Instructions to use appvoid/event-attribute-extractor-Q8_0-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use appvoid/event-attribute-extractor-Q8_0-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="appvoid/event-attribute-extractor-Q8_0-GGUF", filename="event-attribute-extractor-q8_0.gguf", )
output = llm( "Once upon a time,", max_tokens=512, echo=True ) print(output)
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- llama.cpp
How to use appvoid/event-attribute-extractor-Q8_0-GGUF with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf appvoid/event-attribute-extractor-Q8_0-GGUF:Q8_0 # Run inference directly in the terminal: llama-cli -hf appvoid/event-attribute-extractor-Q8_0-GGUF:Q8_0
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf appvoid/event-attribute-extractor-Q8_0-GGUF:Q8_0 # Run inference directly in the terminal: llama-cli -hf appvoid/event-attribute-extractor-Q8_0-GGUF:Q8_0
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 appvoid/event-attribute-extractor-Q8_0-GGUF:Q8_0 # Run inference directly in the terminal: ./llama-cli -hf appvoid/event-attribute-extractor-Q8_0-GGUF:Q8_0
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 appvoid/event-attribute-extractor-Q8_0-GGUF:Q8_0 # Run inference directly in the terminal: ./build/bin/llama-cli -hf appvoid/event-attribute-extractor-Q8_0-GGUF:Q8_0
Use Docker
docker model run hf.co/appvoid/event-attribute-extractor-Q8_0-GGUF:Q8_0
- LM Studio
- Jan
- Ollama
How to use appvoid/event-attribute-extractor-Q8_0-GGUF with Ollama:
ollama run hf.co/appvoid/event-attribute-extractor-Q8_0-GGUF:Q8_0
- Unsloth Studio new
How to use appvoid/event-attribute-extractor-Q8_0-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 appvoid/event-attribute-extractor-Q8_0-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 appvoid/event-attribute-extractor-Q8_0-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for appvoid/event-attribute-extractor-Q8_0-GGUF to start chatting
- Docker Model Runner
How to use appvoid/event-attribute-extractor-Q8_0-GGUF with Docker Model Runner:
docker model run hf.co/appvoid/event-attribute-extractor-Q8_0-GGUF:Q8_0
- Lemonade
How to use appvoid/event-attribute-extractor-Q8_0-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull appvoid/event-attribute-extractor-Q8_0-GGUF:Q8_0
Run and chat with the model
lemonade run user.event-attribute-extractor-Q8_0-GGUF-Q8_0
List all available models
lemonade list
File size: 1,909 Bytes
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license: apache-2.0
language:
- en
base_model: ahalt/event-attribute-extractor
tags:
- event-data
- political-science
- computational-social-science
- llama-cpp
- gguf-my-repo
---
# appvoid/event-attribute-extractor-Q8_0-GGUF
This model was converted to GGUF format from [`ahalt/event-attribute-extractor`](https://huggingface.co/ahalt/event-attribute-extractor) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/ahalt/event-attribute-extractor) for more details on the model.
## 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 appvoid/event-attribute-extractor-Q8_0-GGUF --hf-file event-attribute-extractor-q8_0.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo appvoid/event-attribute-extractor-Q8_0-GGUF --hf-file event-attribute-extractor-q8_0.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 appvoid/event-attribute-extractor-Q8_0-GGUF --hf-file event-attribute-extractor-q8_0.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo appvoid/event-attribute-extractor-Q8_0-GGUF --hf-file event-attribute-extractor-q8_0.gguf -c 2048
```
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