Instructions to use GainEnergy/OGAI-24B-Q6_K-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use GainEnergy/OGAI-24B-Q6_K-GGUF with Transformers:

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("GainEnergy/OGAI-24B-Q6_K-GGUF", dtype="auto")

llama-cpp-python

How to use GainEnergy/OGAI-24B-Q6_K-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="GainEnergy/OGAI-24B-Q6_K-GGUF",
	filename="ogai-24b-q6_k.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use GainEnergy/OGAI-24B-Q6_K-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 GainEnergy/OGAI-24B-Q6_K-GGUF:Q6_K
# Run inference directly in the terminal:
llama cli -hf GainEnergy/OGAI-24B-Q6_K-GGUF:Q6_K

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf GainEnergy/OGAI-24B-Q6_K-GGUF:Q6_K
# Run inference directly in the terminal:
llama cli -hf GainEnergy/OGAI-24B-Q6_K-GGUF:Q6_K

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 GainEnergy/OGAI-24B-Q6_K-GGUF:Q6_K
# Run inference directly in the terminal:
./llama-cli -hf GainEnergy/OGAI-24B-Q6_K-GGUF:Q6_K

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 GainEnergy/OGAI-24B-Q6_K-GGUF:Q6_K
# Run inference directly in the terminal:
./build/bin/llama-cli -hf GainEnergy/OGAI-24B-Q6_K-GGUF:Q6_K

Use Docker

docker model run hf.co/GainEnergy/OGAI-24B-Q6_K-GGUF:Q6_K

LM Studio
Jan
Ollama
How to use GainEnergy/OGAI-24B-Q6_K-GGUF with Ollama:
```
ollama run hf.co/GainEnergy/OGAI-24B-Q6_K-GGUF:Q6_K
```

Unsloth Studio

How to use GainEnergy/OGAI-24B-Q6_K-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 GainEnergy/OGAI-24B-Q6_K-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 GainEnergy/OGAI-24B-Q6_K-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for GainEnergy/OGAI-24B-Q6_K-GGUF to start chatting

Atomic Chat new
Docker Model Runner
How to use GainEnergy/OGAI-24B-Q6_K-GGUF with Docker Model Runner:
```
docker model run hf.co/GainEnergy/OGAI-24B-Q6_K-GGUF:Q6_K
```

Lemonade

How to use GainEnergy/OGAI-24B-Q6_K-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull GainEnergy/OGAI-24B-Q6_K-GGUF:Q6_K

Run and chat with the model

lemonade run user.OGAI-24B-Q6_K-GGUF-Q6_K

List all available models

lemonade list

GainEnergy/OGAI-24B-Q6_K-GGUF

This model was converted to GGUF format from GainEnergy/OGAI-24B using llama.cpp. Refer to the original model card for more details.

Use with llama.cpp

Install llama.cpp through Homebrew (macOS/Linux):

brew install llama.cpp

Invoke the llama.cpp server or the CLI.

CLI:

llama-cli --hf-repo GainEnergy/OGAI-24B-Q6_K-GGUF --hf-file ogai-24b-q6_k.gguf -p "Explain the principles of reservoir simulation in oil and gas engineering."

Server:

llama-server --hf-repo GainEnergy/OGAI-24B-Q6_K-GGUF --hf-file ogai-24b-q6_k.gguf -c 2048

You can also follow the standard usage steps in the llama.cpp repository.

Manual Installation and Execution

Step 1: Clone llama.cpp

git clone https://github.com/ggerganov/llama.cpp

Step 2: Build llama.cpp with LLAMA_CURL=1 and optional GPU flags

cd llama.cpp && LLAMA_CURL=1 make

For Nvidia GPUs on Linux, add LLAMA_CUDA=1.

Step 3: Run inference

./llama-cli --hf-repo GainEnergy/OGAI-24B-Q6_K-GGUF --hf-file ogai-24b-q6_k.gguf -p "Explain the impact of wellbore stability on drilling efficiency."

./llama-server --hf-repo GainEnergy/OGAI-24B-Q6_K-GGUF --hf-file ogai-24b-q6_k.gguf -c 2048

This model is optimized for oil and gas engineering applications, featuring domain-specific knowledge in drilling, completions, reservoir management, and production optimization.

Downloads last month: 8

GGUF

Model size

24B params

Architecture

llama

Hardware compatibility

6-bit

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