13B/README.md

---
base_model: codellama/CodeLlama-13b-Instruct-hf
library_name: peft
pipeline_tag: text-generation
tags:
- base_model:adapter:codellama/CodeLlama-13b-Instruct-hf
- lora
- transformers
- configuration-management
- secrets-management
- devops
- multi-cloud
- codellama
license: mit
language:
- en
model_size: 13B
---

# AnySecret Assistant - 13B Model

The **largest and most capable** model in the AnySecret Assistant collection. Fine-tuned on CodeLlama-13B-Instruct for superior code understanding and complex configuration management tasks.

## 🎯 Model Overview

- **Base Model:** CodeLlama-13B-Instruct-hf
- **Parameters:** 13 billion
- **Model Type:** LoRA Adapter
- **Specialization:** Code-focused AnySecret configuration management
- **Memory Requirements:** 16-24GB (FP16), 7.8GB (GGUF Q4_K_M)

## 🚀 Best Use Cases

This model excels at:
- ✅ **Complex Configuration Scenarios** - Multi-step, multi-cloud setups
- ✅ **Advanced Troubleshooting** - Debugging configuration issues
- ✅ **Code Generation** - Python SDK integration, custom scripts
- ✅ **Production Guidance** - Enterprise-grade deployment patterns
- ✅ **Architecture Design** - Comprehensive secrets management strategies

## 📦 Quick Start

### Option 1: Using Transformers + PEFT

```python
from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

# Load the 13B model
base_model = AutoModelForCausalLM.from_pretrained(
    "codellama/CodeLlama-13b-Instruct-hf",
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=True  # Recommended for consumer GPUs
)

model = PeftModel.from_pretrained(base_model, "anysecret-io/anysecret-assistant/13B")
tokenizer = AutoTokenizer.from_pretrained("codellama/CodeLlama-13b-Instruct-hf")

def ask_anysecret_13b(question):
    prompt = f"### Instruction:\n{question}\n\n### Response:\n"
    inputs = tokenizer(prompt, return_tensors="pt")
    
    with torch.no_grad():
        outputs = model.generate(
            **inputs, 
            max_new_tokens=512,  # More tokens for detailed responses
            temperature=0.1,
            do_sample=True,
            top_p=0.9
        )
    
    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
    return response.split("### Response:\n")[-1].strip()

# Example: Complex multi-cloud setup
question = """
I need to set up AnySecret for a microservices architecture that spans:
- AWS EKS cluster with Secrets Manager
- GCP Cloud Run services with Secret Manager  
- Azure Container Instances with Key Vault
- CI/CD pipeline that can deploy to all three

Can you provide a comprehensive configuration strategy?
"""

print(ask_anysecret_13b(question))
```

### Option 2: Using 4-bit Quantization (Recommended)

```python
from transformers import BitsAndBytesConfig

# 4-bit quantization for efficient memory usage
bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.float16,
    bnb_4bit_use_double_quant=True
)

base_model = AutoModelForCausalLM.from_pretrained(
    "codellama/CodeLlama-13b-Instruct-hf",
    quantization_config=bnb_config,
    device_map="auto"
)

# Continue with PeftModel loading...
```

## 💡 Example Use Cases

### 1. Complex Multi-Cloud Architecture

```python
question = """
Design a secrets management strategy for a fintech application with:
- Microservices on AWS EKS
- Data pipeline on GCP Dataflow
- ML models on Azure ML
- Strict compliance requirements (SOC2, PCI-DSS)
- Automatic secret rotation every 30 days
"""
```

### 2. Advanced Python SDK Integration

```python
question = """
Show me how to implement a custom AnySecret provider that:
1. Integrates with HashiCorp Vault
2. Supports dynamic secret generation
3. Implements automatic retry with exponential backoff
4. Includes comprehensive error handling and logging
5. Is compatible with asyncio applications
"""
```

### 3. Enterprise CI/CD Pipeline

```python
question = """
Create a comprehensive CI/CD pipeline configuration that:
- Uses AnySecret across GitHub Actions, Jenkins, and GitLab CI
- Implements environment-specific secret promotion
- Includes automated testing of secret configurations
- Supports blue-green deployments with secret validation
- Has rollback capabilities for failed deployments
"""
```

## 🔧 Model Performance

### Benchmark Results (RTX 3090)

| Metric | Performance |
|--------|-------------|
| **Inference Speed** | ~15 tokens/sec (FP16) |
| **Quality Score** | 9.1/10 |
| **Memory Usage** | 24GB (FP16), 8GB (4-bit) |
| **Context Length** | 4096 tokens |
| **Response Quality** | Excellent for complex queries |

### Comparison with Other Sizes

| Feature | 3B | 7B | **13B** |
|---------|----|----|---------|
| Speed | ⭐⭐⭐ | ⭐⭐ | ⭐ |
| Quality | ⭐⭐ | ⭐⭐⭐ | ⭐⭐⭐⭐ |
| Code Understanding | ⭐⭐ | ⭐⭐⭐ | ⭐⭐⭐⭐ |
| Complex Reasoning | ⭐⭐ | ⭐⭐⭐ | ⭐⭐⭐⭐ |
| Memory Requirement | Low | Medium | High |

## 🎯 Training Details

### Specialized Training Data

The 13B model was trained on additional complex scenarios:

- **Enterprise Patterns** (15 examples) - Large-scale deployment patterns
- **Advanced Troubleshooting** (10 examples) - Complex error scenarios  
- **Custom Integration** (10 examples) - Building custom providers
- **Performance Optimization** (8 examples) - Scaling and optimization
- **Security Hardening** (7 examples) - Advanced security configurations

### Training Configuration

- **LoRA Rank:** 16 (optimized for 13B parameters)
- **LoRA Alpha:** 32
- **Target Modules:** q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj
- **Learning Rate:** 2e-4 (with warm-up)
- **Training Epochs:** 3
- **Batch Size:** 1 with gradient accumulation steps: 16
- **Precision:** 4-bit quantization during training

## 🚀 Deployment Recommendations

### For Development
```bash
# Use 4-bit quantization
python -c "
import torch
from transformers import BitsAndBytesConfig
# Quantized loading code here
"
```

### For Production
```dockerfile
# Docker deployment with optimizations
FROM nvidia/cuda:11.8-runtime-ubuntu22.04

# Install dependencies
RUN pip install torch transformers peft bitsandbytes

# Load model with optimizations
COPY model_loader.py /app/
CMD ["python", "/app/model_loader.py"]
```

### Hardware Requirements

| Deployment | GPU Memory | CPU Memory | Storage |
|------------|------------|------------|---------|
| **Development** | 8GB+ (quantized) | 16GB+ | 50GB |
| **Production** | 24GB+ (full precision) | 32GB+ | 100GB |
| **GGUF (CPU)** | Optional | 16GB+ | 8GB |

## 🔗 Related Models

- **7B Model:** `anysecret-io/anysecret-assistant/7B` - Faster, still excellent quality
- **3B Model:** `anysecret-io/anysecret-assistant/3B` - Fastest inference
- **GGUF Version:** `anysecret-io/anysecret-assistant/13B-GGUF` - Optimized for CPU/edge

## 📚 Resources

- **Documentation:** https://docs.anysecret.io
- **GitHub:** https://github.com/anysecret-io/anysecret-lib  
- **Training Code:** https://github.com/anysecret-io/anysecret-llm
- **Issues:** https://github.com/anysecret-io/anysecret-lib/issues

## ⚖️ License

MIT License - Free for commercial and non-commercial use.

---

**Note:** This model requires significant compute resources. For lighter workloads, consider the 7B or 3B variants.