Upload README.md with huggingface_hub

README.md

@@ -1,207 +1,207 @@
 ---
+license: other
 base_model: deepseek-ai/deepseek-coder-6.7b-instruct
+tags:
+  - security
+  - cybersecurity
+  - secure-coding
+  - ai-security
+  - owasp
+  - code-generation
+  - qlora
+  - lora
+  - fine-tuned
+  - securecode
+datasets:
+  - scthornton/securecode
 library_name: peft
 pipeline_tag: text-generation
-tags:
-- base_model:adapter:deepseek-ai/deepseek-coder-6.7b-instruct
-- lora
-- transformers
+language:
+  - code
+  - en
 ---
 
-# Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
-
-
-
-## Model Details
-
-### Model Description
-
-<!-- Provide a longer summary of what this model is. -->
-
-
-
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
-
-### Model Sources [optional]
-
-<!-- Provide the basic links for the model. -->
-
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+# DeepSeek Coder 6.7B SecureCode
 
-## Uses
+<div align="center">
 
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+![Parameters](https://img.shields.io/badge/params-6.7B-blue.svg)
+![Dataset](https://img.shields.io/badge/dataset-2,185_examples-green.svg)
+![OWASP](https://img.shields.io/badge/OWASP-Top_10_2021_+_LLM_Top_10_2025-orange.svg)
+![Method](https://img.shields.io/badge/method-QLoRA_4--bit-purple.svg)
 
-### Direct Use
+**Security-specialized code model fine-tuned on the [SecureCode](https://huggingface.co/datasets/scthornton/securecode) dataset**
 
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+[Dataset](https://huggingface.co/datasets/scthornton/securecode) | [Paper (arXiv:2512.18542)](https://arxiv.org/abs/2512.18542) | [Model Collection](https://huggingface.co/collections/scthornton/securecode) | [perfecXion.ai](https://perfecxion.ai)
 
-[More Information Needed]
+</div>
 
-### Downstream Use [optional]
-
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-
-[More Information Needed]
-
-### Out-of-Scope Use
-
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-[More Information Needed]
-
-## Bias, Risks, and Limitations
-
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
+---
 
-### Recommendations
+## What This Model Does
 
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+This model generates **secure code** when developers ask about building features. Instead of producing vulnerable implementations (like 45% of AI-generated code does), it:
 
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+- Identifies the security risks in common coding patterns
+- Provides vulnerable *and* secure implementations side by side
+- Explains how attackers would exploit the vulnerability
+- Includes defense-in-depth guidance: logging, monitoring, SIEM integration, infrastructure hardening
 
-## How to Get Started with the Model
+The model was fine-tuned on **2,185 security training examples** covering both traditional web security (OWASP Top 10 2021) and AI/ML security (OWASP LLM Top 10 2025).
 
-Use the code below to get started with the model.
+## Model Details
 
-[More Information Needed]
+| | |
+|---|---|
+| **Base Model** | [DeepSeek Coder 6.7B Instruct](https://huggingface.co/deepseek-ai/deepseek-coder-6.7b-instruct) |
+| **Parameters** | 6.7B |
+| **Architecture** | DeepSeek |
+| **Tier** | Tier 2: Mid-size Code Specialist |
+| **Method** | QLoRA (4-bit NormalFloat quantization) |
+| **LoRA Rank** | 16 (alpha=32) |
+| **Target Modules** | `q_proj, k_proj, v_proj, o_proj` (4 modules) |
+| **Training Data** | [scthornton/securecode](https://huggingface.co/datasets/scthornton/securecode) (2,185 examples) |
+| **Hardware** | NVIDIA A100 40GB |
+
+Strong code generation model with excellent fill-in-the-middle capabilities. Competitive with larger models on coding benchmarks.
+
+## Quick Start
+
+```python
+from peft import PeftModel
+from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
+import torch
+
+# Load with 4-bit quantization (matches training)
+bnb_config = BitsAndBytesConfig(
+    load_in_4bit=True,
+    bnb_4bit_quant_type="nf4",
+    bnb_4bit_compute_dtype=torch.bfloat16,
+)
+
+base_model = AutoModelForCausalLM.from_pretrained(
+    "deepseek-ai/deepseek-coder-6.7b-instruct",
+    quantization_config=bnb_config,
+    device_map="auto",
+)
+tokenizer = AutoTokenizer.from_pretrained("scthornton/deepseek-coder-6.7b-securecode")
+model = PeftModel.from_pretrained(base_model, "scthornton/deepseek-coder-6.7b-securecode")
+
+# Ask a security-relevant coding question
+messages = [
+    {"role": "user", "content": "How do I implement JWT authentication with refresh tokens in Python?"}
+]
+
+inputs = tokenizer.apply_chat_template(messages, return_tensors="pt").to(model.device)
+outputs = model.generate(inputs, max_new_tokens=2048, temperature=0.7)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+```
 
 ## Training Details
 
-### Training Data
-
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
-
-[More Information Needed]
-
-### Training Procedure
-
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-
-#### Preprocessing [optional]
-
-[More Information Needed]
-
-
-#### Training Hyperparameters
-
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-
-#### Speeds, Sizes, Times [optional]
-
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-
-[More Information Needed]
-
-## Evaluation
-
-<!-- This section describes the evaluation protocols and provides the results. -->
-
-### Testing Data, Factors & Metrics
-
-#### Testing Data
-
-<!-- This should link to a Dataset Card if possible. -->
-
-[More Information Needed]
-
-#### Factors
-
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-
-[More Information Needed]
-
-#### Metrics
-
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
-
-### Results
-
-[More Information Needed]
-
-#### Summary
-
-
-
-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
-
-## Environmental Impact
-
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+### Dataset
 
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+Trained on the full **[SecureCode](https://huggingface.co/datasets/scthornton/securecode)** unified dataset:
 
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
+- **2,185 total examples** (1,435 web security + 750 AI/ML security)
+- **20 vulnerability categories** across OWASP Top 10 2021 and OWASP LLM Top 10 2025
+- **12+ programming languages** and **49+ frameworks**
+- **4-turn conversational structure**: feature request, vulnerable/secure implementations, advanced probing, operational guidance
+- **100% incident grounding**: every example tied to real CVEs, vendor advisories, or published attack research
 
-## Technical Specifications [optional]
+### Hyperparameters
 
-### Model Architecture and Objective
+| Parameter | Value |
+|-----------|-------|
+| LoRA rank | 16 |
+| LoRA alpha | 32 |
+| LoRA dropout | 0.05 |
+| Target modules | 4 linear layers |
+| Quantization | 4-bit NormalFloat (NF4) |
+| Learning rate | 2e-4 |
+| LR scheduler | Cosine with 100-step warmup |
+| Epochs | 3 |
+| Per-device batch size | 2 |
+| Gradient accumulation | 8x |
+| Effective batch size | 16 |
+| Max sequence length | 4096 tokens |
+| Optimizer | paged_adamw_8bit |
+| Precision | bf16 |
 
-[More Information Needed]
+**Notes:** Compact LoRA targeting attention layers only (4 modules). Extended 4096-token context.
 
-### Compute Infrastructure
+## Security Coverage
 
-[More Information Needed]
+### Web Security (1,435 examples)
 
-#### Hardware
+OWASP Top 10 2021: Broken Access Control, Cryptographic Failures, Injection, Insecure Design, Security Misconfiguration, Vulnerable Components, Authentication Failures, Software Integrity Failures, Logging/Monitoring Failures, SSRF.
 
-[More Information Needed]
+Languages: Python, JavaScript, Java, Go, PHP, C#, TypeScript, Ruby, Rust, Kotlin, YAML.
 
-#### Software
+### AI/ML Security (750 examples)
 
-[More Information Needed]
+OWASP LLM Top 10 2025: Prompt Injection, Sensitive Information Disclosure, Supply Chain Vulnerabilities, Data/Model Poisoning, Improper Output Handling, Excessive Agency, System Prompt Leakage, Vector/Embedding Weaknesses, Misinformation, Unbounded Consumption.
 
-## Citation [optional]
+Frameworks: LangChain, OpenAI, Anthropic, HuggingFace, LlamaIndex, ChromaDB, Pinecone, FastAPI, Flask, vLLM, CrewAI, and 30+ more.
 
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+## SecureCode Model Collection
 
-**BibTeX:**
+This model is part of the **SecureCode** collection of 8 security-specialized models:
 
-[More Information Needed]
+| Model | Base | Size | Tier | HuggingFace |
+|-------|------|------|------|-------------|
+| Llama 3.2 SecureCode | meta-llama/Llama-3.2-3B-Instruct | 3B | Accessible | [`llama-3.2-3b-securecode`](https://huggingface.co/scthornton/llama-3.2-3b-securecode) |
+| Qwen2.5 Coder SecureCode | Qwen/Qwen2.5-Coder-7B-Instruct | 7B | Mid-size | [`qwen2.5-coder-7b-securecode`](https://huggingface.co/scthornton/qwen2.5-coder-7b-securecode) |
+| DeepSeek Coder SecureCode | deepseek-ai/deepseek-coder-6.7b-instruct | 6.7B | Mid-size | [`deepseek-coder-6.7b-securecode`](https://huggingface.co/scthornton/deepseek-coder-6.7b-securecode) |
+| CodeGemma SecureCode | google/codegemma-7b-it | 7B | Mid-size | [`codegemma-7b-securecode`](https://huggingface.co/scthornton/codegemma-7b-securecode) |
+| CodeLlama SecureCode | codellama/CodeLlama-13b-Instruct-hf | 13B | Large | [`codellama-13b-securecode`](https://huggingface.co/scthornton/codellama-13b-securecode) |
+| Qwen2.5 Coder 14B SecureCode | Qwen/Qwen2.5-Coder-14B-Instruct | 14B | Large | [`qwen2.5-coder-14b-securecode`](https://huggingface.co/scthornton/qwen2.5-coder-14b-securecode) |
+| StarCoder2 SecureCode | bigcode/starcoder2-15b-instruct-v0.1 | 15B | Large | [`starcoder2-15b-securecode`](https://huggingface.co/scthornton/starcoder2-15b-securecode) |
+| Granite 20B Code SecureCode | ibm-granite/granite-20b-code-instruct-8k | 20B | XL | [`granite-20b-code-securecode`](https://huggingface.co/scthornton/granite-20b-code-securecode) |
 
-**APA:**
+Choose based on your deployment constraints: **3B** for edge/mobile, **7B** for general use, **13B-15B** for deeper reasoning, **20B** for maximum capability.
 
-[More Information Needed]
+## SecureCode Dataset Family
 
-## Glossary [optional]
+| Dataset | Examples | Focus | Link |
+|---------|----------|-------|------|
+| **SecureCode** | 2,185 | Unified (web + AI/ML) | [scthornton/securecode](https://huggingface.co/datasets/scthornton/securecode) |
+| SecureCode Web | 1,435 | Web security (OWASP Top 10 2021) | [scthornton/securecode-web](https://huggingface.co/datasets/scthornton/securecode-web) |
+| SecureCode AI/ML | 750 | AI/ML security (OWASP LLM Top 10 2025) | [scthornton/securecode-aiml](https://huggingface.co/datasets/scthornton/securecode-aiml) |
 
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+## Intended Use
 
-[More Information Needed]
+**Use this model for:**
+- Training AI coding assistants to write secure code
+- Security education and training
+- Vulnerability research and secure code review
+- Building security-aware development tools
 
-## More Information [optional]
+**Do not use this model for:**
+- Offensive exploitation or automated attack generation
+- Circumventing security controls
+- Any activity that violates the base model's license
 
-[More Information Needed]
+## Citation
 
-## Model Card Authors [optional]
+```bibtex
+@misc{thornton2026securecode,
+  title={SecureCode: A Production-Grade Multi-Turn Dataset for Training Security-Aware Code Generation Models},
+  author={Thornton, Scott},
+  year={2026},
+  publisher={perfecXion.ai},
+  url={https://huggingface.co/datasets/scthornton/securecode},
+  note={arXiv:2512.18542}
+}
+```
 
-[More Information Needed]
+## Links
 
-## Model Card Contact
+- **Dataset**: [scthornton/securecode](https://huggingface.co/datasets/scthornton/securecode)
+- **Research Paper**: [arXiv:2512.18542](https://arxiv.org/abs/2512.18542)
+- **Model Collection**: [huggingface.co/collections/scthornton/securecode](https://huggingface.co/collections/scthornton/securecode)
+- **Author**: [perfecXion.ai](https://perfecxion.ai)
 
-[More Information Needed]
-### Framework versions
+## License
 
-- PEFT 0.18.1
+This model is released under the **other** license (inherited from the base model). The training dataset ([SecureCode](https://huggingface.co/datasets/scthornton/securecode)) is licensed under **CC BY-NC-SA 4.0**.