Instructions to use Stormtrooperaim/Everything-Mistral-7b-0.3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use Stormtrooperaim/Everything-Mistral-7b-0.3 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 Stormtrooperaim/Everything-Mistral-7b-0.3:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Stormtrooperaim/Everything-Mistral-7b-0.3:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Stormtrooperaim/Everything-Mistral-7b-0.3:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Stormtrooperaim/Everything-Mistral-7b-0.3: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 Stormtrooperaim/Everything-Mistral-7b-0.3:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Stormtrooperaim/Everything-Mistral-7b-0.3: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 Stormtrooperaim/Everything-Mistral-7b-0.3:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Stormtrooperaim/Everything-Mistral-7b-0.3:Q4_K_M

Use Docker

docker model run hf.co/Stormtrooperaim/Everything-Mistral-7b-0.3:Q4_K_M

LM Studio
Jan
Ollama
How to use Stormtrooperaim/Everything-Mistral-7b-0.3 with Ollama:
```
ollama run hf.co/Stormtrooperaim/Everything-Mistral-7b-0.3:Q4_K_M
```

Unsloth Studio

How to use Stormtrooperaim/Everything-Mistral-7b-0.3 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 Stormtrooperaim/Everything-Mistral-7b-0.3 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 Stormtrooperaim/Everything-Mistral-7b-0.3 to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Stormtrooperaim/Everything-Mistral-7b-0.3 to start chatting

How to use Stormtrooperaim/Everything-Mistral-7b-0.3 with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Stormtrooperaim/Everything-Mistral-7b-0.3:Q4_K_M

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": "Stormtrooperaim/Everything-Mistral-7b-0.3:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use Stormtrooperaim/Everything-Mistral-7b-0.3 with Docker Model Runner:
```
docker model run hf.co/Stormtrooperaim/Everything-Mistral-7b-0.3:Q4_K_M
```

Lemonade

How to use Stormtrooperaim/Everything-Mistral-7b-0.3 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Stormtrooperaim/Everything-Mistral-7b-0.3:Q4_K_M

Run and chat with the model

lemonade run user.Everything-Mistral-7b-0.3-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use Stormtrooperaim/Everything-Mistral-7b-0.3 with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Stormtrooperaim/Everything-Mistral-7b-0.3:Q4_K_M

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 Stormtrooperaim/Everything-Mistral-7b-0.3:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use Stormtrooperaim/Everything-Mistral-7b-0.3 with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Stormtrooperaim/Everything-Mistral-7b-0.3:Q4_K_M

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 "Stormtrooperaim/Everything-Mistral-7b-0.3:Q4_K_M" \
  --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"

Everything-Mistral-7B-0.3

Everything-Mistral-7B-0.3 is a general-purpose instruction-tuned language model based on the Mistral 7B v0.3 architecture. It was fine-tuned on EverythingData, a synthetic dataset of approximately 9,000 instruction-response samples generated using the HY3 model through OpenRouter.

Despite its relatively small training set, the dataset covers a wide variety of topics, including programming, mathematics, science, reasoning, creative writing, history, and general knowledge, making the model suitable for diverse conversational and instruction-following tasks.

Features

🧠 General-purpose AI assistant
💻 Code generation and debugging
📚 Broad multi-domain knowledge
✍️ Writing, summarization, and reasoning
⚡ Built on the efficient Mistral 7B v0.3 architecture

Note

This model is intended for research and experimentation. Like all language models, it may occasionally produce inaccurate or outdated information.

Downloads last month: 34

GGUF

Model size

7B params

Architecture

llama

Hardware compatibility

2-bit

4-bit

8-bit

16-bit

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Model tree for Stormtrooperaim/Everything-Mistral-7b-0.3

Base model

mistralai/Mistral-7B-v0.3

Finetuned

mistralai/Mistral-7B-Instruct-v0.3

Quantized

(287)

this model

Stormtrooperaim
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Everything-Mistral-7b-0.3

Everything-Mistral-7B-0.3

Features

Note

Model tree for Stormtrooperaim/Everything-Mistral-7b-0.3

Dataset used to train Stormtrooperaim/Everything-Mistral-7b-0.3