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

Libraries

How to use deltakitsune/properly with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="deltakitsune/properly")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("deltakitsune/properly", dtype="auto")

llama-cpp-python

How to use deltakitsune/properly with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="deltakitsune/properly",
	filename="exports/gguf/run_95-fp16.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 deltakitsune/properly with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf deltakitsune/properly:Q8_0
# Run inference directly in the terminal:
llama-cli -hf deltakitsune/properly:Q8_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf deltakitsune/properly:Q8_0
# Run inference directly in the terminal:
llama-cli -hf deltakitsune/properly: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 deltakitsune/properly:Q8_0
# Run inference directly in the terminal:
./llama-cli -hf deltakitsune/properly: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 deltakitsune/properly:Q8_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf deltakitsune/properly:Q8_0

Use Docker

docker model run hf.co/deltakitsune/properly:Q8_0

LM Studio
Jan

vLLM

How to use deltakitsune/properly with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "deltakitsune/properly"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "deltakitsune/properly",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/deltakitsune/properly:Q8_0

SGLang

How to use deltakitsune/properly with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "deltakitsune/properly" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "deltakitsune/properly",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "deltakitsune/properly" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "deltakitsune/properly",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use deltakitsune/properly with Ollama:
```
ollama run hf.co/deltakitsune/properly:Q8_0
```

Unsloth Studio new

How to use deltakitsune/properly 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 deltakitsune/properly 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 deltakitsune/properly to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for deltakitsune/properly to start chatting

Docker Model Runner
How to use deltakitsune/properly with Docker Model Runner:
```
docker model run hf.co/deltakitsune/properly:Q8_0
```

Lemonade

How to use deltakitsune/properly with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull deltakitsune/properly:Q8_0

Run and chat with the model

lemonade run user.properly-Q8_0

List all available models

lemonade list

deltakitsune commited on 24 days ago

Commit

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1 Parent(s): 29f7da0

Update README.md

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@@ -36,6 +36,9 @@ This model ran through 4 epochs on curated dataset mixtures from Hugging Face, a
 **Properly v1.03 — E3** Added spelling data to the mix. Catches the majority of errors. No banter. Occasional rogue 🚀. Pretty solid across tested turns. "theorectical" became "theoretical."
 **Properly v1.04 — E4** Increased spelling and edit percentage, removed everything else, lowered steps. Adjusted learning rate from 1e-4 to 5e-5 and grad accumulation from 8 to 16. Determined that temp 0.5 with top_p 0.9 is ideal, paired with a system prompt. Eradicates most undesired behavior while preserving the author's voice. Drastically improved spelling correction. The model does struggle with informal conversational input — prompts like "OMG i loved that song im listening to" can produce a full conversation rather than a correction. This behavior has not appeared in typical email or post editing tests. A future training run should revise the dataset mix accordingly. Also found a bug in the dataviewer that leads to the zigzagging in the curve. The issue originated in E1. Finally identified that bug and corrected plus added better health checks + viewing options to Forge.
 ---

 **Properly v1.03 — E3** Added spelling data to the mix. Catches the majority of errors. No banter. Occasional rogue 🚀. Pretty solid across tested turns. "theorectical" became "theoretical."
 **Properly v1.04 — E4** Increased spelling and edit percentage, removed everything else, lowered steps. Adjusted learning rate from 1e-4 to 5e-5 and grad accumulation from 8 to 16. Determined that temp 0.5 with top_p 0.9 is ideal, paired with a system prompt. Eradicates most undesired behavior while preserving the author's voice. Drastically improved spelling correction. The model does struggle with informal conversational input — prompts like "OMG i loved that song im listening to" can produce a full conversation rather than a correction. This behavior has not appeared in typical email or post editing tests. A future training run should revise the dataset mix accordingly. Also found a bug in the dataviewer that leads to the zigzagging in the curve. The issue originated in E1. Finally identified that bug and corrected plus added better health checks + viewing options to Forge.
+<br>
+![image](https://cdn-uploads.huggingface.co/production/uploads/693f7a72a7dfa854483548cb/DN6wt08apkO2UTD6NSHJo.png)
 ---