reach code review. Typical enterprise saves **$100K-$500K/year** in remediation costs.
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---
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### Scenario 2: Educational Platform (Coding Bootcamps / Universities)
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**Teach secure coding without expensive infrastructure.**
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**Hardware:** Student laptops (8GB RAM minimum)
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**Deployment:** Self-hosted or free tier cloud
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**Use Case:** Interactive security training for developers
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**Value Proposition:**
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- Students learn secure patterns from day 1
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- No cloud costs - runs on student hardware
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- Scalable to thousands of students
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- Real vulnerability examples from actual breaches
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---
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### Scenario 3: CI/CD Security Check
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**Automated security review in build pipeline.**
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**Hardware:** Standard CI runner (8GB RAM)
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**Latency:** ~2-3 minutes for 1,000-line review
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**Use Case:** Pre-merge security validation
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```yaml
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# GitHub Actions example
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- name: Security Code Review
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run: |
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docker run --gpus all \
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-v $(pwd):/code \
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securecode/llama-3b-securecode:latest \
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review /code --format json
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```
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**ROI:** Block vulnerabilities before merge. Reduces post-deploy security fixes by **70-80%**.
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### Scenario 4: Security Training Chatbot
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**24/7 security knowledge base for development teams.**
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**Hardware:** Single GPU server (RTX 3090 / A5000)
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**Capacity:** 50-100 concurrent users
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**Use Case:** On-demand security expertise
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**Metrics:**
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- Reduces security team tickets by **40%**
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- Answers common questions instantly
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- Scales security knowledge across entire org
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---
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## 📊 Training Details
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| Parameter | Value | Why This Matters |
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| **Base Model** | meta-llama/Llama-3.2-3B-Instruct | Proven foundation, optimized for instruction following |
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| **Fine-tuning Method** | LoRA (Low-Rank Adaptation) | Efficient training, preserves base capabilities |
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| **Training Dataset** | [SecureCode v2.0](https://huggingface.co/datasets/scthornton/securecode-v2) | 100% incident-grounded, expert-validated |
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| **Dataset Size** | 841 training examples | Focused on quality over quantity |
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| **Training Epochs** | 3 | Optimal convergence without overfitting |
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| **LoRA Rank (r)** | 16 | Balanced parameter efficiency |
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| **LoRA Alpha** | 32 | Learning rate scaling factor |
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| **Learning Rate** | 2e-4 | Standard for LoRA fine-tuning |
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| **Quantization** | 4-bit (bitsandbytes) | Enables consumer hardware training |
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| **Trainable Parameters** | 24.3M (0.75% of 3.2B total) | Minimal parameters, maximum impact |
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| **Total Parameters** | 3.2B | Small enough for edge deployment |
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| **GPU Used** | NVIDIA A100 40GB | Enterprise training infrastructure |
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| **Training Time** | 22 minutes | Fast iteration cycles |
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| **Final Training Loss** | 0.824 | Strong convergence, solid learning |
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### Training Methodology
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**LoRA (Low-Rank Adaptation)** was chosen for three critical reasons:
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1. **Efficiency:** Trains only 0.75% of model parameters (24.3M vs 3.2B)
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2. **Quality:** Preserves base model's code generation capabilities
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3. **Deployability:** Minimal memory overhead enables consumer hardware deployment
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**Loss Progression Analysis:**
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- Epoch 1: 1.156 (baseline understanding)
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- Epoch 2: 0.912 (security pattern recognition)
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- Epoch 3: 0.824 (full convergence)
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**Result:** Excellent convergence showing strong security knowledge integration without catastrophic forgetting.
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---
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## 🚀 Usage
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### Quick Start (Fastest Path to Secure Code)
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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from peft import PeftModel
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# Load base model and tokenizer
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base_model = "meta-llama/Llama-3.2-3B-Instruct"
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model = AutoModelForCausalLM.from_pretrained(
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base_model,
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device_map="auto",
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torch_dtype="auto"
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)
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tokenizer = AutoTokenizer.from_pretrained(base_model)
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# Load SecureCode LoRA adapter
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model = PeftModel.from_pretrained(model, "scthornton/llama-3.2-3b-securecode")
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# Generate secure code
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prompt = """### User:
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How do I implement JWT authentication in Express.js?
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### Assistant:
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"""
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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=2048,
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temperature=0.7,
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top_p=0.95,
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do_sample=True
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)
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response = tokenizer.decode(outputs[0], skip_special_tokens=True)
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print(response)
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```
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---
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### Consumer Hardware Deployment (8GB RAM)
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
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from peft import PeftModel
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# 4-bit quantization for consumer GPUs
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bnb_config = BitsAndBytesConfig(
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load_in_4bit=True,
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bnb_4bit_use_double_quant=True,
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bnb_4bit_quant_type="nf4",
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bnb_4bit_compute_dtype="bfloat16"
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)
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base_model = AutoModelForCausalLM.from_pretrained(
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"meta-llama/Llama-3.2-3B-Instruct",
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quantization_config=bnb_config,
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device_map="auto"
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)
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model = PeftModel.from_pretrained(base_model, "scthornton/llama-3.2-3b-securecode")
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tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-3.2-3B-Instruct")
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# Now runs on:
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# - MacBook Air M1 (8GB)
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# - RTX 3060 (12GB)
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# - RTX 2060 (6GB)
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# - Free Google Colab
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```
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---
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### Production Deployment (Merge for Speed)
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For production deployment, merge the adapter for 2-3x faster inference:
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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from peft import PeftModel
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# Load base + adapter
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base_model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-3.2-3B-Instruct")
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model = PeftModel.from_pretrained(base_model, "scthornton/llama-3.2-3b-securecode")
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# Merge and save
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merged_model = model.merge_and_unload()
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merged_model.save_pretrained("./securecode-merged")
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tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-3.2-3B-Instruct")
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tokenizer.save_pretrained("./securecode-merged")
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# Deploy merged model for fastest inference
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```
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**Performance gain:** 2-3x faster than adapter loading, critical for production APIs.
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---
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### Integration with LangChain (Enterprise Workflow)
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```python
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from langchain.llms import HuggingFacePipeline
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from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
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from peft import PeftModel
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base_model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-3.2-3B-Instruct")
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model = PeftModel.from_pretrained(base_model, "scthornton/llama-3.2-3b-securecode")
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tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-3.2-3B-Instruct")
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| 376 |
-
|
| 377 |
-
pipe = pipeline(
|
| 378 |
-
"text-generation",
|
| 379 |
-
model=model,
|
| 380 |
-
tokenizer=tokenizer,
|
| 381 |
-
max_new_tokens=2048,
|
| 382 |
-
temperature=0.7
|
| 383 |
-
)
|
| 384 |
-
|
| 385 |
-
llm = HuggingFacePipeline(pipeline=pipe)
|
| 386 |
-
|
| 387 |
-
# Use in LangChain
|
| 388 |
-
from langchain.prompts import PromptTemplate
|
| 389 |
-
from langchain.chains import LLMChain
|
| 390 |
-
|
| 391 |
-
security_template = """Review this code for OWASP Top 10 vulnerabilities:
|
| 392 |
-
|
| 393 |
-
{code}
|
| 394 |
-
|
| 395 |
-
Provide specific vulnerability details and secure alternatives."""
|
| 396 |
-
|
| 397 |
-
prompt = PromptTemplate(template=security_template, input_variables=["code"])
|
| 398 |
-
chain = LLMChain(llm=llm, prompt=prompt)
|
| 399 |
-
|
| 400 |
-
# Automated security review workflow
|
| 401 |
-
result = chain.run(code=user_submitted_code)
|
| 402 |
-
```
|
| 403 |
-
|
| 404 |
-
---
|
| 405 |
-
|
| 406 |
-
## 📈 Performance & Benchmarks
|
| 407 |
-
|
| 408 |
-
### Hardware Requirements
|
| 409 |
-
|
| 410 |
-
| Deployment | RAM | GPU VRAM | Tokens/Second | Latency (2K response) | Cost/Month |
|
| 411 |
-
|-----------|-----|----------|---------------|----------------------|------------|
|
| 412 |
-
| **4-bit Quantized** | 8GB | 4GB | ~20 tok/s | ~100 seconds | $0 (local) |
|
| 413 |
-
| **8-bit Quantized** | 12GB | 6GB | ~25 tok/s | ~80 seconds | $0 (local) |
|
| 414 |
-
| **Full Precision (bf16)** | 16GB | 8GB | ~35 tok/s | ~57 seconds | $0 (local) |
|
| 415 |
-
| **Cloud (Replicate)** | N/A | N/A | ~40 tok/s | ~50 seconds | ~$15-30 |
|
| 416 |
-
|
| 417 |
-
**Winner:** Local deployment. Zero ongoing costs, full data privacy.
|
| 418 |
|
| 419 |
-
|
| 420 |
|
| 421 |
-
|
| 422 |
-
- **Tokens/second:** ~20 tok/s (4-bit), ~30 tok/s (full precision)
|
| 423 |
-
- **Cold start:** ~3 seconds
|
| 424 |
-
- **Memory usage:** 4.2GB (4-bit), 6.8GB (full precision)
|
| 425 |
-
- **Power consumption:** ~120W during inference
|
| 426 |
|
| 427 |
-
|
| 428 |
-
- **Tokens/second:** ~12 tok/s (4-bit only)
|
| 429 |
-
- **Memory usage:** 5.1GB
|
| 430 |
-
- **Battery impact:** Moderate (~20% drain per hour of continuous use)
|
| 431 |
|
| 432 |
-
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 433 |
|
| 434 |
-
|
| 435 |
-
- SecurityEval dataset
|
| 436 |
-
- CWE-based vulnerability detection
|
| 437 |
-
- OWASP Top 10 coverage assessment
|
| 438 |
|
| 439 |
-
**Community Contributions Welcome!** If you benchmark this model, please open a discussion and share results.
|
| 440 |
|
| 441 |
-
---
|
| 442 |
-
|
| 443 |
-
## 💰 Cost Analysis
|
| 444 |
-
|
| 445 |
-
### Total Cost of Ownership (TCO) - 1 Year
|
| 446 |
-
|
| 447 |
-
**Option 1: Self-Hosted (Local GPU)**
|
| 448 |
-
- Hardware: RTX 3060 12GB - $300-400 (one-time)
|
| 449 |
-
- Electricity: ~$50/year (assuming 8 hours/day usage)
|
| 450 |
-
- **Total Year 1:** $350-450
|
| 451 |
-
- **Total Year 2+:** $50/year
|
| 452 |
-
|
| 453 |
-
**Option 2: Self-Hosted (Cloud GPU)**
|
| 454 |
-
- AWS g4dn.xlarge: $0.526/hour
|
| 455 |
-
- Usage: 40 hours/week (development team)
|
| 456 |
-
- **Total Year 1:** $1,094/year
|
| 457 |
-
|
| 458 |
-
**Option 3: API Service (Replicate / Together AI)**
|
| 459 |
-
- Cost: $0.10-0.25 per 1M tokens
|
| 460 |
-
- Usage: 500M tokens/year (medium team)
|
| 461 |
-
- **Total Year 1:** $50-125/year
|
| 462 |
-
|
| 463 |
-
**Option 4: Enterprise GPT-4 (for comparison)**
|
| 464 |
-
- Cost: $30/1M input tokens, $60/1M output tokens
|
| 465 |
-
- Usage: 250M input + 250M output
|
| 466 |
-
- **Total Year 1:** $22,500/year
|
| 467 |
-
|
| 468 |
-
**ROI Winner:** Self-hosted local GPU. Pays for itself in 1-2 months vs cloud, instant ROI vs GPT-4.
|
| 469 |
-
|
| 470 |
-
---
|
| 471 |
-
|
| 472 |
-
## 🎯 Use Cases & Examples
|
| 473 |
-
|
| 474 |
-
### 1. Secure Code Review Assistant
|
| 475 |
-
|
| 476 |
-
Ask the model to review code for security vulnerabilities:
|
| 477 |
-
|
| 478 |
-
```python
|
| 479 |
-
prompt = """### User:
|
| 480 |
-
Review this authentication code for security issues:
|
| 481 |
-
|
| 482 |
-
@app.route('/login', methods=['POST'])
|
| 483 |
-
def login():
|
| 484 |
-
username = request.form['username']
|
| 485 |
-
password = request.form['password']
|
| 486 |
-
query = f"SELECT * FROM users WHERE username='{username}' AND password='{password}'"
|
| 487 |
-
user = db.execute(query).fetchone()
|
| 488 |
-
if user:
|
| 489 |
-
session['user_id'] = user['id']
|
| 490 |
-
return redirect('/dashboard')
|
| 491 |
-
return 'Invalid credentials'
|
| 492 |
-
|
| 493 |
-
### Assistant:
|
| 494 |
-
"""
|
| 495 |
-
```
|
| 496 |
-
|
| 497 |
-
**Model Response:** Identifies SQL injection, plain-text passwords, missing rate limiting, session fixation risks, and provides secure alternatives.
|
| 498 |
-
|
| 499 |
-
---
|
| 500 |
-
|
| 501 |
-
### 2. Security-Aware Code Generation
|
| 502 |
-
|
| 503 |
-
Generate implementations that are secure by default:
|
| 504 |
-
|
| 505 |
-
```python
|
| 506 |
-
prompt = """### User:
|
| 507 |
-
Write a secure REST API endpoint for user registration with proper input validation, password hashing, and rate limiting in Python Flask.
|
| 508 |
-
|
| 509 |
-
### Assistant:
|
| 510 |
-
"""
|
| 511 |
-
```
|
| 512 |
-
|
| 513 |
-
**Model Response:** Generates production-ready code with bcrypt hashing, input validation, rate limiting, CSRF protection, and security headers.
|
| 514 |
-
|
| 515 |
-
---
|
| 516 |
-
|
| 517 |
-
### 3. Vulnerability Explanation & Exploitation
|
| 518 |
-
|
| 519 |
-
Understand attack vectors and exploitation:
|
| 520 |
-
|
| 521 |
-
```python
|
| 522 |
-
prompt = """### User:
|
| 523 |
-
Explain how SSRF attacks work and show me a concrete example in Python with defense strategies.
|
| 524 |
-
|
| 525 |
-
### Assistant:
|
| 526 |
-
"""
|
| 527 |
-
```
|
| 528 |
-
|
| 529 |
-
**Model Response:** Provides vulnerable code, attack demonstration, exploitation payload, and comprehensive defense-in-depth remediation.
|
| 530 |
-
|
| 531 |
-
---
|
| 532 |
-
|
| 533 |
-
### 4. Production Security Guidance
|
| 534 |
-
|
| 535 |
-
Get operational security recommendations:
|
| 536 |
-
|
| 537 |
-
```python
|
| 538 |
-
prompt = """### User:
|
| 539 |
-
How do I implement secure session management for a Flask application with 10,000 concurrent users?
|
| 540 |
-
|
| 541 |
-
### Assistant:
|
| 542 |
-
"""
|
| 543 |
-
```
|
| 544 |
-
|
| 545 |
-
**Model Response:** Covers Redis session storage, secure cookie configuration, session rotation, timeout policies, SIEM integration, and monitoring.
|
| 546 |
-
|
| 547 |
-
---
|
| 548 |
-
|
| 549 |
-
### 5. Developer Training
|
| 550 |
-
|
| 551 |
-
Use as an interactive security training tool for development teams:
|
| 552 |
-
|
| 553 |
-
```python
|
| 554 |
-
prompt = """### User:
|
| 555 |
-
Our team is building a new payment processing API. What are the top 5 security concerns we should address first?
|
| 556 |
-
|
| 557 |
-
### Assistant:
|
| 558 |
-
"""
|
| 559 |
-
```
|
| 560 |
-
|
| 561 |
-
**Model Response:** Prioritized security checklist with implementation guidance specific to payment processing.
|
| 562 |
-
|
| 563 |
-
---
|
| 564 |
-
|
| 565 |
-
## ⚠️ Limitations & Transparency
|
| 566 |
-
|
| 567 |
-
### What This Model Does Well
|
| 568 |
-
✅ Identifies common security vulnerabilities in code (OWASP Top 10)
|
| 569 |
-
✅ Generates secure implementations for standard patterns
|
| 570 |
-
✅ Explains attack vectors with concrete examples
|
| 571 |
-
✅ Provides defense-in-depth operational guidance
|
| 572 |
-
✅ Runs on consumer hardware (8GB+ RAM)
|
| 573 |
-
✅ Fast inference for IDE integration
|
| 574 |
-
|
| 575 |
-
### What This Model Doesn't Do
|
| 576 |
-
❌ **Not a security scanner** - Use tools like Semgrep, CodeQL, or Snyk for automated scanning
|
| 577 |
-
❌ **Not a penetration testing tool** - Cannot discover novel 0-days or perform active exploitation
|
| 578 |
-
❌ **Not legal/compliance advice** - Consult security professionals for regulatory requirements
|
| 579 |
-
❌ **Not a replacement for security experts** - Critical systems should undergo professional security review
|
| 580 |
-
❌ **Not trained on proprietary vulnerabilities** - Only public CVEs and documented breaches
|
| 581 |
-
|
| 582 |
-
### Known Issues & Constraints
|
| 583 |
-
- **Verbose responses:** Model was trained on detailed security explanations, may generate longer responses than needed
|
| 584 |
-
- **Common patterns only:** Best suited for OWASP Top 10 and common vulnerability patterns, not novel attack vectors
|
| 585 |
-
- **Context limitations:** 4K context window limits analysis of very large files (use chunking for large codebases)
|
| 586 |
-
- **Small model trade-offs:** 3B parameters means reduced reasoning capability vs 13B+ models
|
| 587 |
-
- **No real-time threat intelligence:** Training data frozen at Dec 2024, doesn't include 2025+ CVEs
|
| 588 |
-
|
| 589 |
-
### Appropriate Use
|
| 590 |
-
✅ Development assistance and education
|
| 591 |
-
✅ Pre-commit security checks
|
| 592 |
-
✅ Training and knowledge sharing
|
| 593 |
-
✅ Prototype security review
|
| 594 |
-
|
| 595 |
-
### Inappropriate Use
|
| 596 |
-
❌ Sole security validation for production systems
|
| 597 |
-
❌ Replacement for professional security audits
|
| 598 |
-
❌ Compliance certification validation
|
| 599 |
-
❌ Active penetration testing or exploitation
|
| 600 |
-
|
| 601 |
-
---
|
| 602 |
-
|
| 603 |
-
## 🔬 Dataset Information
|
| 604 |
-
|
| 605 |
-
This model was trained on **[SecureCode v2.0](https://huggingface.co/datasets/scthornton/securecode-v2)**, a production-grade security dataset with:
|
| 606 |
-
|
| 607 |
-
- **1,209 total examples** (841 train / 175 validation / 193 test)
|
| 608 |
-
- **100% incident grounding** - every example tied to real CVEs or security breaches
|
| 609 |
-
- **11 vulnerability categories** - complete OWASP Top 10:2025 coverage
|
| 610 |
-
- **11 programming languages** - from Python to Rust
|
| 611 |
-
- **4-turn conversational structure** - mirrors real developer-AI workflows
|
| 612 |
-
- **100% expert validation** - reviewed by independent security professionals
|
| 613 |
-
|
| 614 |
-
### Dataset Methodology
|
| 615 |
-
|
| 616 |
-
**Incident Mining Process:**
|
| 617 |
-
1. CVE database analysis (2015-2024)
|
| 618 |
-
2. Security incident reports (breaches, bug bounties)
|
| 619 |
-
3. OWASP, MITRE, and security research papers
|
| 620 |
-
4. Real-world exploitation examples
|
| 621 |
-
|
| 622 |
-
**Quality Assurance:**
|
| 623 |
-
- Expert security review (every example)
|
| 624 |
-
- CVE-aware train/validation/test split (no overlap)
|
| 625 |
-
- Multi-LLM synthesis (Claude Sonnet 4.5, GPT-4, Llama 3.2)
|
| 626 |
-
- Attack demonstration validation (tested exploits)
|
| 627 |
-
|
| 628 |
-
**Key Dataset Features:**
|
| 629 |
-
- Real-world incident references (Equifax, Capital One, SolarWinds, LastPass)
|
| 630 |
-
- Concrete attack demonstrations with exploit payloads
|
| 631 |
-
- Production operational guidance (SIEM, logging, monitoring)
|
| 632 |
-
- Defense-in-depth security controls
|
| 633 |
-
- Language-specific idioms and frameworks
|
| 634 |
-
|
| 635 |
-
See the [full dataset card](https://huggingface.co/datasets/scthornton/securecode-v2) and [research paper](https://perfecxion.ai/articles/securecode-v2-dataset-paper.html) for complete details.
|
| 636 |
-
|
| 637 |
-
---
|
| 638 |
-
|
| 639 |
-
## 🏢 About perfecXion.ai
|
| 640 |
-
|
| 641 |
-
[perfecXion.ai](https://perfecxion.ai) is dedicated to advancing AI security through research, datasets, and production-grade security tooling. Our mission is to ensure AI systems are secure by design.
|
| 642 |
-
|
| 643 |
-
**Our Work:**
|
| 644 |
-
- 🔬 **Security research** on AI/ML vulnerabilities and adversarial attacks
|
| 645 |
-
- 📊 **Open-source datasets** (SecureCode, GuardrailReduction, PromptInjection)
|
| 646 |
-
- 🛠️ **Production tools** for AI security testing and validation
|
| 647 |
-
- 🎓 **Developer education** and security training resources
|
| 648 |
-
- 📝 **Research publications** on AI security best practices
|
| 649 |
-
|
| 650 |
-
**Research Focus:**
|
| 651 |
-
- Prompt injection and jailbreak detection
|
| 652 |
-
- LLM security guardrails and safety systems
|
| 653 |
-
- RAG poisoning and retrieval vulnerabilities
|
| 654 |
-
- AI agent security and agentic AI risks
|
| 655 |
-
- Adversarial ML and model robustness
|
| 656 |
-
|
| 657 |
-
**Connect:**
|
| 658 |
-
- Website: [perfecxion.ai](https://perfecxion.ai)
|
| 659 |
-
- Research: [perfecxion.ai/research](https://perfecxion.ai/research)
|
| 660 |
-
- Knowledge Hub: [perfecxion.ai/knowledge](https://perfecxion.ai/knowledge)
|
| 661 |
-
- GitHub: [@scthornton](https://github.com/scthornton)
|
| 662 |
-
- HuggingFace: [@scthornton](https://huggingface.co/scthornton)
|
| 663 |
-
- Email: scott@perfecxion.ai
|
| 664 |
-
|
| 665 |
-
---
|
| 666 |
-
|
| 667 |
-
## 📄 License
|
| 668 |
-
|
| 669 |
-
**Model License:** Apache 2.0 (permissive - use in commercial applications)
|
| 670 |
-
**Dataset License:** CC BY-NC-SA 4.0 (non-commercial with attribution)
|
| 671 |
-
|
| 672 |
-
This model's weights are released under Apache 2.0, allowing commercial use. The training dataset (SecureCode v2.0) is CC BY-NC-SA 4.0, restricting commercial use of the raw data.
|
| 673 |
-
|
| 674 |
-
### What You CAN Do
|
| 675 |
-
✅ Use this model commercially in production applications
|
| 676 |
-
✅ Fine-tune further for your specific use case
|
| 677 |
-
✅ Deploy in enterprise environments
|
| 678 |
-
✅ Integrate into commercial products
|
| 679 |
-
✅ Distribute and modify the model weights
|
| 680 |
-
✅ Charge for services built on this model
|
| 681 |
-
|
| 682 |
-
### What You CANNOT Do with the Dataset
|
| 683 |
-
❌ Sell or redistribute the raw SecureCode v2.0 dataset commercially
|
| 684 |
-
❌ Use the dataset to train commercial models without releasing under the same license
|
| 685 |
-
❌ Remove attribution or claim ownership of the dataset
|
| 686 |
-
|
| 687 |
-
For commercial dataset licensing or custom training, contact: scott@perfecxion.ai
|
| 688 |
-
|
| 689 |
-
---
|
| 690 |
-
|
| 691 |
-
## 📚 Citation
|
| 692 |
-
|
| 693 |
-
If you use this model in your research or applications, please cite:
|
| 694 |
-
|
| 695 |
-
```bibtex
|
| 696 |
-
@misc{thornton2025securecode-llama3b,
|
| 697 |
-
title={Llama 3.2 3B - SecureCode Edition},
|
| 698 |
-
author={Thornton, Scott},
|
| 699 |
-
year={2025},
|
| 700 |
-
publisher={perfecXion.ai},
|
| 701 |
-
url={https://huggingface.co/scthornton/llama-3.2-3b-securecode},
|
| 702 |
-
note={Fine-tuned on SecureCode v2.0: https://huggingface.co/datasets/scthornton/securecode-v2}
|
| 703 |
-
}
|
| 704 |
-
|
| 705 |
-
@misc{thornton2025securecode-dataset,
|
| 706 |
-
title={SecureCode v2.0: A Production-Grade Dataset for Training Security-Aware Code Generation Models},
|
| 707 |
-
author={Thornton, Scott},
|
| 708 |
-
year={2025},
|
| 709 |
-
month={January},
|
| 710 |
-
publisher={perfecXion.ai},
|
| 711 |
-
url={https://perfecxion.ai/articles/securecode-v2-dataset-paper.html},
|
| 712 |
-
note={Dataset: https://huggingface.co/datasets/scthornton/securecode-v2}
|
| 713 |
-
}
|
| 714 |
-
```
|
| 715 |
-
|
| 716 |
-
---
|
| 717 |
-
|
| 718 |
-
## 🙏 Acknowledgments
|
| 719 |
-
|
| 720 |
-
- **Meta AI** for the excellent Llama 3.2 base model and open-source commitment
|
| 721 |
-
- **OWASP Foundation** for maintaining the Top 10 vulnerability taxonomy
|
| 722 |
-
- **MITRE Corporation** for the CVE database and vulnerability research
|
| 723 |
-
- **Security research community** for responsible disclosure practices that enabled this dataset
|
| 724 |
-
- **Hugging Face** for model hosting and inference infrastructure
|
| 725 |
-
- **Independent security reviewers** who validated dataset quality
|
| 726 |
-
|
| 727 |
-
---
|
| 728 |
-
|
| 729 |
-
## 🤝 Contributing
|
| 730 |
-
|
| 731 |
-
Found a security issue or have suggestions for improvement?
|
| 732 |
-
|
| 733 |
-
- 🐛 **Report issues:** [GitHub Issues](https://github.com/scthornton/securecode-models/issues)
|
| 734 |
-
- 💬 **Discuss improvements:** [HuggingFace Discussions](https://huggingface.co/scthornton/llama-3.2-3b-securecode/discussions)
|
| 735 |
-
- 📧 **Contact:** scott@perfecxion.ai
|
| 736 |
-
|
| 737 |
-
### Community Contributions Welcome
|
| 738 |
-
|
| 739 |
-
Especially interested in:
|
| 740 |
-
- **Security benchmark evaluations** on industry-standard datasets
|
| 741 |
-
- **Production deployment case studies** showing real-world impact
|
| 742 |
-
- **Integration examples** with popular frameworks (LangChain, AutoGen, CrewAI)
|
| 743 |
-
- **Vulnerability detection accuracy** assessments
|
| 744 |
-
- **Performance optimization** techniques for specific hardware
|
| 745 |
-
|
| 746 |
-
---
|
| 747 |
-
|
| 748 |
-
## 🔗 SecureCode Model Collection
|
| 749 |
-
|
| 750 |
-
Explore other SecureCode fine-tuned models optimized for different use cases:
|
| 751 |
-
|
| 752 |
-
### Entry-Level Models (3-7B)
|
| 753 |
-
- **[llama-3.2-3b-securecode](https://huggingface.co/scthornton/llama-3.2-3b-securecode)** ⭐ (YOU ARE HERE)
|
| 754 |
-
- **Best for:** Consumer hardware, IDE integration, education
|
| 755 |
-
- **Hardware:** 8GB RAM minimum
|
| 756 |
-
- **Unique strength:** Most accessible
|
| 757 |
-
|
| 758 |
-
- **[deepseek-coder-6.7b-securecode](https://huggingface.co/scthornton/deepseek-coder-6.7b-securecode)**
|
| 759 |
-
- **Best for:** Security-optimized baseline
|
| 760 |
-
- **Hardware:** 16GB RAM
|
| 761 |
-
- **Unique strength:** Security-first architecture
|
| 762 |
-
|
| 763 |
-
- **[qwen2.5-coder-7b-securecode](https://huggingface.co/scthornton/qwen2.5-coder-7b-securecode)**
|
| 764 |
-
- **Best for:** Best code understanding in 7B class
|
| 765 |
-
- **Hardware:** 16GB RAM
|
| 766 |
-
- **Unique strength:** 128K context, best-in-class
|
| 767 |
-
|
| 768 |
-
- **[codegemma-7b-securecode](https://huggingface.co/scthornton/codegemma-7b-securecode)**
|
| 769 |
-
- **Best for:** Google ecosystem, instruction following
|
| 770 |
-
- **Hardware:** 16GB RAM
|
| 771 |
-
- **Unique strength:** Google brand, strong completion
|
| 772 |
-
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| 773 |
-
### Mid-Range Models (13-15B)
|
| 774 |
-
- **[codellama-13b-securecode](https://huggingface.co/scthornton/codellama-13b-securecode)**
|
| 775 |
-
- **Best for:** Enterprise trust, Meta brand
|
| 776 |
-
- **Hardware:** 24GB RAM
|
| 777 |
-
- **Unique strength:** Proven track record
|
| 778 |
-
|
| 779 |
-
- **[qwen2.5-coder-14b-securecode](https://huggingface.co/scthornton/qwen2.5-coder-14b-securecode)**
|
| 780 |
-
- **Best for:** Advanced code analysis
|
| 781 |
-
- **Hardware:** 32GB RAM
|
| 782 |
-
- **Unique strength:** 128K context window
|
| 783 |
-
|
| 784 |
-
- **[starcoder2-15b-securecode](https://huggingface.co/scthornton/starcoder2-15b-securecode)**
|
| 785 |
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- **Best for:** Multi-language projects (600+ languages)
|
| 786 |
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- **Hardware:** 32GB RAM
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| 787 |
-
- **Unique strength:** Broadest language support
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| 788 |
-
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| 789 |
-
### Enterprise-Scale Models (20B+)
|
| 790 |
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- **[granite-20b-code-securecode](https://huggingface.co/scthornton/granite-20b-code-securecode)**
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| 791 |
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- **Best for:** Enterprise-scale, IBM trust
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| 792 |
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- **Hardware:** 48GB RAM
|
| 793 |
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- **Unique strength:** Largest model, enterprise compliance
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| 794 |
-
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| 795 |
-
**View Complete Collection:** [SecureCode Models](https://huggingface.co/collections/scthornton/securecode)
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| 796 |
-
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| 797 |
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---
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| 798 |
-
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| 799 |
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<div align="center">
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| 800 |
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| 801 |
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**Built with ❤️ for secure software development**
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| 802 |
-
|
| 803 |
-
[perfecXion.ai](https://perfecxion.ai) | [Research](https://perfecxion.ai/research) | [Knowledge Hub](https://perfecxion.ai/knowledge) | [Contact](mailto:scott@perfecxion.ai)
|
| 804 |
-
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| 805 |
-
---
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| 806 |
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| 807 |
-
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| 808 |
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| 809 |
-
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| 1 |
---
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| 2 |
+
library_name: peft
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| 3 |
+
license: llama3.2
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| 4 |
base_model: meta-llama/Llama-3.2-3B-Instruct
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| 5 |
tags:
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| 6 |
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- base_model:adapter:meta-llama/Llama-3.2-3B-Instruct
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| 7 |
+
- lora
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| 8 |
+
- transformers
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| 9 |
pipeline_tag: text-generation
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model-index:
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- name: llama-3.2-3b-securecode
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| 12 |
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results: []
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| 13 |
---
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| 14 |
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| 15 |
+
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
|
| 16 |
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should probably proofread and complete it, then remove this comment. -->
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| 17 |
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| 18 |
+
# llama-3.2-3b-securecode
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| 19 |
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This model is a fine-tuned version of [meta-llama/Llama-3.2-3B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-3B-Instruct) on the None dataset.
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| 21 |
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## Model description
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| 23 |
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More information needed
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## Intended uses & limitations
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| 27 |
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More information needed
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## Training and evaluation data
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| 31 |
|
| 32 |
+
More information needed
|
| 33 |
|
| 34 |
+
## Training procedure
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| 35 |
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| 36 |
+
### Training hyperparameters
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| 37 |
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| 38 |
+
The following hyperparameters were used during training:
|
| 39 |
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- learning_rate: 0.0002
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| 40 |
+
- train_batch_size: 4
|
| 41 |
+
- eval_batch_size: 8
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| 42 |
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- seed: 42
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| 43 |
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- gradient_accumulation_steps: 4
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| 44 |
+
- total_train_batch_size: 16
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| 45 |
+
- optimizer: Use paged_adamw_8bit with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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| 46 |
+
- lr_scheduler_type: cosine
|
| 47 |
+
- lr_scheduler_warmup_steps: 100
|
| 48 |
+
- num_epochs: 3
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| 49 |
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| 50 |
+
### Training results
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| 51 |
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|
|
|
|
|
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|
|
|
|
|
|
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|
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|
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|
|
| 53 |
|
| 54 |
+
### Framework versions
|
| 55 |
|
| 56 |
+
- PEFT 0.18.1
|
| 57 |
+
- Transformers 5.1.0
|
| 58 |
+
- Pytorch 2.7.1+cu128
|
| 59 |
+
- Datasets 2.21.0
|
| 60 |
+
- Tokenizers 0.22.2
|
tokenizer_config.json
CHANGED
|
@@ -1,2063 +1,14 @@
|
|
| 1 |
{
|
| 2 |
-
"
|
| 3 |
-
"128000": {
|
| 4 |
-
"content": "<|begin_of_text|>",
|
| 5 |
-
"lstrip": false,
|
| 6 |
-
"normalized": false,
|
| 7 |
-
"rstrip": false,
|
| 8 |
-
"single_word": false,
|
| 9 |
-
"special": true
|
| 10 |
-
},
|
| 11 |
-
"128001": {
|
| 12 |
-
"content": "<|end_of_text|>",
|
| 13 |
-
"lstrip": false,
|
| 14 |
-
"normalized": false,
|
| 15 |
-
"rstrip": false,
|
| 16 |
-
"single_word": false,
|
| 17 |
-
"special": true
|
| 18 |
-
},
|
| 19 |
-
"128002": {
|
| 20 |
-
"content": "<|reserved_special_token_0|>",
|
| 21 |
-
"lstrip": false,
|
| 22 |
-
"normalized": false,
|
| 23 |
-
"rstrip": false,
|
| 24 |
-
"single_word": false,
|
| 25 |
-
"special": true
|
| 26 |
-
},
|
| 27 |
-
"128003": {
|
| 28 |
-
"content": "<|reserved_special_token_1|>",
|
| 29 |
-
"lstrip": false,
|
| 30 |
-
"normalized": false,
|
| 31 |
-
"rstrip": false,
|
| 32 |
-
"single_word": false,
|
| 33 |
-
"special": true
|
| 34 |
-
},
|
| 35 |
-
"128004": {
|
| 36 |
-
"content": "<|finetune_right_pad_id|>",
|
| 37 |
-
"lstrip": false,
|
| 38 |
-
"normalized": false,
|
| 39 |
-
"rstrip": false,
|
| 40 |
-
"single_word": false,
|
| 41 |
-
"special": true
|
| 42 |
-
},
|
| 43 |
-
"128005": {
|
| 44 |
-
"content": "<|reserved_special_token_2|>",
|
| 45 |
-
"lstrip": false,
|
| 46 |
-
"normalized": false,
|
| 47 |
-
"rstrip": false,
|
| 48 |
-
"single_word": false,
|
| 49 |
-
"special": true
|
| 50 |
-
},
|
| 51 |
-
"128006": {
|
| 52 |
-
"content": "<|start_header_id|>",
|
| 53 |
-
"lstrip": false,
|
| 54 |
-
"normalized": false,
|
| 55 |
-
"rstrip": false,
|
| 56 |
-
"single_word": false,
|
| 57 |
-
"special": true
|
| 58 |
-
},
|
| 59 |
-
"128007": {
|
| 60 |
-
"content": "<|end_header_id|>",
|
| 61 |
-
"lstrip": false,
|
| 62 |
-
"normalized": false,
|
| 63 |
-
"rstrip": false,
|
| 64 |
-
"single_word": false,
|
| 65 |
-
"special": true
|
| 66 |
-
},
|
| 67 |
-
"128008": {
|
| 68 |
-
"content": "<|eom_id|>",
|
| 69 |
-
"lstrip": false,
|
| 70 |
-
"normalized": false,
|
| 71 |
-
"rstrip": false,
|
| 72 |
-
"single_word": false,
|
| 73 |
-
"special": true
|
| 74 |
-
},
|
| 75 |
-
"128009": {
|
| 76 |
-
"content": "<|eot_id|>",
|
| 77 |
-
"lstrip": false,
|
| 78 |
-
"normalized": false,
|
| 79 |
-
"rstrip": false,
|
| 80 |
-
"single_word": false,
|
| 81 |
-
"special": true
|
| 82 |
-
},
|
| 83 |
-
"128010": {
|
| 84 |
-
"content": "<|python_tag|>",
|
| 85 |
-
"lstrip": false,
|
| 86 |
-
"normalized": false,
|
| 87 |
-
"rstrip": false,
|
| 88 |
-
"single_word": false,
|
| 89 |
-
"special": true
|
| 90 |
-
},
|
| 91 |
-
"128011": {
|
| 92 |
-
"content": "<|reserved_special_token_3|>",
|
| 93 |
-
"lstrip": false,
|
| 94 |
-
"normalized": false,
|
| 95 |
-
"rstrip": false,
|
| 96 |
-
"single_word": false,
|
| 97 |
-
"special": true
|
| 98 |
-
},
|
| 99 |
-
"128012": {
|
| 100 |
-
"content": "<|reserved_special_token_4|>",
|
| 101 |
-
"lstrip": false,
|
| 102 |
-
"normalized": false,
|
| 103 |
-
"rstrip": false,
|
| 104 |
-
"single_word": false,
|
| 105 |
-
"special": true
|
| 106 |
-
},
|
| 107 |
-
"128013": {
|
| 108 |
-
"content": "<|reserved_special_token_5|>",
|
| 109 |
-
"lstrip": false,
|
| 110 |
-
"normalized": false,
|
| 111 |
-
"rstrip": false,
|
| 112 |
-
"single_word": false,
|
| 113 |
-
"special": true
|
| 114 |
-
},
|
| 115 |
-
"128014": {
|
| 116 |
-
"content": "<|reserved_special_token_6|>",
|
| 117 |
-
"lstrip": false,
|
| 118 |
-
"normalized": false,
|
| 119 |
-
"rstrip": false,
|
| 120 |
-
"single_word": false,
|
| 121 |
-
"special": true
|
| 122 |
-
},
|
| 123 |
-
"128015": {
|
| 124 |
-
"content": "<|reserved_special_token_7|>",
|
| 125 |
-
"lstrip": false,
|
| 126 |
-
"normalized": false,
|
| 127 |
-
"rstrip": false,
|
| 128 |
-
"single_word": false,
|
| 129 |
-
"special": true
|
| 130 |
-
},
|
| 131 |
-
"128016": {
|
| 132 |
-
"content": "<|reserved_special_token_8|>",
|
| 133 |
-
"lstrip": false,
|
| 134 |
-
"normalized": false,
|
| 135 |
-
"rstrip": false,
|
| 136 |
-
"single_word": false,
|
| 137 |
-
"special": true
|
| 138 |
-
},
|
| 139 |
-
"128017": {
|
| 140 |
-
"content": "<|reserved_special_token_9|>",
|
| 141 |
-
"lstrip": false,
|
| 142 |
-
"normalized": false,
|
| 143 |
-
"rstrip": false,
|
| 144 |
-
"single_word": false,
|
| 145 |
-
"special": true
|
| 146 |
-
},
|
| 147 |
-
"128018": {
|
| 148 |
-
"content": "<|reserved_special_token_10|>",
|
| 149 |
-
"lstrip": false,
|
| 150 |
-
"normalized": false,
|
| 151 |
-
"rstrip": false,
|
| 152 |
-
"single_word": false,
|
| 153 |
-
"special": true
|
| 154 |
-
},
|
| 155 |
-
"128019": {
|
| 156 |
-
"content": "<|reserved_special_token_11|>",
|
| 157 |
-
"lstrip": false,
|
| 158 |
-
"normalized": false,
|
| 159 |
-
"rstrip": false,
|
| 160 |
-
"single_word": false,
|
| 161 |
-
"special": true
|
| 162 |
-
},
|
| 163 |
-
"128020": {
|
| 164 |
-
"content": "<|reserved_special_token_12|>",
|
| 165 |
-
"lstrip": false,
|
| 166 |
-
"normalized": false,
|
| 167 |
-
"rstrip": false,
|
| 168 |
-
"single_word": false,
|
| 169 |
-
"special": true
|
| 170 |
-
},
|
| 171 |
-
"128021": {
|
| 172 |
-
"content": "<|reserved_special_token_13|>",
|
| 173 |
-
"lstrip": false,
|
| 174 |
-
"normalized": false,
|
| 175 |
-
"rstrip": false,
|
| 176 |
-
"single_word": false,
|
| 177 |
-
"special": true
|
| 178 |
-
},
|
| 179 |
-
"128022": {
|
| 180 |
-
"content": "<|reserved_special_token_14|>",
|
| 181 |
-
"lstrip": false,
|
| 182 |
-
"normalized": false,
|
| 183 |
-
"rstrip": false,
|
| 184 |
-
"single_word": false,
|
| 185 |
-
"special": true
|
| 186 |
-
},
|
| 187 |
-
"128023": {
|
| 188 |
-
"content": "<|reserved_special_token_15|>",
|
| 189 |
-
"lstrip": false,
|
| 190 |
-
"normalized": false,
|
| 191 |
-
"rstrip": false,
|
| 192 |
-
"single_word": false,
|
| 193 |
-
"special": true
|
| 194 |
-
},
|
| 195 |
-
"128024": {
|
| 196 |
-
"content": "<|reserved_special_token_16|>",
|
| 197 |
-
"lstrip": false,
|
| 198 |
-
"normalized": false,
|
| 199 |
-
"rstrip": false,
|
| 200 |
-
"single_word": false,
|
| 201 |
-
"special": true
|
| 202 |
-
},
|
| 203 |
-
"128025": {
|
| 204 |
-
"content": "<|reserved_special_token_17|>",
|
| 205 |
-
"lstrip": false,
|
| 206 |
-
"normalized": false,
|
| 207 |
-
"rstrip": false,
|
| 208 |
-
"single_word": false,
|
| 209 |
-
"special": true
|
| 210 |
-
},
|
| 211 |
-
"128026": {
|
| 212 |
-
"content": "<|reserved_special_token_18|>",
|
| 213 |
-
"lstrip": false,
|
| 214 |
-
"normalized": false,
|
| 215 |
-
"rstrip": false,
|
| 216 |
-
"single_word": false,
|
| 217 |
-
"special": true
|
| 218 |
-
},
|
| 219 |
-
"128027": {
|
| 220 |
-
"content": "<|reserved_special_token_19|>",
|
| 221 |
-
"lstrip": false,
|
| 222 |
-
"normalized": false,
|
| 223 |
-
"rstrip": false,
|
| 224 |
-
"single_word": false,
|
| 225 |
-
"special": true
|
| 226 |
-
},
|
| 227 |
-
"128028": {
|
| 228 |
-
"content": "<|reserved_special_token_20|>",
|
| 229 |
-
"lstrip": false,
|
| 230 |
-
"normalized": false,
|
| 231 |
-
"rstrip": false,
|
| 232 |
-
"single_word": false,
|
| 233 |
-
"special": true
|
| 234 |
-
},
|
| 235 |
-
"128029": {
|
| 236 |
-
"content": "<|reserved_special_token_21|>",
|
| 237 |
-
"lstrip": false,
|
| 238 |
-
"normalized": false,
|
| 239 |
-
"rstrip": false,
|
| 240 |
-
"single_word": false,
|
| 241 |
-
"special": true
|
| 242 |
-
},
|
| 243 |
-
"128030": {
|
| 244 |
-
"content": "<|reserved_special_token_22|>",
|
| 245 |
-
"lstrip": false,
|
| 246 |
-
"normalized": false,
|
| 247 |
-
"rstrip": false,
|
| 248 |
-
"single_word": false,
|
| 249 |
-
"special": true
|
| 250 |
-
},
|
| 251 |
-
"128031": {
|
| 252 |
-
"content": "<|reserved_special_token_23|>",
|
| 253 |
-
"lstrip": false,
|
| 254 |
-
"normalized": false,
|
| 255 |
-
"rstrip": false,
|
| 256 |
-
"single_word": false,
|
| 257 |
-
"special": true
|
| 258 |
-
},
|
| 259 |
-
"128032": {
|
| 260 |
-
"content": "<|reserved_special_token_24|>",
|
| 261 |
-
"lstrip": false,
|
| 262 |
-
"normalized": false,
|
| 263 |
-
"rstrip": false,
|
| 264 |
-
"single_word": false,
|
| 265 |
-
"special": true
|
| 266 |
-
},
|
| 267 |
-
"128033": {
|
| 268 |
-
"content": "<|reserved_special_token_25|>",
|
| 269 |
-
"lstrip": false,
|
| 270 |
-
"normalized": false,
|
| 271 |
-
"rstrip": false,
|
| 272 |
-
"single_word": false,
|
| 273 |
-
"special": true
|
| 274 |
-
},
|
| 275 |
-
"128034": {
|
| 276 |
-
"content": "<|reserved_special_token_26|>",
|
| 277 |
-
"lstrip": false,
|
| 278 |
-
"normalized": false,
|
| 279 |
-
"rstrip": false,
|
| 280 |
-
"single_word": false,
|
| 281 |
-
"special": true
|
| 282 |
-
},
|
| 283 |
-
"128035": {
|
| 284 |
-
"content": "<|reserved_special_token_27|>",
|
| 285 |
-
"lstrip": false,
|
| 286 |
-
"normalized": false,
|
| 287 |
-
"rstrip": false,
|
| 288 |
-
"single_word": false,
|
| 289 |
-
"special": true
|
| 290 |
-
},
|
| 291 |
-
"128036": {
|
| 292 |
-
"content": "<|reserved_special_token_28|>",
|
| 293 |
-
"lstrip": false,
|
| 294 |
-
"normalized": false,
|
| 295 |
-
"rstrip": false,
|
| 296 |
-
"single_word": false,
|
| 297 |
-
"special": true
|
| 298 |
-
},
|
| 299 |
-
"128037": {
|
| 300 |
-
"content": "<|reserved_special_token_29|>",
|
| 301 |
-
"lstrip": false,
|
| 302 |
-
"normalized": false,
|
| 303 |
-
"rstrip": false,
|
| 304 |
-
"single_word": false,
|
| 305 |
-
"special": true
|
| 306 |
-
},
|
| 307 |
-
"128038": {
|
| 308 |
-
"content": "<|reserved_special_token_30|>",
|
| 309 |
-
"lstrip": false,
|
| 310 |
-
"normalized": false,
|
| 311 |
-
"rstrip": false,
|
| 312 |
-
"single_word": false,
|
| 313 |
-
"special": true
|
| 314 |
-
},
|
| 315 |
-
"128039": {
|
| 316 |
-
"content": "<|reserved_special_token_31|>",
|
| 317 |
-
"lstrip": false,
|
| 318 |
-
"normalized": false,
|
| 319 |
-
"rstrip": false,
|
| 320 |
-
"single_word": false,
|
| 321 |
-
"special": true
|
| 322 |
-
},
|
| 323 |
-
"128040": {
|
| 324 |
-
"content": "<|reserved_special_token_32|>",
|
| 325 |
-
"lstrip": false,
|
| 326 |
-
"normalized": false,
|
| 327 |
-
"rstrip": false,
|
| 328 |
-
"single_word": false,
|
| 329 |
-
"special": true
|
| 330 |
-
},
|
| 331 |
-
"128041": {
|
| 332 |
-
"content": "<|reserved_special_token_33|>",
|
| 333 |
-
"lstrip": false,
|
| 334 |
-
"normalized": false,
|
| 335 |
-
"rstrip": false,
|
| 336 |
-
"single_word": false,
|
| 337 |
-
"special": true
|
| 338 |
-
},
|
| 339 |
-
"128042": {
|
| 340 |
-
"content": "<|reserved_special_token_34|>",
|
| 341 |
-
"lstrip": false,
|
| 342 |
-
"normalized": false,
|
| 343 |
-
"rstrip": false,
|
| 344 |
-
"single_word": false,
|
| 345 |
-
"special": true
|
| 346 |
-
},
|
| 347 |
-
"128043": {
|
| 348 |
-
"content": "<|reserved_special_token_35|>",
|
| 349 |
-
"lstrip": false,
|
| 350 |
-
"normalized": false,
|
| 351 |
-
"rstrip": false,
|
| 352 |
-
"single_word": false,
|
| 353 |
-
"special": true
|
| 354 |
-
},
|
| 355 |
-
"128044": {
|
| 356 |
-
"content": "<|reserved_special_token_36|>",
|
| 357 |
-
"lstrip": false,
|
| 358 |
-
"normalized": false,
|
| 359 |
-
"rstrip": false,
|
| 360 |
-
"single_word": false,
|
| 361 |
-
"special": true
|
| 362 |
-
},
|
| 363 |
-
"128045": {
|
| 364 |
-
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| 770 |
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| 1594 |
-
},
|
| 1595 |
-
"128199": {
|
| 1596 |
-
"content": "<|reserved_special_token_191|>",
|
| 1597 |
-
"lstrip": false,
|
| 1598 |
-
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|
| 1599 |
-
"rstrip": false,
|
| 1600 |
-
"single_word": false,
|
| 1601 |
-
"special": true
|
| 1602 |
-
},
|
| 1603 |
-
"128200": {
|
| 1604 |
-
"content": "<|reserved_special_token_192|>",
|
| 1605 |
-
"lstrip": false,
|
| 1606 |
-
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|
| 1607 |
-
"rstrip": false,
|
| 1608 |
-
"single_word": false,
|
| 1609 |
-
"special": true
|
| 1610 |
-
},
|
| 1611 |
-
"128201": {
|
| 1612 |
-
"content": "<|reserved_special_token_193|>",
|
| 1613 |
-
"lstrip": false,
|
| 1614 |
-
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|
| 1615 |
-
"rstrip": false,
|
| 1616 |
-
"single_word": false,
|
| 1617 |
-
"special": true
|
| 1618 |
-
},
|
| 1619 |
-
"128202": {
|
| 1620 |
-
"content": "<|reserved_special_token_194|>",
|
| 1621 |
-
"lstrip": false,
|
| 1622 |
-
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|
| 1623 |
-
"rstrip": false,
|
| 1624 |
-
"single_word": false,
|
| 1625 |
-
"special": true
|
| 1626 |
-
},
|
| 1627 |
-
"128203": {
|
| 1628 |
-
"content": "<|reserved_special_token_195|>",
|
| 1629 |
-
"lstrip": false,
|
| 1630 |
-
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|
| 1631 |
-
"rstrip": false,
|
| 1632 |
-
"single_word": false,
|
| 1633 |
-
"special": true
|
| 1634 |
-
},
|
| 1635 |
-
"128204": {
|
| 1636 |
-
"content": "<|reserved_special_token_196|>",
|
| 1637 |
-
"lstrip": false,
|
| 1638 |
-
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|
| 1639 |
-
"rstrip": false,
|
| 1640 |
-
"single_word": false,
|
| 1641 |
-
"special": true
|
| 1642 |
-
},
|
| 1643 |
-
"128205": {
|
| 1644 |
-
"content": "<|reserved_special_token_197|>",
|
| 1645 |
-
"lstrip": false,
|
| 1646 |
-
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|
| 1647 |
-
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|
| 1648 |
-
"single_word": false,
|
| 1649 |
-
"special": true
|
| 1650 |
-
},
|
| 1651 |
-
"128206": {
|
| 1652 |
-
"content": "<|reserved_special_token_198|>",
|
| 1653 |
-
"lstrip": false,
|
| 1654 |
-
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|
| 1655 |
-
"rstrip": false,
|
| 1656 |
-
"single_word": false,
|
| 1657 |
-
"special": true
|
| 1658 |
-
},
|
| 1659 |
-
"128207": {
|
| 1660 |
-
"content": "<|reserved_special_token_199|>",
|
| 1661 |
-
"lstrip": false,
|
| 1662 |
-
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|
| 1663 |
-
"rstrip": false,
|
| 1664 |
-
"single_word": false,
|
| 1665 |
-
"special": true
|
| 1666 |
-
},
|
| 1667 |
-
"128208": {
|
| 1668 |
-
"content": "<|reserved_special_token_200|>",
|
| 1669 |
-
"lstrip": false,
|
| 1670 |
-
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|
| 1671 |
-
"rstrip": false,
|
| 1672 |
-
"single_word": false,
|
| 1673 |
-
"special": true
|
| 1674 |
-
},
|
| 1675 |
-
"128209": {
|
| 1676 |
-
"content": "<|reserved_special_token_201|>",
|
| 1677 |
-
"lstrip": false,
|
| 1678 |
-
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|
| 1679 |
-
"rstrip": false,
|
| 1680 |
-
"single_word": false,
|
| 1681 |
-
"special": true
|
| 1682 |
-
},
|
| 1683 |
-
"128210": {
|
| 1684 |
-
"content": "<|reserved_special_token_202|>",
|
| 1685 |
-
"lstrip": false,
|
| 1686 |
-
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|
| 1687 |
-
"rstrip": false,
|
| 1688 |
-
"single_word": false,
|
| 1689 |
-
"special": true
|
| 1690 |
-
},
|
| 1691 |
-
"128211": {
|
| 1692 |
-
"content": "<|reserved_special_token_203|>",
|
| 1693 |
-
"lstrip": false,
|
| 1694 |
-
"normalized": false,
|
| 1695 |
-
"rstrip": false,
|
| 1696 |
-
"single_word": false,
|
| 1697 |
-
"special": true
|
| 1698 |
-
},
|
| 1699 |
-
"128212": {
|
| 1700 |
-
"content": "<|reserved_special_token_204|>",
|
| 1701 |
-
"lstrip": false,
|
| 1702 |
-
"normalized": false,
|
| 1703 |
-
"rstrip": false,
|
| 1704 |
-
"single_word": false,
|
| 1705 |
-
"special": true
|
| 1706 |
-
},
|
| 1707 |
-
"128213": {
|
| 1708 |
-
"content": "<|reserved_special_token_205|>",
|
| 1709 |
-
"lstrip": false,
|
| 1710 |
-
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|
| 1711 |
-
"rstrip": false,
|
| 1712 |
-
"single_word": false,
|
| 1713 |
-
"special": true
|
| 1714 |
-
},
|
| 1715 |
-
"128214": {
|
| 1716 |
-
"content": "<|reserved_special_token_206|>",
|
| 1717 |
-
"lstrip": false,
|
| 1718 |
-
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|
| 1719 |
-
"rstrip": false,
|
| 1720 |
-
"single_word": false,
|
| 1721 |
-
"special": true
|
| 1722 |
-
},
|
| 1723 |
-
"128215": {
|
| 1724 |
-
"content": "<|reserved_special_token_207|>",
|
| 1725 |
-
"lstrip": false,
|
| 1726 |
-
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|
| 1727 |
-
"rstrip": false,
|
| 1728 |
-
"single_word": false,
|
| 1729 |
-
"special": true
|
| 1730 |
-
},
|
| 1731 |
-
"128216": {
|
| 1732 |
-
"content": "<|reserved_special_token_208|>",
|
| 1733 |
-
"lstrip": false,
|
| 1734 |
-
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|
| 1735 |
-
"rstrip": false,
|
| 1736 |
-
"single_word": false,
|
| 1737 |
-
"special": true
|
| 1738 |
-
},
|
| 1739 |
-
"128217": {
|
| 1740 |
-
"content": "<|reserved_special_token_209|>",
|
| 1741 |
-
"lstrip": false,
|
| 1742 |
-
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|
| 1743 |
-
"rstrip": false,
|
| 1744 |
-
"single_word": false,
|
| 1745 |
-
"special": true
|
| 1746 |
-
},
|
| 1747 |
-
"128218": {
|
| 1748 |
-
"content": "<|reserved_special_token_210|>",
|
| 1749 |
-
"lstrip": false,
|
| 1750 |
-
"normalized": false,
|
| 1751 |
-
"rstrip": false,
|
| 1752 |
-
"single_word": false,
|
| 1753 |
-
"special": true
|
| 1754 |
-
},
|
| 1755 |
-
"128219": {
|
| 1756 |
-
"content": "<|reserved_special_token_211|>",
|
| 1757 |
-
"lstrip": false,
|
| 1758 |
-
"normalized": false,
|
| 1759 |
-
"rstrip": false,
|
| 1760 |
-
"single_word": false,
|
| 1761 |
-
"special": true
|
| 1762 |
-
},
|
| 1763 |
-
"128220": {
|
| 1764 |
-
"content": "<|reserved_special_token_212|>",
|
| 1765 |
-
"lstrip": false,
|
| 1766 |
-
"normalized": false,
|
| 1767 |
-
"rstrip": false,
|
| 1768 |
-
"single_word": false,
|
| 1769 |
-
"special": true
|
| 1770 |
-
},
|
| 1771 |
-
"128221": {
|
| 1772 |
-
"content": "<|reserved_special_token_213|>",
|
| 1773 |
-
"lstrip": false,
|
| 1774 |
-
"normalized": false,
|
| 1775 |
-
"rstrip": false,
|
| 1776 |
-
"single_word": false,
|
| 1777 |
-
"special": true
|
| 1778 |
-
},
|
| 1779 |
-
"128222": {
|
| 1780 |
-
"content": "<|reserved_special_token_214|>",
|
| 1781 |
-
"lstrip": false,
|
| 1782 |
-
"normalized": false,
|
| 1783 |
-
"rstrip": false,
|
| 1784 |
-
"single_word": false,
|
| 1785 |
-
"special": true
|
| 1786 |
-
},
|
| 1787 |
-
"128223": {
|
| 1788 |
-
"content": "<|reserved_special_token_215|>",
|
| 1789 |
-
"lstrip": false,
|
| 1790 |
-
"normalized": false,
|
| 1791 |
-
"rstrip": false,
|
| 1792 |
-
"single_word": false,
|
| 1793 |
-
"special": true
|
| 1794 |
-
},
|
| 1795 |
-
"128224": {
|
| 1796 |
-
"content": "<|reserved_special_token_216|>",
|
| 1797 |
-
"lstrip": false,
|
| 1798 |
-
"normalized": false,
|
| 1799 |
-
"rstrip": false,
|
| 1800 |
-
"single_word": false,
|
| 1801 |
-
"special": true
|
| 1802 |
-
},
|
| 1803 |
-
"128225": {
|
| 1804 |
-
"content": "<|reserved_special_token_217|>",
|
| 1805 |
-
"lstrip": false,
|
| 1806 |
-
"normalized": false,
|
| 1807 |
-
"rstrip": false,
|
| 1808 |
-
"single_word": false,
|
| 1809 |
-
"special": true
|
| 1810 |
-
},
|
| 1811 |
-
"128226": {
|
| 1812 |
-
"content": "<|reserved_special_token_218|>",
|
| 1813 |
-
"lstrip": false,
|
| 1814 |
-
"normalized": false,
|
| 1815 |
-
"rstrip": false,
|
| 1816 |
-
"single_word": false,
|
| 1817 |
-
"special": true
|
| 1818 |
-
},
|
| 1819 |
-
"128227": {
|
| 1820 |
-
"content": "<|reserved_special_token_219|>",
|
| 1821 |
-
"lstrip": false,
|
| 1822 |
-
"normalized": false,
|
| 1823 |
-
"rstrip": false,
|
| 1824 |
-
"single_word": false,
|
| 1825 |
-
"special": true
|
| 1826 |
-
},
|
| 1827 |
-
"128228": {
|
| 1828 |
-
"content": "<|reserved_special_token_220|>",
|
| 1829 |
-
"lstrip": false,
|
| 1830 |
-
"normalized": false,
|
| 1831 |
-
"rstrip": false,
|
| 1832 |
-
"single_word": false,
|
| 1833 |
-
"special": true
|
| 1834 |
-
},
|
| 1835 |
-
"128229": {
|
| 1836 |
-
"content": "<|reserved_special_token_221|>",
|
| 1837 |
-
"lstrip": false,
|
| 1838 |
-
"normalized": false,
|
| 1839 |
-
"rstrip": false,
|
| 1840 |
-
"single_word": false,
|
| 1841 |
-
"special": true
|
| 1842 |
-
},
|
| 1843 |
-
"128230": {
|
| 1844 |
-
"content": "<|reserved_special_token_222|>",
|
| 1845 |
-
"lstrip": false,
|
| 1846 |
-
"normalized": false,
|
| 1847 |
-
"rstrip": false,
|
| 1848 |
-
"single_word": false,
|
| 1849 |
-
"special": true
|
| 1850 |
-
},
|
| 1851 |
-
"128231": {
|
| 1852 |
-
"content": "<|reserved_special_token_223|>",
|
| 1853 |
-
"lstrip": false,
|
| 1854 |
-
"normalized": false,
|
| 1855 |
-
"rstrip": false,
|
| 1856 |
-
"single_word": false,
|
| 1857 |
-
"special": true
|
| 1858 |
-
},
|
| 1859 |
-
"128232": {
|
| 1860 |
-
"content": "<|reserved_special_token_224|>",
|
| 1861 |
-
"lstrip": false,
|
| 1862 |
-
"normalized": false,
|
| 1863 |
-
"rstrip": false,
|
| 1864 |
-
"single_word": false,
|
| 1865 |
-
"special": true
|
| 1866 |
-
},
|
| 1867 |
-
"128233": {
|
| 1868 |
-
"content": "<|reserved_special_token_225|>",
|
| 1869 |
-
"lstrip": false,
|
| 1870 |
-
"normalized": false,
|
| 1871 |
-
"rstrip": false,
|
| 1872 |
-
"single_word": false,
|
| 1873 |
-
"special": true
|
| 1874 |
-
},
|
| 1875 |
-
"128234": {
|
| 1876 |
-
"content": "<|reserved_special_token_226|>",
|
| 1877 |
-
"lstrip": false,
|
| 1878 |
-
"normalized": false,
|
| 1879 |
-
"rstrip": false,
|
| 1880 |
-
"single_word": false,
|
| 1881 |
-
"special": true
|
| 1882 |
-
},
|
| 1883 |
-
"128235": {
|
| 1884 |
-
"content": "<|reserved_special_token_227|>",
|
| 1885 |
-
"lstrip": false,
|
| 1886 |
-
"normalized": false,
|
| 1887 |
-
"rstrip": false,
|
| 1888 |
-
"single_word": false,
|
| 1889 |
-
"special": true
|
| 1890 |
-
},
|
| 1891 |
-
"128236": {
|
| 1892 |
-
"content": "<|reserved_special_token_228|>",
|
| 1893 |
-
"lstrip": false,
|
| 1894 |
-
"normalized": false,
|
| 1895 |
-
"rstrip": false,
|
| 1896 |
-
"single_word": false,
|
| 1897 |
-
"special": true
|
| 1898 |
-
},
|
| 1899 |
-
"128237": {
|
| 1900 |
-
"content": "<|reserved_special_token_229|>",
|
| 1901 |
-
"lstrip": false,
|
| 1902 |
-
"normalized": false,
|
| 1903 |
-
"rstrip": false,
|
| 1904 |
-
"single_word": false,
|
| 1905 |
-
"special": true
|
| 1906 |
-
},
|
| 1907 |
-
"128238": {
|
| 1908 |
-
"content": "<|reserved_special_token_230|>",
|
| 1909 |
-
"lstrip": false,
|
| 1910 |
-
"normalized": false,
|
| 1911 |
-
"rstrip": false,
|
| 1912 |
-
"single_word": false,
|
| 1913 |
-
"special": true
|
| 1914 |
-
},
|
| 1915 |
-
"128239": {
|
| 1916 |
-
"content": "<|reserved_special_token_231|>",
|
| 1917 |
-
"lstrip": false,
|
| 1918 |
-
"normalized": false,
|
| 1919 |
-
"rstrip": false,
|
| 1920 |
-
"single_word": false,
|
| 1921 |
-
"special": true
|
| 1922 |
-
},
|
| 1923 |
-
"128240": {
|
| 1924 |
-
"content": "<|reserved_special_token_232|>",
|
| 1925 |
-
"lstrip": false,
|
| 1926 |
-
"normalized": false,
|
| 1927 |
-
"rstrip": false,
|
| 1928 |
-
"single_word": false,
|
| 1929 |
-
"special": true
|
| 1930 |
-
},
|
| 1931 |
-
"128241": {
|
| 1932 |
-
"content": "<|reserved_special_token_233|>",
|
| 1933 |
-
"lstrip": false,
|
| 1934 |
-
"normalized": false,
|
| 1935 |
-
"rstrip": false,
|
| 1936 |
-
"single_word": false,
|
| 1937 |
-
"special": true
|
| 1938 |
-
},
|
| 1939 |
-
"128242": {
|
| 1940 |
-
"content": "<|reserved_special_token_234|>",
|
| 1941 |
-
"lstrip": false,
|
| 1942 |
-
"normalized": false,
|
| 1943 |
-
"rstrip": false,
|
| 1944 |
-
"single_word": false,
|
| 1945 |
-
"special": true
|
| 1946 |
-
},
|
| 1947 |
-
"128243": {
|
| 1948 |
-
"content": "<|reserved_special_token_235|>",
|
| 1949 |
-
"lstrip": false,
|
| 1950 |
-
"normalized": false,
|
| 1951 |
-
"rstrip": false,
|
| 1952 |
-
"single_word": false,
|
| 1953 |
-
"special": true
|
| 1954 |
-
},
|
| 1955 |
-
"128244": {
|
| 1956 |
-
"content": "<|reserved_special_token_236|>",
|
| 1957 |
-
"lstrip": false,
|
| 1958 |
-
"normalized": false,
|
| 1959 |
-
"rstrip": false,
|
| 1960 |
-
"single_word": false,
|
| 1961 |
-
"special": true
|
| 1962 |
-
},
|
| 1963 |
-
"128245": {
|
| 1964 |
-
"content": "<|reserved_special_token_237|>",
|
| 1965 |
-
"lstrip": false,
|
| 1966 |
-
"normalized": false,
|
| 1967 |
-
"rstrip": false,
|
| 1968 |
-
"single_word": false,
|
| 1969 |
-
"special": true
|
| 1970 |
-
},
|
| 1971 |
-
"128246": {
|
| 1972 |
-
"content": "<|reserved_special_token_238|>",
|
| 1973 |
-
"lstrip": false,
|
| 1974 |
-
"normalized": false,
|
| 1975 |
-
"rstrip": false,
|
| 1976 |
-
"single_word": false,
|
| 1977 |
-
"special": true
|
| 1978 |
-
},
|
| 1979 |
-
"128247": {
|
| 1980 |
-
"content": "<|reserved_special_token_239|>",
|
| 1981 |
-
"lstrip": false,
|
| 1982 |
-
"normalized": false,
|
| 1983 |
-
"rstrip": false,
|
| 1984 |
-
"single_word": false,
|
| 1985 |
-
"special": true
|
| 1986 |
-
},
|
| 1987 |
-
"128248": {
|
| 1988 |
-
"content": "<|reserved_special_token_240|>",
|
| 1989 |
-
"lstrip": false,
|
| 1990 |
-
"normalized": false,
|
| 1991 |
-
"rstrip": false,
|
| 1992 |
-
"single_word": false,
|
| 1993 |
-
"special": true
|
| 1994 |
-
},
|
| 1995 |
-
"128249": {
|
| 1996 |
-
"content": "<|reserved_special_token_241|>",
|
| 1997 |
-
"lstrip": false,
|
| 1998 |
-
"normalized": false,
|
| 1999 |
-
"rstrip": false,
|
| 2000 |
-
"single_word": false,
|
| 2001 |
-
"special": true
|
| 2002 |
-
},
|
| 2003 |
-
"128250": {
|
| 2004 |
-
"content": "<|reserved_special_token_242|>",
|
| 2005 |
-
"lstrip": false,
|
| 2006 |
-
"normalized": false,
|
| 2007 |
-
"rstrip": false,
|
| 2008 |
-
"single_word": false,
|
| 2009 |
-
"special": true
|
| 2010 |
-
},
|
| 2011 |
-
"128251": {
|
| 2012 |
-
"content": "<|reserved_special_token_243|>",
|
| 2013 |
-
"lstrip": false,
|
| 2014 |
-
"normalized": false,
|
| 2015 |
-
"rstrip": false,
|
| 2016 |
-
"single_word": false,
|
| 2017 |
-
"special": true
|
| 2018 |
-
},
|
| 2019 |
-
"128252": {
|
| 2020 |
-
"content": "<|reserved_special_token_244|>",
|
| 2021 |
-
"lstrip": false,
|
| 2022 |
-
"normalized": false,
|
| 2023 |
-
"rstrip": false,
|
| 2024 |
-
"single_word": false,
|
| 2025 |
-
"special": true
|
| 2026 |
-
},
|
| 2027 |
-
"128253": {
|
| 2028 |
-
"content": "<|reserved_special_token_245|>",
|
| 2029 |
-
"lstrip": false,
|
| 2030 |
-
"normalized": false,
|
| 2031 |
-
"rstrip": false,
|
| 2032 |
-
"single_word": false,
|
| 2033 |
-
"special": true
|
| 2034 |
-
},
|
| 2035 |
-
"128254": {
|
| 2036 |
-
"content": "<|reserved_special_token_246|>",
|
| 2037 |
-
"lstrip": false,
|
| 2038 |
-
"normalized": false,
|
| 2039 |
-
"rstrip": false,
|
| 2040 |
-
"single_word": false,
|
| 2041 |
-
"special": true
|
| 2042 |
-
},
|
| 2043 |
-
"128255": {
|
| 2044 |
-
"content": "<|reserved_special_token_247|>",
|
| 2045 |
-
"lstrip": false,
|
| 2046 |
-
"normalized": false,
|
| 2047 |
-
"rstrip": false,
|
| 2048 |
-
"single_word": false,
|
| 2049 |
-
"special": true
|
| 2050 |
-
}
|
| 2051 |
-
},
|
| 2052 |
"bos_token": "<|begin_of_text|>",
|
| 2053 |
"clean_up_tokenization_spaces": true,
|
| 2054 |
"eos_token": "<|eot_id|>",
|
| 2055 |
-
"
|
| 2056 |
"model_input_names": [
|
| 2057 |
"input_ids",
|
| 2058 |
"attention_mask"
|
| 2059 |
],
|
| 2060 |
"model_max_length": 131072,
|
| 2061 |
"pad_token": "<|eot_id|>",
|
| 2062 |
-
"tokenizer_class": "
|
| 2063 |
}
|
|
|
|
| 1 |
{
|
| 2 |
+
"backend": "tokenizers",
|
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| 3 |
"bos_token": "<|begin_of_text|>",
|
| 4 |
"clean_up_tokenization_spaces": true,
|
| 5 |
"eos_token": "<|eot_id|>",
|
| 6 |
+
"is_local": false,
|
| 7 |
"model_input_names": [
|
| 8 |
"input_ids",
|
| 9 |
"attention_mask"
|
| 10 |
],
|
| 11 |
"model_max_length": 131072,
|
| 12 |
"pad_token": "<|eot_id|>",
|
| 13 |
+
"tokenizer_class": "TokenizersBackend"
|
| 14 |
}
|