Instructions to use GainEnergy/OGAI-8x7b-Q4_K_M-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use GainEnergy/OGAI-8x7b-Q4_K_M-GGUF with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("GainEnergy/OGAI-8x7b-Q4_K_M-GGUF", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use GainEnergy/OGAI-8x7b-Q4_K_M-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-8x7b-Q4_K_M-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf GainEnergy/OGAI-8x7b-Q4_K_M-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 GainEnergy/OGAI-8x7b-Q4_K_M-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf GainEnergy/OGAI-8x7b-Q4_K_M-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 GainEnergy/OGAI-8x7b-Q4_K_M-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf GainEnergy/OGAI-8x7b-Q4_K_M-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 GainEnergy/OGAI-8x7b-Q4_K_M-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf GainEnergy/OGAI-8x7b-Q4_K_M-GGUF:Q4_K_M
Use Docker
docker model run hf.co/GainEnergy/OGAI-8x7b-Q4_K_M-GGUF:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use GainEnergy/OGAI-8x7b-Q4_K_M-GGUF with Ollama:
ollama run hf.co/GainEnergy/OGAI-8x7b-Q4_K_M-GGUF:Q4_K_M
- Unsloth Desktop
- Docker Model Runner
How to use GainEnergy/OGAI-8x7b-Q4_K_M-GGUF with Docker Model Runner:
docker model run hf.co/GainEnergy/OGAI-8x7b-Q4_K_M-GGUF:Q4_K_M
- Lemonade
How to use GainEnergy/OGAI-8x7b-Q4_K_M-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull GainEnergy/OGAI-8x7b-Q4_K_M-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.OGAI-8x7b-Q4_K_M-GGUF-Q4_K_M
List all available models
lemonade list
- Atomic Chat
base_model: GainEnergy/OGAI-8x7b
datasets:
- GainEnergy/ogai-8x7B
- GainEnergy/oilandgas-engineering-dataset
- GainEnergy/ogdataset
- GainEnergy/upstrimacentral
library_name: transformers
license: mit
tags:
- oil-gas
- drilling-engineering
- mixtral-8x7b
- lora
- fine-tuned
- energy-ai
- retrieval-augmented-generation
- llama-cpp
- gguf-my-repo
model-index:
- name: OGAI-8x7B
results:
- task:
type: text-generation
name: Drilling Engineering AI
dataset:
name: GainEnergy Oil & Gas Corpus
type: custom
metrics:
- type: accuracy
value: 94.8
name: Drilling Calculations Accuracy
- type: precision
value: 91.2
name: Engineering Document Retrieval Precision
- type: contextual-coherence
value: High
name: Context Retention
GainEnergy/OGAI-8x7b-Q4_K_M-GGUF
This model was converted to GGUF format from GainEnergy/OGAI-8x7b using llama.cpp.
Refer to the original model card for more details on the model.
Use with Ollama
Execute the following
ollama run hf.co/GainEnergy/OGAI-8x7b-Q4_K_M-GGUF:latest
Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
brew install llama.cpp
Invoke the llama.cpp server or the CLI.
CLI:
llama-cli --hf-repo GainEnergy/OGAI-8x7b-Q4_K_M-GGUF --hf-file ogai-8x7b-q4_k_m.gguf -p "The meaning to life and the universe is"
Server:
llama-server --hf-repo GainEnergy/OGAI-8x7b-Q4_K_M-GGUF --hf-file ogai-8x7b-q4_k_m.gguf -c 2048
Note: You can also use this checkpoint directly through the usage steps listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
git clone https://github.com/ggerganov/llama.cpp
Step 2: Move into the llama.cpp folder and build it with LLAMA_CURL=1 flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
cd llama.cpp && LLAMA_CURL=1 make
Step 3: Run inference through the main binary.
./llama-cli --hf-repo GainEnergy/OGAI-8x7b-Q4_K_M-GGUF --hf-file ogai-8x7b-q4_k_m.gguf -p "The meaning to life and the universe is"
or
./llama-server --hf-repo GainEnergy/OGAI-8x7b-Q4_K_M-GGUF --hf-file ogai-8x7b-q4_k_m.gguf -c 2048