13B/README.md

metadata

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

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)

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

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

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

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

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

For Production

# 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.

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