Instructions to use techcodebhavesh/AutoDashAnalyticsV1GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use techcodebhavesh/AutoDashAnalyticsV1GGUF with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("techcodebhavesh/AutoDashAnalyticsV1GGUF", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use techcodebhavesh/AutoDashAnalyticsV1GGUF 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 techcodebhavesh/AutoDashAnalyticsV1GGUF:F16 # Run inference directly in the terminal: llama cli -hf techcodebhavesh/AutoDashAnalyticsV1GGUF:F16
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf techcodebhavesh/AutoDashAnalyticsV1GGUF:F16 # Run inference directly in the terminal: llama cli -hf techcodebhavesh/AutoDashAnalyticsV1GGUF:F16
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 techcodebhavesh/AutoDashAnalyticsV1GGUF:F16 # Run inference directly in the terminal: ./llama-cli -hf techcodebhavesh/AutoDashAnalyticsV1GGUF:F16
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 techcodebhavesh/AutoDashAnalyticsV1GGUF:F16 # Run inference directly in the terminal: ./build/bin/llama-cli -hf techcodebhavesh/AutoDashAnalyticsV1GGUF:F16
Use Docker
docker model run hf.co/techcodebhavesh/AutoDashAnalyticsV1GGUF:F16
- LM Studio
- Jan
- Ollama
How to use techcodebhavesh/AutoDashAnalyticsV1GGUF with Ollama:
ollama run hf.co/techcodebhavesh/AutoDashAnalyticsV1GGUF:F16
- Unsloth Desktop
- Docker Model Runner
How to use techcodebhavesh/AutoDashAnalyticsV1GGUF with Docker Model Runner:
docker model run hf.co/techcodebhavesh/AutoDashAnalyticsV1GGUF:F16
- Lemonade
How to use techcodebhavesh/AutoDashAnalyticsV1GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull techcodebhavesh/AutoDashAnalyticsV1GGUF:F16
Run and chat with the model
lemonade run user.AutoDashAnalyticsV1GGUF-F16
List all available models
lemonade list
- Atomic Chat
Download AutoDashv1.F16.gguf from techcodebhavesh/AutoDashAnalyticsV1GGUF: direct link, hf CLI and curl.
- Browser
- Download file 16.1 GB
-
https://huggingface.co/techcodebhavesh/AutoDashAnalyticsV1GGUF/resolve/main/AutoDashv1.F16.gguf
- Command line
-
hf download hf://techcodebhavesh/AutoDashAnalyticsV1GGUF/AutoDashv1.F16.gguf
-
curl -L -o AutoDashv1.F16.gguf https://huggingface.co/techcodebhavesh/AutoDashAnalyticsV1GGUF/resolve/main/AutoDashv1.F16.gguf
16.1 GB
- Xet hash:
- 9c852b4974d69c0078de46821789828cb1e4ad18df12e1c0268b5df83c74238a
- Size of remote file:
- 16.1 GB
- SHA256:
- 8368f6a0c1f6efa24a0310a7cb89d8b40ddb8d81fcda0bb6b33f0fdc4c4a7370
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.