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	Base Model	Parameters	Format	Best For	Memory
3B	Llama-3.2-3B-Instruct	3B	PyTorch/GGUF	Fast responses, edge deployment	4-6GB
7B	CodeLlama-7B-Instruct	7B	PyTorch/GGUF	Balanced performance, code focus	8-12GB
13B	CodeLlama-13B-Instruct	13B	PyTorch/GGUF	Highest quality, complex queries	16-24GB

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/ - Q4_K_M, Q8_0 formats
• anysecret-io/anysecret-assistant/7B-GGUF/ - Q4_K_M, Q8_0 formats
• anysecret-io/anysecret-assistant/13B-GGUF/ - Q4_K_M, 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): q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj
• CodeLlama (7B/13B): q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, 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	GGUF Q4_K_M	GGUF Q8_0	PyTorch FP16
3B	2.3GB	3.2GB	6GB
7B	4.1GB	7.2GB	14GB
13B	7.8GB	13.8GB	26GB

📚 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

Model	Tokens/sec	Quality Score	Memory (GGUF Q4)
3B	~45	7.2/10	2.3GB
7B	~25	8.5/10	4.1GB
13B	~15	9.1/10	7.8GB

Benchmarks run on RTX 3090 with GGUF Q4_K_M quantization

⚖️ License

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

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For support, visit our GitHub Issues or Documentation.