Instructions to use rubra-ai/Mistral-7B-Instruct-v0.2-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use rubra-ai/Mistral-7B-Instruct-v0.2-GGUF 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 rubra-ai/Mistral-7B-Instruct-v0.2-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf rubra-ai/Mistral-7B-Instruct-v0.2-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf rubra-ai/Mistral-7B-Instruct-v0.2-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf rubra-ai/Mistral-7B-Instruct-v0.2-GGUF: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 rubra-ai/Mistral-7B-Instruct-v0.2-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf rubra-ai/Mistral-7B-Instruct-v0.2-GGUF: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 rubra-ai/Mistral-7B-Instruct-v0.2-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf rubra-ai/Mistral-7B-Instruct-v0.2-GGUF:Q4_K_M

Use Docker

docker model run hf.co/rubra-ai/Mistral-7B-Instruct-v0.2-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use rubra-ai/Mistral-7B-Instruct-v0.2-GGUF with Ollama:
```
ollama run hf.co/rubra-ai/Mistral-7B-Instruct-v0.2-GGUF:Q4_K_M
```

Unsloth Studio

How to use rubra-ai/Mistral-7B-Instruct-v0.2-GGUF 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 rubra-ai/Mistral-7B-Instruct-v0.2-GGUF 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 rubra-ai/Mistral-7B-Instruct-v0.2-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for rubra-ai/Mistral-7B-Instruct-v0.2-GGUF to start chatting

Docker Model Runner
How to use rubra-ai/Mistral-7B-Instruct-v0.2-GGUF with Docker Model Runner:
```
docker model run hf.co/rubra-ai/Mistral-7B-Instruct-v0.2-GGUF:Q4_K_M
```

Lemonade

How to use rubra-ai/Mistral-7B-Instruct-v0.2-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull rubra-ai/Mistral-7B-Instruct-v0.2-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Mistral-7B-Instruct-v0.2-GGUF-Q4_K_M

List all available models

lemonade list

Atomic Chat

Mistral-7B-Instruct-v0.2-GGUF / README.md

sanjay920

Update README.md

70d09b9 verified about 2 years ago

1.94 kB

license: apache-2.0
model-index:
  - name: Rubra-Mistral-7B-Instruct-v0.2
    results:
      - task:
          type: text-generation
        dataset:
          type: MMLU
          name: MMLU
        metrics:
          - type: 5-shot
            value: 58.9
            verified: false
      - task:
          type: text-generation
        dataset:
          type: GPQA
          name: GPQA
        metrics:
          - type: 0-shot
            value: 29.91
            verified: false
      - task:
          type: text-generation
        dataset:
          type: GSM-8K
          name: GSM-8K
        metrics:
          - type: 8-shot, CoT
            value: 34.12
            verified: false
      - task:
          type: text-generation
        dataset:
          type: MATH
          name: MATH
        metrics:
          - type: 4-shot, CoT
            value: 8.36
            verified: false
      - task:
          type: text-generation
        dataset:
          type: MT-bench
          name: MT-bench
        metrics:
          - type: GPT-4 as Judge
            value: 7.36
            verified: false
tags:
  - function-calling
  - tool-calling
  - agentic
  - rubra
  - conversational
language:
  - en

Rubra Mistral 7B Instruct v0.2 GGUF

Original model: rubra-ai/Mistral-7B-Instruct-v0.2

Model description

The model is the result of further post-training mistralai/Mistral-7B-Instruct-v0.2. This model is designed for high performance in various instruction-following tasks and complex interactions, including multi-turn function calling and detailed conversations.

Training Data

The model underwent additional training on a proprietary dataset encompassing diverse instruction-following, chat, and function calling data. This post-training process enhances the model's ability to integrate tools and manage complex interaction scenarios effectively.

How to use

Refer to https://docs.rubra.ai/inference/llamacpp for usage. Feel free to ask/open issues up in our Github repo: https://github.com/rubra-ai/rubra