Instructions to use Lautloserspieler/mistral-code-architect-7b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Lautloserspieler/mistral-code-architect-7b with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Lautloserspieler/mistral-code-architect-7b",
	filename="Mistral-Code-Architect-7b.gguf",
)

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

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use Lautloserspieler/mistral-code-architect-7b 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 Lautloserspieler/mistral-code-architect-7b
# Run inference directly in the terminal:
llama cli -hf Lautloserspieler/mistral-code-architect-7b

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Lautloserspieler/mistral-code-architect-7b
# Run inference directly in the terminal:
llama cli -hf Lautloserspieler/mistral-code-architect-7b

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 Lautloserspieler/mistral-code-architect-7b
# Run inference directly in the terminal:
./llama-cli -hf Lautloserspieler/mistral-code-architect-7b

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 Lautloserspieler/mistral-code-architect-7b
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Lautloserspieler/mistral-code-architect-7b

Use Docker

docker model run hf.co/Lautloserspieler/mistral-code-architect-7b

LM Studio
Jan

vLLM

How to use Lautloserspieler/mistral-code-architect-7b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Lautloserspieler/mistral-code-architect-7b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Lautloserspieler/mistral-code-architect-7b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Lautloserspieler/mistral-code-architect-7b

Ollama
How to use Lautloserspieler/mistral-code-architect-7b with Ollama:
```
ollama run hf.co/Lautloserspieler/mistral-code-architect-7b
```

Unsloth Studio

How to use Lautloserspieler/mistral-code-architect-7b 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 Lautloserspieler/mistral-code-architect-7b 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 Lautloserspieler/mistral-code-architect-7b to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Lautloserspieler/mistral-code-architect-7b to start chatting

Atomic Chat new
Docker Model Runner
How to use Lautloserspieler/mistral-code-architect-7b with Docker Model Runner:
```
docker model run hf.co/Lautloserspieler/mistral-code-architect-7b
```

Lemonade

How to use Lautloserspieler/mistral-code-architect-7b with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Lautloserspieler/mistral-code-architect-7b

Run and chat with the model

lemonade run user.mistral-code-architect-7b-{{QUANT_TAG}}

List all available models

lemonade list

Mistral Code Architect 7B (GGUF)

Mistral Code Architect 7B is a highly specialized, locally fine-tuned large language model designed for advanced software development, deep debugging, and cybersecurity analysis. It has been trained to act as an autonomous coding assistant that provides robust, production-ready code and reasoning traces.

🚀 Model Details

Architecture: Mistral 7B (Quantized to GGUF q4_k_m)
Base Model: unsloth/mistral-7b-instruct-v0.3-bnb-4bit
Context Length: 4096 tokens
Training Framework: Unsloth (QLoRA)
Training Hardware: NVIDIA RTX 5070 (12 GB VRAM)
Language Support: Dynamic (Optimized for English and German)

📊 Training Data & Methodology

The model was fine-tuned using a curated mix of approximately 25,000 high-quality coding and security samples. The training pipeline heavily utilized packing (packing=True) to maximize context efficiency during the QLoRA fine-tuning process.

The dataset mixture includes:

CodeFeedback: Filtered instruction-following for robust code generation.
WebDev-Arena: Preference-based web development queries (React, Next.js, Backend).
CyberGym & Security-Code: Specialized datasets focusing on vulnerability detection, memory safety, and secure patching strategies.
IFEval: To strictly adhere to system constraints and formatting instructions.
OpenCodeInterpreter: For advanced reasoning and step-by-step logic.

💻 Usage with Ollama (Recommended)

Since this model is provided in the highly efficient .gguf format, you can easily run it locally using Ollama. The model is trained to dynamically adapt to the user's language.

1. Download the Model: Download the Mistral-Code-Architect-7b.gguf file from the Files and versions tab.

2. Create a Modelfile: In the same directory as your downloaded .gguf file, create a file named Modelfile and paste the following configuration. This includes a dynamic system prompt to ensure the model mirrors your language:

FROM ./Mistral-Code-Architect-7b.gguf

SYSTEM "You are an expert software developer and architect. Always respond in the exact language the user is speaking. If the user writes in English, reply in English. If the user writes in German, reply in German. Provide precise, secure, and highly technical answers."

TEMPLATE "{{ if .System }}<|im_start|>system\n{{ .System }}<|im_end|>\n{{ end }}{{ if .Prompt }}<|im_start|>user\n{{ .Prompt }}<|im_end|>\n{{ end }}<|im_start|>assistant\n{{ .Response }}<|im_end|>"

PARAMETER stop "<|im_start|>"
PARAMETER stop "<|im_end|>"

3. Build and Run: Open your terminal in that directory and run:

ollama create code-architect -f Modelfile
ollama run code-architect

⚠️ Limitations: While highly capable in generating code and identifying vulnerabilities, this model can still hallucinate complex APIs or suggest outdated libraries. Always review and test the generated code before deploying it to a production environment. The model is specifically fine-tuned for software engineering tasks and might underperform on general creative writing tasks.