Instructions to use lucasbischofchaperon/Sophia-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use lucasbischofchaperon/Sophia-v2 with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf lucasbischofchaperon/Sophia-v2:F16
# Run inference directly in the terminal:
llama cli -hf lucasbischofchaperon/Sophia-v2:F16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf lucasbischofchaperon/Sophia-v2:F16
# Run inference directly in the terminal:
llama cli -hf lucasbischofchaperon/Sophia-v2:F16

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 lucasbischofchaperon/Sophia-v2:F16
# Run inference directly in the terminal:
./llama-cli -hf lucasbischofchaperon/Sophia-v2:F16

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 lucasbischofchaperon/Sophia-v2:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf lucasbischofchaperon/Sophia-v2:F16

Use Docker

docker model run hf.co/lucasbischofchaperon/Sophia-v2:F16

LM Studio
Jan

vLLM

How to use lucasbischofchaperon/Sophia-v2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "lucasbischofchaperon/Sophia-v2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "lucasbischofchaperon/Sophia-v2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/lucasbischofchaperon/Sophia-v2:F16

Ollama
How to use lucasbischofchaperon/Sophia-v2 with Ollama:
```
ollama run hf.co/lucasbischofchaperon/Sophia-v2:F16
```

Unsloth Studio

How to use lucasbischofchaperon/Sophia-v2 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 lucasbischofchaperon/Sophia-v2 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 lucasbischofchaperon/Sophia-v2 to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for lucasbischofchaperon/Sophia-v2 to start chatting

How to use lucasbischofchaperon/Sophia-v2 with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf lucasbischofchaperon/Sophia-v2:F16

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "lucasbischofchaperon/Sophia-v2:F16"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

OpenClaw new

How to use lucasbischofchaperon/Sophia-v2 with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf lucasbischofchaperon/Sophia-v2:F16

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "lucasbischofchaperon/Sophia-v2:F16" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Docker Model Runner
How to use lucasbischofchaperon/Sophia-v2 with Docker Model Runner:
```
docker model run hf.co/lucasbischofchaperon/Sophia-v2:F16
```

Lemonade

How to use lucasbischofchaperon/Sophia-v2 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull lucasbischofchaperon/Sophia-v2:F16

Run and chat with the model

lemonade run user.Sophia-v2-F16

List all available models

lemonade list

Hermes Agent

How to use lucasbischofchaperon/Sophia-v2 with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf lucasbischofchaperon/Sophia-v2:F16

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default lucasbischofchaperon/Sophia-v2:F16

Run Hermes

hermes

Atomic Chat

Chaperon-Sophia-v2

An experimental research assistant for archaeology, history, biblical studies, and ancient texts.

Developed by Chaperon GmbH and Lucas Bischof, Sophia-v2 demonstrates a complete end-to-end AI workflow including multimodal data processing, document extraction, dataset generation, LoRA fine-tuning, GGUF deployment, and local AI integration.

Overview

Sophia-v2 is a custom fine-tuned variant of Qwen2.5-7B-Instruct designed to explore historical, textual, archaeological, and religious source material through natural conversation.

The project serves both as a technical showcase of Chaperon GmbH's AI capabilities and as a practical proof-of-concept for domain-specific model adaptation.

Project Goals

This project demonstrates the ability to:

Process large collections of PDFs
Extract text from historical documents
Convert audio and video into training datasets
Process image-based source material
Generate structured instruction datasets
Fine-tune Large Language Models using LoRA
Merge and deploy custom models
Publish GGUF models for local inference

Training Sources

The training process included the preparation and processing of curated material related to:

CDLI Cuneiform Collections
ETCSL Translations
Ancient Mesopotamian Literature
Biblical Texts
Apocryphal Literature
Gnostic Literature
Gospel of Mary
Pistis Sophia
Comparative Religion
Historical Source Material
Archaeological Research

The focus of Sophia-v2 is source-oriented exploration and discussion rather than authoritative interpretation.

Intended Audience

Sophia-v2 may be useful for:

Archaeology enthusiasts
History researchers
Biblical scholars
Comparative religion communities
Independent researchers
Educational projects
Digital humanities initiatives

Available Models

Sophia_v2_q5_K_M.gguf

Recommended version for most users.

Provides an excellent balance between quality, performance, and memory usage.

Sophia_v2_f16.gguf

Reference-quality version.

Provides maximum fidelity while requiring significantly more storage and memory.

Technical Specifications

Base Model:

Qwen/Qwen2.5-7B-Instruct

Training Method:

Supervised Fine Tuning (SFT)
LoRA Adaptation
Model Merge
GGUF Conversion

Deployment Targets:

LM Studio
llama.cpp
Open WebUI
KoboldCpp

Limitations

Sophia-v2 is a language model and may:

Generate incorrect information
Misinterpret source material
Produce inaccurate citations
Present plausible but incorrect conclusions

Users should verify important claims using primary and scholarly sources.

About Chaperon GmbH

Sophia-v2 serves as a public demonstration of the capabilities of Chaperon GmbH in:

Multimodal data processing
Knowledge extraction
AI training pipelines
Custom model development
Local AI deployment
Enterprise AI integration

Credits

Created by:

Lucas Bischof
Chaperon GmbH

Base Model:

Qwen/Qwen2.5-7B-Instruct

GGUF Conversion:

llama.cpp

038febb (Improve model card)

Downloads last month: 453

GGUF

Model size

8B params

Architecture

qwen2

Hardware compatibility

5-bit

16-bit

Model tree for lucasbischofchaperon/Sophia-v2

Base model

Qwen/Qwen2.5-7B

Finetuned

Qwen/Qwen2.5-7B-Instruct

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