Transformers
GGUF
oil-gas
drilling-engineering
mixtral-8x7b
lora
fine-tuned
energy-ai
retrieval-augmented-generation
llama-cpp
gguf-my-repo
Eval Results (legacy)
conversational
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`](https://huggingface.co/GainEnergy/OGAI-8x7b) using llama.cpp. | |
| Refer to the [original model card](https://huggingface.co/GainEnergy/OGAI-8x7b) for more details on the model. | |
| ## Use with Ollama | |
| Execute the following | |
| ```bash | |
| 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) | |
| ```bash | |
| brew install llama.cpp | |
| ``` | |
| Invoke the llama.cpp server or the CLI. | |
| ### CLI: | |
| ```bash | |
| 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: | |
| ```bash | |
| 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](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) 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 | |
| ``` | |