Instructions to use Jaward/CodeOptimus-Instruct-Mistral-7B-v0.1.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 Jaward/CodeOptimus-Instruct-Mistral-7B-v0.1.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 Jaward/CodeOptimus-Instruct-Mistral-7B-v0.1.gguf:Q5_K_M
# Run inference directly in the terminal:
llama cli -hf Jaward/CodeOptimus-Instruct-Mistral-7B-v0.1.gguf:Q5_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Jaward/CodeOptimus-Instruct-Mistral-7B-v0.1.gguf:Q5_K_M
# Run inference directly in the terminal:
llama cli -hf Jaward/CodeOptimus-Instruct-Mistral-7B-v0.1.gguf:Q5_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 Jaward/CodeOptimus-Instruct-Mistral-7B-v0.1.gguf:Q5_K_M
# Run inference directly in the terminal:
./llama-cli -hf Jaward/CodeOptimus-Instruct-Mistral-7B-v0.1.gguf:Q5_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 Jaward/CodeOptimus-Instruct-Mistral-7B-v0.1.gguf:Q5_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Jaward/CodeOptimus-Instruct-Mistral-7B-v0.1.gguf:Q5_K_M

Use Docker

docker model run hf.co/Jaward/CodeOptimus-Instruct-Mistral-7B-v0.1.gguf:Q5_K_M

LM Studio
Jan
Ollama
How to use Jaward/CodeOptimus-Instruct-Mistral-7B-v0.1.gguf with Ollama:
```
ollama run hf.co/Jaward/CodeOptimus-Instruct-Mistral-7B-v0.1.gguf:Q5_K_M
```

Unsloth Studio

How to use Jaward/CodeOptimus-Instruct-Mistral-7B-v0.1.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 Jaward/CodeOptimus-Instruct-Mistral-7B-v0.1.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 Jaward/CodeOptimus-Instruct-Mistral-7B-v0.1.gguf to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Jaward/CodeOptimus-Instruct-Mistral-7B-v0.1.gguf to start chatting

Atomic Chat new
Docker Model Runner
How to use Jaward/CodeOptimus-Instruct-Mistral-7B-v0.1.gguf with Docker Model Runner:
```
docker model run hf.co/Jaward/CodeOptimus-Instruct-Mistral-7B-v0.1.gguf:Q5_K_M
```

Lemonade

How to use Jaward/CodeOptimus-Instruct-Mistral-7B-v0.1.gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Jaward/CodeOptimus-Instruct-Mistral-7B-v0.1.gguf:Q5_K_M

Run and chat with the model

lemonade run user.CodeOptimus-Instruct-Mistral-7B-v0.1.gguf-Q5_K_M

List all available models

lemonade list

Finetuned Model For My Thesis: Design And Implementation Of An Adaptive Virtual Intelligent Teaching Assistant Based On Supervised Fine-tuning Of A Pre-trained Large Language Model

Model Name: CodeOptimus - Adaptive Supervised Instruction Fine-tuning Mistral 7B Instruct using qLora.

Prerequisites For Reproduction

GPU: Requires powerful GPUs - I used 8 Nvidia A100s.
Train Time: 1 week.
RAG Module: Updates the knowledge base of the model in real-time with adaptive features learned from conversations with the model over time..
Python Packages: Install requirements.txt.
Dataset: Download code_instructions_122k_alpaca_style plus some custom curated dataset
Mistra-7B-Instruct-v0.1: Download mistralai/Mistral-7B-Instruct-v0.1 pytorch bin weights
Realistic 3D Intelligent Persona/Avatar (Optional): For this I'm using soulmachine's digital humans.

Downloads last month: 6

GGUF

Model size

7B params

Architecture

llama

Hardware compatibility

5-bit

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