Text Generation
Safetensors
GGUF
English
qwen2_5_vl
nuextract
extraction
json
schedule
rrule
ical
rfc5545
structured-data
conversational
Instructions to use connect211/RRULE_Extractor with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use connect211/RRULE_Extractor with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="connect211/RRULE_Extractor", filename="model-q4_k_m.gguf", )
llm.create_chat_completion( messages = [ { "role": "user", "content": "What is the capital of France?" } ] ) - Notebooks
- Google Colab
- Kaggle
- Local Apps
- llama.cpp
How to use connect211/RRULE_Extractor with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf connect211/RRULE_Extractor:Q4_K_M # Run inference directly in the terminal: llama-cli -hf connect211/RRULE_Extractor:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf connect211/RRULE_Extractor:Q4_K_M # Run inference directly in the terminal: llama-cli -hf connect211/RRULE_Extractor: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 connect211/RRULE_Extractor:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf connect211/RRULE_Extractor: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 connect211/RRULE_Extractor:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf connect211/RRULE_Extractor:Q4_K_M
Use Docker
docker model run hf.co/connect211/RRULE_Extractor:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use connect211/RRULE_Extractor with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "connect211/RRULE_Extractor" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "connect211/RRULE_Extractor", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/connect211/RRULE_Extractor:Q4_K_M
- Ollama
How to use connect211/RRULE_Extractor with Ollama:
ollama run hf.co/connect211/RRULE_Extractor:Q4_K_M
- Unsloth Studio
How to use connect211/RRULE_Extractor 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 connect211/RRULE_Extractor 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 connect211/RRULE_Extractor to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for connect211/RRULE_Extractor to start chatting
- Docker Model Runner
How to use connect211/RRULE_Extractor with Docker Model Runner:
docker model run hf.co/connect211/RRULE_Extractor:Q4_K_M
- Lemonade
How to use connect211/RRULE_Extractor with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull connect211/RRULE_Extractor:Q4_K_M
Run and chat with the model
lemonade run user.RRULE_Extractor-Q4_K_M
List all available models
lemonade list
| { | |
| "crop_size": null, | |
| "data_format": "channels_first", | |
| "default_to_square": true, | |
| "device": null, | |
| "disable_grouping": null, | |
| "do_center_crop": null, | |
| "do_convert_rgb": true, | |
| "do_normalize": true, | |
| "do_rescale": true, | |
| "do_resize": true, | |
| "image_mean": [ | |
| 0.48145466, | |
| 0.4578275, | |
| 0.40821073 | |
| ], | |
| "image_processor_type": "Qwen2VLImageProcessorFast", | |
| "image_std": [ | |
| 0.26862954, | |
| 0.26130258, | |
| 0.27577711 | |
| ], | |
| "input_data_format": null, | |
| "max_pixels": 23000000, | |
| "merge_size": 2, | |
| "min_pixels": 200704, | |
| "patch_size": 14, | |
| "processor_class": "Qwen2_5_VLProcessor", | |
| "resample": 3, | |
| "rescale_factor": 0.00392156862745098, | |
| "return_tensors": null, | |
| "size": { | |
| "longest_edge": 23000000, | |
| "shortest_edge": 200704 | |
| }, | |
| "temporal_patch_size": 2 | |
| } | |