Instructions to use connect211/RRULE_Extractor with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

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 new

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

CheetoBandito commited on Feb 27

Commit

809dcba

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1 Parent(s): bc1f07f

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@@ -19,7 +19,7 @@ This model is a fine-tuned and quantized version of the **NuExtract 8B** paramet
 ## Model Description
-This model is a working proof of concept for a meaningful hypothesis: that the semantically rich, human-stewarded data maintained by 211 networks is not limited by its lack of machine-readable structure. With targeted fine-tuning, AI can bridge that gap — preserving the accuracy and nuance of human curation while producing the structured outputs that governments, hospitals, researchers, and software providers need to build effective solutions around the social safety net.
 This model was trained as a follow-up to our initial experiments with the Osmosis 0.6B model. While the 0.6B model successfully learned the schema fields and could handle basic cases, it struggled with the complex realities of RFC 5545 RRULE schedule data. To overcome this limitation and ensure high-quality, strict structural adherence, we fine-tuned the highly capable **NuExtract 8B** model.

 ## Model Description
+This model is a working proof of concept for a meaningful hypothesis: that the semantically rich, human-stewarded data maintained by some 211 networks (especially in rural areas) is not limited by its lack of machine-readable structure. With targeted fine-tuning, AI can bridge that gap — preserving the accuracy and nuance of human curation while producing the structured outputs that governments, hospitals, researchers, and software providers need to build effective solutions around the social safety net.
 This model was trained as a follow-up to our initial experiments with the Osmosis 0.6B model. While the 0.6B model successfully learned the schema fields and could handle basic cases, it struggled with the complex realities of RFC 5545 RRULE schedule data. To overcome this limitation and ensure high-quality, strict structural adherence, we fine-tuned the highly capable **NuExtract 8B** model.