Model save

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README.md

@@ -1,684 +1,60 @@
-# CodeGemma 7B - SecureCode Edition
-
-<div align="center">
-
-[![License](https://img.shields.io/badge/license-Apache%202.0-blue.svg)](https://opensource.org/licenses/Apache-2.0)
-[![Training Dataset](https://img.shields.io/badge/dataset-SecureCode%20v2.0-green.svg)](https://huggingface.co/datasets/scthornton/securecode-v2)
-[![Base Model](https://img.shields.io/badge/base-CodeGemma%207B-orange.svg)](https://huggingface.co/google/codegemma-7b-it)
-[![perfecXion.ai](https://img.shields.io/badge/by-perfecXion.ai-purple.svg)](https://perfecxion.ai)
-
-**🔷 Google's code model enhanced with security expertise**
-
-Exceptional instruction following meets security awareness. Perfect for developers who want Google's proven quality with security-first coding.
-
-[🤗 Model Hub](https://huggingface.co/scthornton/codegemma-7b-securecode) | [📊 Dataset](https://huggingface.co/datasets/scthornton/securecode-v2) | [💻 perfecXion.ai](https://perfecxion.ai) | [📚 Collection](https://huggingface.co/collections/scthornton/securecode)
-
-</div>
-
----
-
-## 🎯 Quick Decision Guide
-
-**Choose This Model If:**
-- ✅ You value **Google brand trust** and proven quality
-- ✅ You need **excellent instruction following** for complex security tasks
-- ✅ You want **strong code completion** with security awareness
-- ✅ You're building on **Google Cloud Platform** or Google ecosystem
-- ✅ You need **reliable, consistent responses** from a proven architecture
-- ✅ You prefer **7B efficiency** with Google's engineering quality
-
-**Consider Other Models If:**
-- ⚠️ You need maximum context window (→ Qwen 7B/14B with 128K)
-- ⚠️ You're on very limited hardware (→ Llama 3B)
-- ⚠️ You need enterprise brand diversity (→ IBM Granite, Meta CodeLlama)
-- ⚠️ You want absolute best code understanding (→ Qwen 7B slightly edges out)
-
----
-
-## 📊 Collection Positioning
-
-| Model | Size | Best For | Hardware | Inference Speed | Unique Strength |
-|-------|------|----------|----------|-----------------|-----------------|
-| Llama 3.2 3B | 3B | Consumer deployment | 8GB RAM | ⚡⚡⚡ Fastest | Most accessible |
-| DeepSeek 6.7B | 6.7B | Security-optimized baseline | 16GB RAM | ⚡⚡ Fast | Security architecture |
-| Qwen 7B | 7B | Best code understanding | 16GB RAM | ⚡⚡ Fast | Best-in-class 7B |
-| **CodeGemma 7B** | **7B** | **Google ecosystem** | **16GB RAM** | **⚡⚡ Fast** | **Instruction following, Google quality** |
-| CodeLlama 13B | 13B | Enterprise trust | 24GB RAM | ⚡ Medium | Meta brand, proven |
-| Qwen 14B | 14B | Advanced analysis | 32GB RAM | ⚡ Medium | 128K context window |
-| StarCoder2 15B | 15B | Multi-language specialist | 32GB RAM | ⚡ Medium | 600+ languages |
-| Granite 20B | 20B | Enterprise-scale | 48GB RAM | Medium | IBM trust, largest |
-
-**This Model's Sweet Spot:** Google quality + security expertise. Best for teams who value Google's engineering rigor and want proven, reliable security guidance.
-
----
-
-## 🚨 The Problem This Solves
-
-**AI coding assistants produce vulnerable code in 45% of security-relevant scenarios** (Veracode 2025). While many code models focus on syntax and functionality, they lack security awareness.
-
-**Real-world costs:**
-- **Equifax** (SQL injection): $425 million settlement + brand destruction
-- **Capital One** (SSRF): 100 million customer records, $80M fine
-- **SolarWinds** (authentication bypass): 18,000 organizations compromised
-- **LastPass** (cryptographic failures): 30 million users affected
-
-CodeGemma SecureCode Edition brings Google's renowned engineering quality to secure coding, combining reliable instruction following with comprehensive security knowledge.
-
----
-
-## 💡 What is This?
-
-This is **Google CodeGemma 7B Instruct** fine-tuned on the **SecureCode v2.0 dataset** - Google's specialized code model enhanced with production-grade security expertise covering the complete OWASP Top 10:2025.
-
-CodeGemma is part of Google's Gemma family, built on the same technology powering Google's AI products. It's specifically optimized for code generation with exceptional instruction-following capabilities.
-
-Combined with SecureCode training, this model delivers:
-
-✅ **Excellent instruction following** - Reliably follows complex security requirements
-✅ **Google engineering quality** - Proven architecture from Google AI
-✅ **Strong code completion** - Exceptional at completing partial secure code
-✅ **Consistent, reliable responses** - Predictable behavior for production use
-✅ **Security-first code generation** - Trained on real vulnerability patterns
-
-**The Result:** A code assistant that combines Google's quality with security expertise.
-
-**Why CodeGemma 7B?** This model offers Google's advantages:
-- 🔷 **Google brand trust** - Built by the team behind TensorFlow, BERT, and PaLM
-- 🎯 **Instruction-following excellence** - Consistently follows complex security specifications
-- ⚡ **Production efficiency** - 7B parameters = fast inference
-- 🌍 **Broad language support** - Code generation across major languages
-- 🏢 **GCP integration** - Optimized for Google Cloud Platform deployment
-- ⚖️ **Apache 2.0 licensed** - Full commercial freedom
-
-Perfect for development teams using Google Cloud, organizations valuing Google's engineering culture, and developers who prioritize instruction-following reliability.
-
----
-
-## 🔐 Security Training Coverage
-
-### Real-World Vulnerability Distribution
-
-Trained on 1,209 security examples with real CVE grounding:
-
-| OWASP Category | Examples | Real Incidents |
-|----------------|----------|----------------|
-| **Broken Access Control** | 224 | Equifax, Facebook, Uber |
-| **Authentication Failures** | 199 | SolarWinds, Okta, LastPass |
-| **Injection Attacks** | 125 | Capital One, Yahoo, LinkedIn |
-| **Cryptographic Failures** | 115 | LastPass, Adobe, Dropbox |
-| **Security Misconfiguration** | 98 | Tesla, MongoDB, Elasticsearch |
-| **Vulnerable Components** | 87 | Log4Shell, Heartbleed, Struts |
-| **Identification/Auth Failures** | 84 | Twitter, GitHub, Reddit |
-| **Software/Data Integrity** | 78 | SolarWinds, Codecov, npm |
-| **Logging Failures** | 71 | Various incident responses |
-| **SSRF** | 69 | Capital One, Shopify |
-| **Insecure Design** | 59 | Architectural flaws |
-
-### Multi-Language Support
-
-Fine-tuned on security examples across:
-- **Python** (Django, Flask, FastAPI) - 280 examples
-- **JavaScript/TypeScript** (Express, NestJS, React) - 245 examples
-- **Java** (Spring Boot) - 178 examples
-- **Go** (Gin framework) - 145 examples
-- **PHP** (Laravel, Symfony) - 112 examples
-- **C#** (ASP.NET Core) - 89 examples
-- **Ruby** (Rails) - 67 examples
-- **Rust** (Actix, Rocket) - 45 examples
-- **C/C++** (Memory safety) - 28 examples
-- **Kotlin, Swift** - 20 examples
-
 ---
-
-## 🎯 Deployment Scenarios
-
-### Scenario 1: Google Cloud Platform Integration
-
-**Native integration with GCP services.**
-
-**Platform:** Google Cloud Run, Vertex AI, GKE
-**Hardware:** Cloud TPU, NVIDIA T4/A100
-**Use Case:** Serverless security code generation
-
-**GCP Benefits:**
-- Optimized for Google Cloud infrastructure
-- Seamless Vertex AI integration
-- Cloud Run auto-scaling
-- Integrated monitoring and logging
-
-**ROI:** Reduced deployment complexity on GCP. Natural fit for Google-first organizations.
-
+library_name: peft
+license: gemma
+base_model: google/codegemma-7b-it
+tags:
+- base_model:adapter:google/codegemma-7b-it
+- lora
+- transformers
+pipeline_tag: text-generation
+model-index:
+- name: codegemma-7b-securecode
+  results: []
 ---
 
-### Scenario 2: Secure API Code Generation
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
 
-**Generate production-ready secure APIs with precise specifications.**
+# codegemma-7b-securecode
 
-**Hardware:** Standard cloud instance (16GB RAM)
-**Use Case:** API security automation
-**Strength:** Follows detailed security requirements precisely
+This model is a fine-tuned version of [google/codegemma-7b-it](https://huggingface.co/google/codegemma-7b-it) on the None dataset.
 
-**Example Use Case:**
-```
-Generate a secure REST API for user authentication with:
-- JWT tokens (RS256)
-- Refresh token rotation
-- Rate limiting (10 req/min per IP)
-- Comprehensive audit logging
-- CSRF protection
-```
+## Model description
 
-**Instruction Following:** CodeGemma reliably implements ALL specified requirements, not just some.
-
----
-
-### Scenario 3: Code Review Copilot
-
-**Real-time security suggestions during code review.**
-
-**Platform:** GitHub Copilot alternative, IDE plugins
-**Latency:** <100ms for inline suggestions
-**Use Case:** Security-aware code completion
-
-**Value Proposition:**
-- Suggests secure patterns as developers type
-- Catches vulnerabilities during development
-- Educates developers on security best practices
-- Reduces security debt accumulation
-
----
-
-### Scenario 4: Educational Platform
-
-**Teaching secure coding with Google-quality foundations.**
-
-**Audience:** CS students, bootcamp students, junior developers
-**Platform:** Interactive coding platforms
-**Use Case:** Security education at scale
-
-**Educational Benefits:**
-- Google brand credibility for students
-- Consistent, predictable teaching responses
-- Clear explanations of security concepts
-- Reliable code examples
-
----
-
-## 📊 Training Details
-
-| Parameter | Value | Why This Matters |
-|-----------|-------|------------------|
-| **Base Model** | google/codegemma-7b-it | Google's instruction-tuned code model |
-| **Fine-tuning Method** | LoRA (Low-Rank Adaptation) | Efficient training, preserves base capabilities |
-| **Training Dataset** | [SecureCode v2.0](https://huggingface.co/datasets/scthornton/securecode-v2) | 100% incident-grounded, expert-validated |
-| **Dataset Size** | 841 training examples | Focused on quality over quantity |
-| **Training Epochs** | 3 | Optimal convergence without overfitting |
-| **LoRA Rank (r)** | 16 | Balanced parameter efficiency |
-| **LoRA Alpha** | 32 | Learning rate scaling factor |
-| **Learning Rate** | 2e-4 | Standard for LoRA fine-tuning |
-| **Quantization** | 4-bit (bitsandbytes) | Enables efficient training |
-| **Trainable Parameters** | ~40M (0.57% of 7B total) | Minimal parameters, maximum impact |
-| **Total Parameters** | 7B | Sweet spot for efficiency |
-| **Context Window** | 8K tokens | Standard for code analysis |
-| **GPU Used** | NVIDIA A100 40GB | Enterprise training infrastructure |
-| **Training Time** | ~6 hours (estimated) | Efficient training cycle |
-
-### Training Methodology
-
-**LoRA (Low-Rank Adaptation)** preserves CodeGemma's instruction-following capabilities:
-1. **Efficiency:** Trains only 0.57% of model parameters (40M vs 7B)
-2. **Quality:** Maintains Google's exceptional code generation
-3. **Reliability:** Preserves consistent, predictable behavior
-
-**Google Gemma Foundation:** Built on Google's cutting-edge AI research:
-- State-of-the-art instruction following
-- Optimized for code generation tasks
-- Proven reliability in production
-- Backed by Google AI engineering
-
----
-
-## 🚀 Usage
-
-### Quick Start
-
-```python
-from transformers import AutoModelForCausalLM, AutoTokenizer
-from peft import PeftModel
-
-# Load Google CodeGemma base model
-base_model = "google/codegemma-7b-it"
-model = AutoModelForCausalLM.from_pretrained(
-    base_model,
-    device_map="auto",
-    torch_dtype="auto",
-    trust_remote_code=True
-)
-tokenizer = AutoTokenizer.from_pretrained(base_model, trust_remote_code=True)
-
-# Load SecureCode LoRA adapter
-model = PeftModel.from_pretrained(model, "scthornton/codegemma-7b-securecode")
-
-# Generate secure code with precise requirements
-prompt = """### User:
-Generate a secure user registration endpoint in Python Flask with these exact requirements:
-1. Email validation with regex
-2. Password: minimum 12 chars, complexity requirements
-3. Bcrypt hashing (cost factor 12)
-4. Rate limiting: 5 attempts per 15 minutes per IP
-5. CSRF token validation
-6. SQL injection prevention via parameterized queries
-7. Comprehensive audit logging to Stackdriver
-8. Return JSON with proper status codes
-
-### Assistant:
-"""
-
-inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
-outputs = model.generate(
-    **inputs,
-    max_new_tokens=2048,
-    temperature=0.7,
-    top_p=0.95,
-    do_sample=True
-)
-
-response = tokenizer.decode(outputs[0], skip_special_tokens=True)
-print(response)
-```
-
----
-
-### GCP Deployment (Vertex AI)
-
-```python
-from google.cloud import aiplatform
-from transformers import AutoModelForCausalLM
-from peft import PeftModel
-
-# Initialize Vertex AI
-aiplatform.init(project='your-project', location='us-central1')
-
-# Deploy CodeGemma SecureCode to Vertex AI
-model = AutoModelForCausalLM.from_pretrained("google/codegemma-7b-it", device_map="auto")
-model = PeftModel.from_pretrained(model, "scthornton/codegemma-7b-securecode")
-
-# Upload to Vertex AI Model Registry
-# Deploy as endpoint for production use
-# Integrate with Cloud Run, GKE, or other GCP services
-```
-
----
-
-### Production Deployment (4-bit Quantization)
-
-```python
-from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
-from peft import PeftModel
-
-# 4-bit quantization - runs on 16GB GPU
-bnb_config = BitsAndBytesConfig(
-    load_in_4bit=True,
-    bnb_4bit_use_double_quant=True,
-    bnb_4bit_quant_type="nf4",
-    bnb_4bit_compute_dtype="bfloat16"
-)
-
-model = AutoModelForCausalLM.from_pretrained(
-    "google/codegemma-7b-it",
-    quantization_config=bnb_config,
-    device_map="auto",
-    trust_remote_code=True
-)
-
-model = PeftModel.from_pretrained(model, "scthornton/codegemma-7b-securecode")
-tokenizer = AutoTokenizer.from_pretrained("google/codegemma-7b-it", trust_remote_code=True)
-
-# Production-ready: Runs on RTX 3090, RTX 4080, A5000, or GCP T4
-```
-
----
+More information needed
 
-## 📈 Performance & Benchmarks
+## Intended uses & limitations
 
-### Hardware Requirements
+More information needed
 
-| Deployment | RAM | GPU VRAM | Tokens/Second | Latency (2K response) | Cost/Month |
-|-----------|-----|----------|---------------|----------------------|------------|
-| **4-bit Quantized** | 16GB | 12GB | ~40 tok/s | ~50 seconds | $0 (local) or $50-100 (cloud) |
-| **8-bit Quantized** | 20GB | 16GB | ~50 tok/s | ~40 seconds | $0 (local) or $100-150 (cloud) |
-| **Full Precision (bf16)** | 28GB | 20GB | ~65 tok/s | ~31 seconds | $0 (local) or $200-300 (cloud) |
-| **GCP Vertex AI** | Managed | Managed | ~60 tok/s | ~33 seconds | $150-250 (pay-per-use) |
+## Training and evaluation data
 
-**GCP Integration Winner:** Native Vertex AI deployment with Google's infrastructure optimization.
+More information needed
 
-### Real-World Performance
+## Training procedure
 
-**Tested on RTX 3090 24GB** (consumer/prosumer GPU):
-- **Tokens/second:** ~40 tok/s (4-bit), ~60 tok/s (full precision)
-- **Cold start:** ~3 seconds
-- **Memory usage:** 10GB (4-bit), 16GB (full precision)
-- **Instruction following:** Excellent - implements 95%+ of specified requirements
+### Training hyperparameters
 
-**Tested on GCP T4 GPU** (cloud deployment):
-- **Tokens/second:** ~35 tok/s (optimized for cost)
-- **Auto-scaling:** 0 to 100 instances in <60 seconds
-- **Cost efficiency:** $0.35/hour per instance
+The following hyperparameters were used during training:
+- learning_rate: 0.0002
+- train_batch_size: 2
+- eval_batch_size: 8
+- seed: 42
+- gradient_accumulation_steps: 8
+- total_train_batch_size: 16
+- optimizer: Use paged_adamw_8bit with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
+- lr_scheduler_type: cosine
+- lr_scheduler_warmup_steps: 100
+- num_epochs: 3
 
-### Code Generation Quality
+### Training results
 
-**Instruction Following Benchmark:**
-- **Requirement compliance:** 95% (implements specified requirements accurately)
-- **Security specification adherence:** Excellent
-- **Consistency:** High - predictable, reliable outputs
 
----
-
-## 💰 Cost Analysis
-
-### Total Cost of Ownership (TCO) - 1 Year
-
-**Option 1: GCP Vertex AI (Recommended for GCP Users)**
-- Deployment: Managed Vertex AI endpoint
-- Cost: ~$0.50/hour (auto-scaling)
-- Usage: 500 hours/month
-- **Total Year 1:** $3,000/year
-
-**Option 2: Self-Hosted (Cloud GPU)**
-- GCP n1-highmem-8 + T4 GPU: $0.55/hour
-- Usage: 160 hours/month (development team)
-- **Total Year 1:** $1,056/year
-
-**Option 3: Self-Hosted (Local GPU)**
-- Hardware: RTX 3090 24GB - $1,000-1,200 (one-time)
-- Electricity: ~$60/year
-- **Total Year 1:** $1,060-1,260
-- **Total Year 2+:** $60/year
-
-**Option 4: Google Gemini API (for comparison)**
-- Cost: Variable pricing
-- Typical usage: $1,500-3,000/year for team
-- **Total Year 1:** $1,500-3,000/year
-
-**ROI Winner:** GCP Vertex AI for Google-first orgs (native integration). Local GPU for multi-cloud or cost optimization.
-
----
-
-## 🎯 Use Cases & Examples
-
-### 1. Secure API Generation with Precise Specifications
-
-Generate APIs that exactly match security requirements:
-
-```python
-prompt = """### User:
-Create a secure payment processing API endpoint in Node.js/Express with:
-- Input validation using Joi
-- PCI-DSS compliant data handling
-- Stripe integration with webhook verification
-- Idempotency key support
-- Comprehensive error handling
-- Rate limiting (100 req/min)
-- Request/response logging to Stackdriver
-
-### Assistant:
-"""
-```
-
-**Model Response:** Generates complete, production-ready code implementing ALL specified requirements.
-
----
-
-### 2. Security Code Review with Structured Output
-
-Review code with predictable, structured responses:
-
-```python
-prompt = """### User:
-Review this authentication code for OWASP Top 10 vulnerabilities. Provide output in this exact format:
-1. Vulnerability Type
-2. Severity (Critical/High/Medium/Low)
-3. Affected Code Line
-4. Exploitation Scenario
-5. Secure Alternative
-6. OWASP Category
-
-[Code to review]
-
-### Assistant:
-"""
-```
-
-**Model Response:** Follows the exact format specified, reliable structured output.
-
----
-
-### 3. Educational Content Generation
-
-Generate consistent educational examples:
-
-```python
-prompt = """### User:
-Create a teaching example showing SQL injection vulnerability and fix. Include:
-1. Vulnerable code with clear comments
-2. Attack demonstration
-3. Secure code with parameterized queries
-4. Explanation suitable for beginners
-5. Practice exercise
-
-### Assistant:
-"""
-```
-
-**Model Response:** Generates clear, educational content following Google's technical writing standards.
-
----
-
-## ⚠️ Limitations & Transparency
-
-### What This Model Does Well
-✅ Excellent instruction following for security requirements
-✅ Consistent, predictable responses (Google quality)
-✅ Strong code completion with security awareness
-✅ Reliable implementation of specified security controls
-✅ Clear, well-structured code generation
-✅ Native GCP integration
-
-### What This Model Doesn't Do
-❌ **Not a security scanner** - Use tools like Semgrep, CodeQL, or Snyk
-❌ **Not a penetration testing tool** - Cannot perform active exploitation
-❌ **Not legal/compliance advice** - Consult security professionals
-❌ **Not a replacement for security experts** - Critical systems need professional review
-❌ **Not the largest context window** - 8K tokens (vs Qwen's 128K)
-
-### Known Characteristics
-- **Instruction-focused:** Excels when given clear, structured requirements
-- **Consistent outputs:** Highly predictable - good for automation
-- **Google ecosystem:** Best performance when deployed on GCP
-- **Standard context:** 8K tokens sufficient for most code files
-
-### Appropriate Use
-✅ API generation with precise security requirements
-✅ Code completion and IDE integration
-✅ Educational platforms and training
-✅ GCP-based development workflows
-✅ Teams valuing Google engineering culture
-
-### Inappropriate Use
-❌ Sole security validation for production systems
-❌ Replacement for professional security audits
-❌ Active penetration testing without authorization
-❌ Very large codebase analysis (use Qwen 14B instead)
-
----
-
-## 🔬 Dataset Information
-
-This model was trained on **[SecureCode v2.0](https://huggingface.co/datasets/scthornton/securecode-v2)**, a production-grade security dataset with:
-
-- **1,209 total examples** (841 train / 175 validation / 193 test)
-- **100% incident grounding** - every example tied to real CVEs or security breaches
-- **11 vulnerability categories** - complete OWASP Top 10:2025 coverage
-- **11 programming languages** - from Python to Rust
-- **4-turn conversational structure** - mirrors real developer-AI workflows
-- **100% expert validation** - reviewed by independent security professionals
-
-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.
-
----
-
-## 🏢 About perfecXion.ai
-
-[perfecXion.ai](https://perfecxion.ai) is dedicated to advancing AI security through research, datasets, and production-grade security tooling.
-
-**Connect:**
-- Website: [perfecxion.ai](https://perfecxion.ai)
-- Research: [perfecxion.ai/research](https://perfecxion.ai/research)
-- Knowledge Hub: [perfecxion.ai/knowledge](https://perfecxion.ai/knowledge)
-- GitHub: [@scthornton](https://github.com/scthornton)
-- HuggingFace: [@scthornton](https://huggingface.co/scthornton)
-- Email: scott@perfecxion.ai
-
----
-
-## 📄 License
-
-**Model License:** Apache 2.0 (permissive - use in commercial applications)
-**Dataset License:** CC BY-NC-SA 4.0 (non-commercial with attribution)
-
-### What You CAN Do
-✅ Use this model commercially in production applications
-✅ Fine-tune further for your specific use case
-✅ Deploy in enterprise environments
-✅ Integrate into commercial products
-✅ Distribute and modify the model weights
-✅ Charge for services built on this model
-
-### What You CANNOT Do with the Dataset
-❌ Sell or redistribute the raw SecureCode v2.0 dataset commercially
-❌ Use the dataset to train commercial models without releasing under the same license
-❌ Remove attribution or claim ownership of the dataset
-
-For commercial dataset licensing or custom training, contact: scott@perfecxion.ai
-
----
-
-## 📚 Citation
-
-If you use this model in your research or applications, please cite:
-
-```bibtex
-@misc{thornton2025securecode-codegemma7b,
-  title={CodeGemma 7B - SecureCode Edition},
-  author={Thornton, Scott},
-  year={2025},
-  publisher={perfecXion.ai},
-  url={https://huggingface.co/scthornton/codegemma-7b-securecode},
-  note={Fine-tuned on SecureCode v2.0: https://huggingface.co/datasets/scthornton/securecode-v2}
-}
-
-@misc{thornton2025securecode-dataset,
-  title={SecureCode v2.0: A Production-Grade Dataset for Training Security-Aware Code Generation Models},
-  author={Thornton, Scott},
-  year={2025},
-  month={January},
-  publisher={perfecXion.ai},
-  url={https://perfecxion.ai/articles/securecode-v2-dataset-paper.html},
-  note={Dataset: https://huggingface.co/datasets/scthornton/securecode-v2}
-}
-```
-
----
-
-## 🙏 Acknowledgments
-
-- **Google DeepMind & Google AI** for the excellent CodeGemma base model
-- **OWASP Foundation** for maintaining the Top 10 vulnerability taxonomy
-- **MITRE Corporation** for the CVE database and vulnerability research
-- **Security research community** for responsible disclosure practices
-- **Hugging Face** for model hosting and inference infrastructure
-- **GCP users** who validated this model in production environments
-
----
-
-## 🤝 Contributing
-
-Found a security issue or have suggestions for improvement?
-
-- 🐛 **Report issues:** [GitHub Issues](https://github.com/scthornton/securecode-models/issues)
-- 💬 **Discuss improvements:** [HuggingFace Discussions](https://huggingface.co/scthornton/codegemma-7b-securecode/discussions)
-- 📧 **Contact:** scott@perfecxion.ai
-
-### Community Contributions Welcome
-
-Especially interested in:
-- **GCP deployment examples** and Vertex AI integrations
-- **Benchmark evaluations** on security datasets
-- **Instruction-following assessments** for security tasks
-- **Production deployment case studies**
-- **Performance optimization** for GCP infrastructure
-
----
-
-## 🔗 SecureCode Model Collection
-
-Explore other SecureCode fine-tuned models optimized for different use cases:
-
-### Entry-Level Models (3-7B)
-- **[llama-3.2-3b-securecode](https://huggingface.co/scthornton/llama-3.2-3b-securecode)**
-  - **Best for:** Consumer hardware, IDE integration, education
-  - **Hardware:** 8GB RAM minimum
-  - **Unique strength:** Most accessible
-
-- **[deepseek-coder-6.7b-securecode](https://huggingface.co/scthornton/deepseek-coder-6.7b-securecode)**
-  - **Best for:** Security-optimized baseline
-  - **Hardware:** 16GB RAM
-  - **Unique strength:** Security-first architecture
-
-- **[qwen2.5-coder-7b-securecode](https://huggingface.co/scthornton/qwen2.5-coder-7b-securecode)**
-  - **Best for:** Best code understanding in 7B class
-  - **Hardware:** 16GB RAM
-  - **Unique strength:** 128K context, best-in-class
-
-- **[codegemma-7b-securecode](https://huggingface.co/scthornton/codegemma-7b-securecode)** ⭐ (YOU ARE HERE)
-  - **Best for:** Google ecosystem, instruction following
-  - **Hardware:** 16GB RAM
-  - **Unique strength:** Google quality, GCP integration
-
-### Mid-Range Models (13-15B)
-- **[codellama-13b-securecode](https://huggingface.co/scthornton/codellama-13b-securecode)**
-  - **Best for:** Enterprise trust, Meta brand
-  - **Hardware:** 24GB RAM
-  - **Unique strength:** Proven track record
-
-- **[qwen2.5-coder-14b-securecode](https://huggingface.co/scthornton/qwen2.5-coder-14b-securecode)**
-  - **Best for:** Advanced code analysis
-  - **Hardware:** 32GB RAM
-  - **Unique strength:** 128K context window
-
-- **[starcoder2-15b-securecode](https://huggingface.co/scthornton/starcoder2-15b-securecode)**
-  - **Best for:** Multi-language projects (600+ languages)
-  - **Hardware:** 32GB RAM
-  - **Unique strength:** Broadest language support
-
-### Enterprise-Scale Models (20B+)
-- **[granite-20b-code-securecode](https://huggingface.co/scthornton/granite-20b-code-securecode)**
-  - **Best for:** Enterprise-scale, IBM trust
-  - **Hardware:** 48GB RAM
-  - **Unique strength:** Largest model, deepest analysis
-
-**View Complete Collection:** [SecureCode Models](https://huggingface.co/collections/scthornton/securecode)
-
----
-
-<div align="center">
-
-**Built with ❤️ for secure software development**
-
-[perfecXion.ai](https://perfecxion.ai) | [Research](https://perfecxion.ai/research) | [Knowledge Hub](https://perfecxion.ai/knowledge) | [Contact](mailto:scott@perfecxion.ai)
-
----
 
-*Google quality. Security expertise. Production ready.*
+### Framework versions
 
-</div>
+- PEFT 0.18.1
+- Transformers 4.57.6
+- Pytorch 2.7.1+cu128
+- Datasets 4.5.0
+- Tokenizers 0.22.2

chat_template.jinja

@@ -0,0 +1,4 @@
+{{ bos_token }}{% if messages[0]['role'] == 'system' %}{{ raise_exception('System role not supported') }}{% endif %}{% for message in messages %}{% if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}{{ raise_exception('Conversation roles must alternate user/assistant/user/assistant/...') }}{% endif %}{% if (message['role'] == 'assistant') %}{% set role = 'model' %}{% else %}{% set role = message['role'] %}{% endif %}{{ '<start_of_turn>' + role + '
+' + message['content'] | trim + '<end_of_turn>
+' }}{% endfor %}{% if add_generation_prompt %}{{'<start_of_turn>model
+'}}{% endif %}

special_tokens_map.json

@@ -0,0 +1,24 @@
+{
+  "bos_token": {
+    "content": "<bos>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "<eos>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": "<eos>",
+  "unk_token": {
+    "content": "<unk>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:65385b921a0ca43424830b8eb602b785c9b5f3a019a70cf73a27b0ecf2115695
+size 34363182

tokenizer_config.json

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