Model save

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+tokenizer.json filter=lfs diff=lfs merge=lfs -text

README.md

@@ -1,396 +1,62 @@
-# Qwen 2.5-Coder 14B - 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-Qwen%202.5%20Coder%2014B-orange.svg)](https://huggingface.co/Qwen/Qwen2.5-Coder-14B-Instruct)
-[![perfecXion.ai](https://img.shields.io/badge/by-perfecXion.ai-purple.svg)](https://perfecxion.ai)
-
-**Enterprise-grade code security - powerful reasoning with production efficiency**
-
-[🤗 Model Card](https://huggingface.co/scthornton/qwen2.5-coder-14b-securecode) | [📊 Dataset](https://huggingface.co/datasets/scthornton/securecode-v2) | [💻 perfecXion.ai](https://perfecxion.ai)
-
-</div>
-
----
-
-## 🎯 What is This?
-
-This is **Qwen 2.5-Coder 14B Instruct** fine-tuned on the **SecureCode v2.0 dataset** - the sweet spot between code intelligence and computational efficiency, now enhanced with production-grade security knowledge.
-
-Qwen 2.5-Coder 14B delivers exceptional code understanding from the same architecture that powers the best-in-class 7B model, scaled up for enterprise complexity. Combined with SecureCode training, this model delivers:
-
-✅ **Advanced security reasoning** across complex codebases
-✅ **Production-ready efficiency** - fits comfortably on single GPU
-✅ **Enterprise-scale analysis** with 128K context window
-✅ **Best-in-class code understanding** at the 14B parameter tier
-
-**The Result:** An enterprise-ready security expert that runs efficiently on standard hardware.
-
-**Why Qwen 2.5-Coder 14B?** This model offers the optimal balance:
-- 🎯 **Superior to smaller models** - More nuanced security analysis than 7B
-- ⚡ **More efficient than 32B+** - 2x faster training, lower deployment cost
-- 🌍 **92 programming languages** - Comprehensive language coverage
-- 📏 **128K context window** - Analyze entire applications at once
-- 🏢 **Enterprise deployable** - Runs on single A100 or 2x RTX 4090
-
----
-
-## 🚨 The Problem This Solves
-
-**AI coding assistants produce vulnerable code in 45% of security-relevant scenarios** (Veracode 2025). While smaller models miss nuanced vulnerabilities and larger models demand excessive resources, the 14B tier delivers the security intelligence enterprises need with the efficiency they demand.
-
-**Real-world enterprise impact:**
-- Equifax breach: **$425 million** settlement + reputation damage
-- Capital One: **100 million** customer records, $80M fine
-- SolarWinds: **18,000** organizations compromised
-
-Qwen 2.5-Coder 14B SecureCode Edition brings advanced security analysis to enterprise-scale codebases without the infrastructure costs of 32B+ models.
-
----
-
-## 💡 Key Features
-
-### 🏆 Enterprise-Scale Code Intelligence
-
-**Qwen 2.5-Coder 14B** delivers exceptional performance:
-- HumanEval: **89.0%** pass@1 (surpasses many 30B+ models)
-- MBPP: **77.6%** pass@1
-- MultiPL-E: **82.1%** average across languages
-- Matches or exceeds 32B models on most benchmarks
-
-Now enhanced with **1,209 security-focused examples** covering OWASP Top 10:2025.
-
-### 🔐 Advanced Security Pattern Recognition
-
-Trained on real-world security incidents:
-- **224 examples** of Broken Access Control vulnerabilities
-- **199 examples** of Authentication Failures
-- **125 examples** of Injection attacks (SQL, Command, XSS)
-- **115 examples** of Cryptographic Failures
-- Complete **OWASP Top 10:2025** coverage
-
-### 🌍 Production-Ready Multi-Language Support
-
-Fine-tuned on security examples across:
-- Python (Django, Flask, FastAPI)
-- JavaScript/TypeScript (Express, NestJS, React)
-- Java (Spring Boot)
-- Go (Gin framework)
-- PHP (Laravel, Symfony)
-- C# (ASP.NET Core)
-- Ruby (Rails)
-- Rust (Actix, Rocket)
-- **Plus 84 more languages from Qwen's base training**
-
-### 📋 Sophisticated Security Analysis
-
-Every response includes:
-1. **Multi-layered vulnerability analysis** with attack chain identification
-2. **Defense-in-depth implementations** with enterprise patterns
-3. **Concrete exploitation demonstrations** proving security flaws
-4. **Operational guidance** including monitoring, logging, and SIEM integration
-
 ---
-
-## 📊 Training Details
-
-| Parameter | Value |
-|-----------|-------|
-| **Base Model** | Qwen/Qwen2.5-Coder-14B-Instruct |
-| **Fine-tuning Method** | LoRA (Low-Rank Adaptation) |
-| **Training Dataset** | [SecureCode v2.0](https://huggingface.co/datasets/scthornton/securecode-v2) |
-| **Dataset Size** | 841 training examples |
-| **Training Epochs** | 3 |
-| **LoRA Rank (r)** | 16 |
-| **LoRA Alpha** | 32 |
-| **Learning Rate** | 2e-4 |
-| **Quantization** | 4-bit (bitsandbytes) |
-| **Trainable Parameters** | ~74M (0.53% of 14B total) |
-| **Total Parameters** | 14B |
-| **Context Window** | 128K tokens (inherited from base) |
-| **GPU Used** | NVIDIA A100 40GB |
-| **Training Time** | ~8 hours (estimated) |
-
-### Training Methodology
-
-**LoRA (Low-Rank Adaptation)** preserves Qwen's exceptional code abilities:
-- Trains only 0.53% of model parameters
-- Maintains SOTA code generation quality
-- Adds security-specific knowledge without catastrophic forgetting
-- Enables deployment with minimal memory overhead
-
-**4-bit Quantization** enables efficient training while maintaining model quality.
-
-**Extended Context:** Qwen's 128K context window allows analyzing entire applications, making it ideal for enterprise security audits.
-
+library_name: peft
+license: apache-2.0
+base_model: Qwen/Qwen2.5-Coder-14B-Instruct
+tags:
+- base_model:adapter:Qwen/Qwen2.5-Coder-14B-Instruct
+- lora
+- transformers
+datasets:
+- securecode-v2
+pipeline_tag: text-generation
+model-index:
+- name: qwen2.5-coder-14b-securecode
+  results: []
 ---
 
-## 🚀 Usage
-
-### Quick Start
-
-```python
-from transformers import AutoModelForCausalLM, AutoTokenizer
-from peft import PeftModel
-
-# Load base model and tokenizer
-base_model = "Qwen/Qwen2.5-Coder-14B-Instruct"
-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/qwen2.5-coder-14b-securecode")
-
-# Analyze enterprise codebase for vulnerabilities
-prompt = """### User:
-Perform a comprehensive security audit of this microservices authentication system:
-
-```python
-# auth-service/middleware.py
-async def verify_token(request):
-    token = request.headers.get('Authorization')
-    if not token:
-        return None
-
-    payload = jwt.decode(token, settings.SECRET_KEY, algorithms=['HS256'])
-    user = await User.get(id=payload['user_id'])
-    return user
-
-# payment-service/api.py
-@app.post('/transfer')
-async def transfer_funds(request):
-    user = await verify_token(request)
-    amount = request.json.get('amount')
-    recipient = request.json.get('recipient_id')
-
-    await process_transfer(user.id, recipient, amount)
-    return {'status': 'success'}
-```
-
-### Assistant:
-"""
-
-inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
-outputs = model.generate(
-    **inputs,
-    max_new_tokens=3072,
-    temperature=0.3,  # Lower temperature for precise analysis
-    top_p=0.95,
-    do_sample=True
-)
+<!-- 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. -->
 
-response = tokenizer.decode(outputs[0], skip_special_tokens=True)
-print(response)
-```
+# qwen2.5-coder-14b-securecode
 
-### Enterprise Deployment (4-bit Quantization)
+This model is a fine-tuned version of [Qwen/Qwen2.5-Coder-14B-Instruct](https://huggingface.co/Qwen/Qwen2.5-Coder-14B-Instruct) on the securecode-v2 dataset.
 
-```python
-from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
-from peft import PeftModel
+## Model description
 
-# 4-bit quantization - runs on 24GB GPU
-bnb_config = BitsAndBytesConfig(
-    load_in_4bit=True,
-    bnb_4bit_use_double_quant=True,
-    bnb_4bit_quant_type="nf4",
-    bnb_4bit_compute_dtype="bfloat16"
-)
+More information needed
 
-base_model = AutoModelForCausalLM.from_pretrained(
-    "Qwen/Qwen2.5-Coder-14B-Instruct",
-    quantization_config=bnb_config,
-    device_map="auto",
-    trust_remote_code=True
-)
+## Intended uses & limitations
 
-model = PeftModel.from_pretrained(base_model, "scthornton/qwen2.5-coder-14b-securecode")
-tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-Coder-14B-Instruct", trust_remote_code=True)
+More information needed
 
-# Production-ready: Runs on RTX 4090, A5000, or A100
-```
+## Training and evaluation data
 
-### Large-Scale Codebase Analysis
+More information needed
 
-```python
-# Analyze multiple related files with 128K context
-files_to_review = {
-    "auth.py": open("backend/auth.py").read(),
-    "middleware.py": open("backend/middleware.py").read(),
-    "models.py": open("backend/models.py").read(),
-}
+## Training procedure
 
-combined_code = "\n\n".join([f"# {name}\n{code}" for name, code in files_to_review.items()])
+### Training hyperparameters
 
-prompt = f"""### User:
-Perform a comprehensive security analysis of this authentication system. Identify:
-1. All OWASP Top 10 vulnerabilities
-2. Attack chains that combine multiple vulnerabilities
-3. Race conditions and timing attacks
-4. Authorization bypass opportunities
+The following hyperparameters were used during training:
+- learning_rate: 0.0002
+- train_batch_size: 1
+- eval_batch_size: 8
+- seed: 42
+- gradient_accumulation_steps: 16
+- 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
 
-```python
-{combined_code}
-```
-
-### Assistant:
-"""
-
-inputs = tokenizer(prompt, return_tensors="pt", truncation=True, max_length=65536).to(model.device)
-outputs = model.generate(**inputs, max_new_tokens=4096, temperature=0.3)
-analysis = tokenizer.decode(outputs[0], skip_special_tokens=True)
-print(analysis)
-```
-
----
-
-## 🎯 Use Cases
-
-### 1. **Enterprise Security Architecture Review**
-Analyze complex multi-service architectures:
-```
-Review this microservices platform for security vulnerabilities, focusing on authentication flows, service-to-service authorization, and data validation boundaries
-```
-
-### 2. **Large Codebase Vulnerability Scanning**
-With 128K context, analyze entire modules:
-```
-Audit this 10,000-line payment processing system for injection attacks, authorization bypasses, and cryptographic failures
-```
-
-### 3. **Advanced Attack Chain Analysis**
-Identify sophisticated multi-step attacks:
-```
-Analyze how an attacker could chain CSRF, XSS, and session fixation to achieve account takeover in this web application
-```
-
-### 4. **Production Security Hardening**
-Get operational security recommendations:
-```
-Design a defense-in-depth security architecture for this e-commerce platform handling 1M+ transactions/day
-```
-
-### 5. **Compliance-Focused Code Generation**
-Generate SOC 2, PCI-DSS, HIPAA-compliant code:
-```
-Create a HIPAA-compliant patient data API with comprehensive audit logging, encryption at rest and in transit, and role-based access control
-```
-
----
-
-## ⚠️ Limitations
-
-### What This Model Does Well
-✅ Complex security reasoning across large codebases
-✅ Multi-file analysis with 128K context window
-✅ Advanced attack chain identification
-✅ Enterprise-scale architecture security review
-✅ Detailed operational guidance
-
-### 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
-
-### Known Characteristics
-- Detailed analysis may generate verbose responses (trained on comprehensive security explanations)
-- Optimized for common vulnerability patterns (OWASP Top 10) vs novel 0-days
-- Best performance on code within OWASP taxonomy
-
----
-
-## 📈 Performance Benchmarks
-
-### Hardware Requirements
-
-**Minimum:**
-- 28GB RAM
-- 20GB GPU VRAM (with 4-bit quantization)
-
-**Recommended:**
-- 48GB RAM
-- 24GB+ GPU (RTX 4090, A5000, A100)
-
-**Inference Speed (on A100 40GB):**
-- ~55 tokens/second (4-bit quantization)
-- ~75 tokens/second (bfloat16)
-
-### Code Generation Benchmarks (Base Qwen 2.5-Coder)
-
-| Benchmark | Score | Rank |
-|-----------|-------|------|
-| HumanEval | 89.0% | #1 in 14B class |
-| MBPP | 77.6% | Top tier |
-| LiveCodeBench | 38.4% | Top 5 overall |
-| MultiPL-E | 82.1% | Best multi-language |
-
-**Performance:** Matches or exceeds many 32B+ models while requiring half the compute.
-
----
-
-## 🔬 Dataset Information
-
-Trained on **[SecureCode v2.0](https://huggingface.co/datasets/scthornton/securecode-v2)**:
-- **1,209 examples** with real CVE grounding
-- **100% incident validation**
-- **OWASP Top 10:2025** complete coverage
-- **Expert security review**
-
----
-
-## 📄 License
-
-**Model:** Apache 2.0 | **Dataset:** CC BY-NC-SA 4.0
-
----
-
-## 📚 Citation
-
-```bibtex
-@misc{thornton2025securecode-qwen14b,
-  title={Qwen 2.5-Coder 14B - SecureCode Edition},
-  author={Thornton, Scott},
-  year={2025},
-  publisher={perfecXion.ai},
-  url={https://huggingface.co/scthornton/qwen2.5-coder-14b-securecode}
-}
-```
-
----
-
-## 🙏 Acknowledgments
-
-- **Alibaba Cloud & Qwen Team** for the exceptional Qwen 2.5-Coder base model
-- **OWASP Foundation** for vulnerability taxonomy
-- **MITRE** for CVE database
-- **Enterprise security community** for real-world validation
-
----
-
-## 🔗 Related Models
-
-- **[llama-3.2-3b-securecode](https://huggingface.co/scthornton/llama-3.2-3b-securecode)** - Most accessible (3B)
-- **[qwen-coder-7b-securecode](https://huggingface.co/scthornton/qwen-coder-7b-securecode)** - Smaller Qwen variant (7B)
-- **[deepseek-coder-6.7b-securecode](https://huggingface.co/scthornton/deepseek-coder-6.7b-securecode)** - Security-optimized (6.7B)
-- **[codellama-13b-securecode](https://huggingface.co/scthornton/codellama-13b-securecode)** - Enterprise trusted (13B)
-- **[starcoder2-15b-securecode](https://huggingface.co/scthornton/starcoder2-15b-securecode)** - Multi-language (15B)
-
-[View Collection](https://huggingface.co/collections/scthornton/securecode)
-
----
+### Training results
 
-<div align="center">
 
-**Built with ❤️ for secure enterprise software development**
 
-[perfecXion.ai](https://perfecxion.ai) | [Contact](mailto:scott@perfecxion.ai)
+### Framework versions
 
-</div>
+- PEFT 0.18.1
+- Transformers 4.57.6
+- Pytorch 2.7.1+cu128
+- Datasets 2.16.0
+- Tokenizers 0.22.2

added_tokens.json

@@ -0,0 +1,24 @@
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+  "<|vision_pad|>": 151654,
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+}

chat_template.jinja

@@ -0,0 +1,54 @@
+{%- if tools %}
+    {{- '<|im_start|>system\n' }}
+    {%- if messages[0]['role'] == 'system' %}
+        {{- messages[0]['content'] }}
+    {%- else %}
+        {{- 'You are Qwen, created by Alibaba Cloud. You are a helpful assistant.' }}
+    {%- endif %}
+    {{- "\n\n# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
+    {%- for tool in tools %}
+        {{- "\n" }}
+        {{- tool | tojson }}
+    {%- endfor %}
+    {{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
+{%- else %}
+    {%- if messages[0]['role'] == 'system' %}
+        {{- '<|im_start|>system\n' + messages[0]['content'] + '<|im_end|>\n' }}
+    {%- else %}
+        {{- '<|im_start|>system\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\n' }}
+    {%- endif %}
+{%- endif %}
+{%- for message in messages %}
+    {%- if (message.role == "user") or (message.role == "system" and not loop.first) or (message.role == "assistant" and not message.tool_calls) %}
+        {{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
+    {%- elif message.role == "assistant" %}
+        {{- '<|im_start|>' + message.role }}
+        {%- if message.content %}
+            {{- '\n' + message.content }}
+        {%- endif %}
+        {%- for tool_call in message.tool_calls %}
+            {%- if tool_call.function is defined %}
+                {%- set tool_call = tool_call.function %}
+            {%- endif %}
+            {{- '\n<tool_call>\n{"name": "' }}
+            {{- tool_call.name }}
+            {{- '", "arguments": ' }}
+            {{- tool_call.arguments | tojson }}
+            {{- '}\n</tool_call>' }}
+        {%- endfor %}
+        {{- '<|im_end|>\n' }}
+    {%- elif message.role == "tool" %}
+        {%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != "tool") %}
+            {{- '<|im_start|>user' }}
+        {%- endif %}
+        {{- '\n<tool_response>\n' }}
+        {{- message.content }}
+        {{- '\n</tool_response>' }}
+        {%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
+            {{- '<|im_end|>\n' }}
+        {%- endif %}
+    {%- endif %}
+{%- endfor %}
+{%- if add_generation_prompt %}
+    {{- '<|im_start|>assistant\n' }}
+{%- endif %}

special_tokens_map.json

@@ -0,0 +1,25 @@
+{
+  "additional_special_tokens": [
+    "<|im_start|>",
+    "<|im_end|>",
+    "<|object_ref_start|>",
+    "<|object_ref_end|>",
+    "<|box_start|>",
+    "<|box_end|>",
+    "<|quad_start|>",
+    "<|quad_end|>",
+    "<|vision_start|>",
+    "<|vision_end|>",
+    "<|vision_pad|>",
+    "<|image_pad|>",
+    "<|video_pad|>"
+  ],
+  "eos_token": {
+    "content": "<|im_end|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": "<|im_end|>"
+}

tokenizer.json

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+version https://git-lfs.github.com/spec/v1
+oid sha256:7ef098fb53e76cfa06a012b3826f5889a5ab693afa875d97c3353ae1edb9a1dc
+size 11422173

tokenizer_config.json

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+{
+  "add_bos_token": false,
+  "add_prefix_space": false,
+  "added_tokens_decoder": {
+    "151643": {
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+    },
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+    "<|im_end|>",
+    "<|object_ref_start|>",
+    "<|object_ref_end|>",
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+    "<|box_end|>",
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+    "<|vision_end|>",
+    "<|vision_pad|>",
+    "<|image_pad|>",
+    "<|video_pad|>"
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+  "clean_up_tokenization_spaces": false,
+  "eos_token": "<|im_end|>",
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+  "pad_token": "<|im_end|>",
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+  "tokenizer_class": "Qwen2Tokenizer",
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+}