anysecret-io/anysecret-assistant

AnySecret Assistant - Multi-Model Collection

A specialized AI assistant collection for AnySecret configuration management, available in multiple sizes and formats optimized for different use cases and deployment scenarios.

🚀 Available Models

Model Variants

PyTorch Models (LoRA Adapters)

• —anysecret-io/anysecret-assistant/3B/ - Llama-3.2-3B base
• —anysecret-io/anysecret-assistant/7B/ - CodeLlama-7B base
• —anysecret-io/anysecret-assistant/13B/ - CodeLlama-13B base

GGUF Models (Quantized)

• —anysecret-io/anysecret-assistant/3B-GGUF/ - Q4KM, Q8_0 formats
• —anysecret-io/anysecret-assistant/7B-GGUF/ - Q4KM, Q8_0 formats
• —anysecret-io/anysecret-assistant/13B-GGUF/ - Q4KM, Q8_0 formats

🎯 Model Description

These models are fine-tuned specifically to assist with AnySecret configuration management across AWS, GCP, Azure, and Kubernetes environments. Each model can help with CLI commands, configuration setup, CI/CD integration, and Python SDK usage.

• —Developed by: anysecret-io
• —Model type: Causal Language Model (LoRA Adapters + GGUF)
• —Language(s): English
• —License: MIT
• —Specialized for: Multi-cloud secrets and configuration management

📦 Quick Start

Option 1: Using Ollama (Recommended for GGUF)

# 7B model (balanced performance)
ollama pull anysecret-io/anysecret-assistant/7B-GGUF
ollama run anysecret-io/anysecret-assistant/7B-GGUF

# 13B model (best quality)
ollama pull anysecret-io/anysecret-assistant/13B-GGUF
ollama run anysecret-io/anysecret-assistant/13B-GGUF

Option 2: Using Transformers (PyTorch)

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

# Choose your model size (3B/7B/13B)
model_size = "7B"  # or "3B", "13B"
base_models = {
    "3B": "meta-llama/Llama-3.2-3B-Instruct",
    "7B": "codellama/CodeLlama-7b-Instruct-hf",
    "13B": "codellama/CodeLlama-13b-Instruct-hf"
}

base_model_name = base_models[model_size]
adapter_path = f"anysecret-io/anysecret-assistant/{model_size}"

# Load model
base_model = AutoModelForCausalLM.from_pretrained(
    base_model_name,
    torch_dtype=torch.float16,
    device_map="auto"
)
model = PeftModel.from_pretrained(base_model, adapter_path)
tokenizer = AutoTokenizer.from_pretrained(base_model_name)

# Generate response
def ask_anysecret(question):
    prompt = f"### Instruction:\n{question}\n\n### Response:\n"
    inputs = tokenizer(prompt, return_tensors="pt")
    outputs = model.generate(**inputs, max_new_tokens=256, temperature=0.1)
    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
    return response.split("### Response:\n")[-1].strip()

# Example usage
print(ask_anysecret("How do I configure AnySecret for AWS?"))

Option 3: Using llama.cpp (GGUF)

# Download GGUF model
wget https://huggingface.co/anysecret-io/anysecret-assistant/resolve/main/7B-GGUF/anysecret-7b-q4_k_m.gguf

# Run with llama.cpp
./llama-server -m anysecret-7b-q4_k_m.gguf --port 8080

🎯 Use Cases

Direct Use

All models are designed to provide expert assistance with:

• —AnySecret CLI - Commands, usage patterns, troubleshooting
• —Multi-cloud Configuration - AWS Secrets Manager, GCP Secret Manager, Azure Key Vault
• —Kubernetes Integration - Secrets, ConfigMaps, operators
• —CI/CD Pipelines - GitHub Actions, Jenkins, GitLab CI
• —Python SDK - Implementation guidance, best practices
• —Security Patterns - Secret rotation, access controls, compliance

Example Queries

"How do I set up AnySecret with AWS Secrets Manager?"
"Show me how to use anysecret in a GitHub Actions workflow"
"How do I rotate secrets across multiple cloud providers?"
"What's the difference between storing secrets vs parameters?"
"How do I configure AnySecret for a Kubernetes deployment?"

🏗️ Training Details

Training Data

Models were trained on 150+ curated examples across 7 categories:

• —CLI Commands (25 examples) - Command usage and patterns
• —AWS Configuration (25 examples) - Secrets Manager integration
• —GCP Configuration (25 examples) - Secret Manager setup
• —Azure Configuration (25 examples) - Key Vault integration
• —Kubernetes (25 examples) - Secrets and ConfigMaps
• —CI/CD Integration (15 examples) - Pipeline workflows
• —Python Integration (10 examples) - SDK usage patterns

Training Configuration

Hyperparameters

• —LoRA Rank: 16
• —LoRA Alpha: 32
• —Learning Rate: 2e-4
• —Batch Size: 1 (with gradient accumulation)
• —Epochs: 2-3
• —Precision: fp16 mixed precision with 4-bit quantization

Target Modules

• —Llama-3.2 (3B): qproj, kproj, vproj, oproj, gateproj, upproj, down_proj
• —CodeLlama (7B/13B): qproj, kproj, vproj, oproj, gateproj, upproj, down_proj

🔧 Model Selection Guide

Choose 3B if you need:

• —✅ Fast inference (< 1 second)
• —✅ Low memory usage (4-6GB)
• —✅ Edge deployment
• —✅ Basic AnySecret queries

Choose 7B if you need:

• —✅ Balanced performance/speed
• —✅ Better code understanding
• —✅ Moderate memory (8-12GB)
• —✅ Complex configuration queries

Choose 13B if you need:

• —✅ Highest quality responses
• —✅ Complex multi-step guidance
• —✅ Advanced troubleshooting
• —✅ Production deployments

🚀 Deployment Options

Local Development

• —GGUF + Ollama: Easiest setup, good performance
• —PyTorch + GPU: Best quality, requires CUDA

Production Deployment

• —Docker + llama.cpp: Scalable, CPU/GPU support
• —Kubernetes: Auto-scaling, load balancing
• —Cloud APIs: Serverless, pay-per-use

Memory Requirements

📚 Model Sources

• —Repository: https://github.com/anysecret-io/anysecret-lib
• —Documentation: https://docs.anysecret.io
• —Training Code: https://github.com/anysecret-io/anysecret-llm
• —Website: https://anysecret.io

🔍 Framework Versions

• —PEFT: 0.17.1+
• —Transformers: 4.35.0+
• —PyTorch: 2.0.0+
• —llama.cpp: Latest
• —Ollama: 0.1.0+

📊 Performance Benchmarks

Benchmarks run on RTX 3090 with GGUF Q4_K_M quantization

⚖️ License

MIT License - See individual model folders for specific license details.

For support, visit our GitHub Issues or Documentation.