Instructions to use anysecret-io/anysecret-assistant with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries
PEFT
How to use anysecret-io/anysecret-assistant with PEFT:
```
Task type is invalid.
```

How to use anysecret-io/anysecret-assistant with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="anysecret-io/anysecret-assistant")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("anysecret-io/anysecret-assistant", dtype="auto")

llama-cpp-python

How to use anysecret-io/anysecret-assistant with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="anysecret-io/anysecret-assistant",
	filename="13B-GGUF/anysecret-assistant-13B-Q4_K_M.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use anysecret-io/anysecret-assistant with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf anysecret-io/anysecret-assistant:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf anysecret-io/anysecret-assistant:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf anysecret-io/anysecret-assistant:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf anysecret-io/anysecret-assistant:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf anysecret-io/anysecret-assistant:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf anysecret-io/anysecret-assistant:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf anysecret-io/anysecret-assistant:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf anysecret-io/anysecret-assistant:Q4_K_M

Use Docker

docker model run hf.co/anysecret-io/anysecret-assistant:Q4_K_M

LM Studio
Jan

vLLM

How to use anysecret-io/anysecret-assistant with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "anysecret-io/anysecret-assistant"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "anysecret-io/anysecret-assistant",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/anysecret-io/anysecret-assistant:Q4_K_M

SGLang

How to use anysecret-io/anysecret-assistant with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "anysecret-io/anysecret-assistant" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "anysecret-io/anysecret-assistant",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "anysecret-io/anysecret-assistant" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "anysecret-io/anysecret-assistant",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use anysecret-io/anysecret-assistant with Ollama:
```
ollama run hf.co/anysecret-io/anysecret-assistant:Q4_K_M
```

Unsloth Studio new

How to use anysecret-io/anysecret-assistant with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for anysecret-io/anysecret-assistant to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for anysecret-io/anysecret-assistant to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for anysecret-io/anysecret-assistant to start chatting

Docker Model Runner
How to use anysecret-io/anysecret-assistant with Docker Model Runner:
```
docker model run hf.co/anysecret-io/anysecret-assistant:Q4_K_M
```

Lemonade

How to use anysecret-io/anysecret-assistant with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull anysecret-io/anysecret-assistant:Q4_K_M

Run and chat with the model

lemonade run user.anysecret-assistant-Q4_K_M

List all available models

lemonade list

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.

For support, visit our GitHub Issues or Documentation.

Downloads last month: 7

GGUF

Model size

13B params

Architecture

llama

Hardware compatibility

4-bit

5-bit

8-bit

Model tree for anysecret-io/anysecret-assistant

Base model

codellama/CodeLlama-13b-Instruct-hf

Adapter

(30)

this model

anysecret-io
/

anysecret-assistant