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

23a837f

verified ·

1 Parent(s): 12ed114

Upload README.md with huggingface_hub

Browse files

Files changed (1) hide show

README.md +49 -13

README.md CHANGED Viewed

@@ -1,13 +1,49 @@
----
-license: apache-2.0
-datasets:
-- deltakitsune/properly-v1.01
-language:
-- en
-base_model:
-- google/gemma-3-1b-it
-tags:
-- grammar
-- spelling
-- editing
----

+# Properly-E4-91E3-2
+## Summary
+- Training run: #95
+- Base model: `Local artifact (path omitted)`
+- Artifact: `Local artifact (path omitted)`
+- Status: completed
+- Started: 2026-04-30T04:00:32.996087+00:00
+- Finished: 2026-04-30T09:22:44.290944+00:00
+- Final loss: 0.8404612632730544
+- Final accuracy: N/A - token accuracy not logged for this run type
+## Training Configuration
+- attn_implementation: `eager`
+- batch_size: `1`
+- epochs: `1`
+- grad_accum: `16`
+- learning_rate: `5e-5`
+- lora_alpha: `32`
+- lora_dropout: `0.05`
+- lora_rank: `16`
+- max_grad_norm: `1`
+- max_seq: `512`
+- system_prompt_override: `You are Properly a helpful assistant. You fix grammar, spelling, and clarity. Preserve the author's voice. Return only the corrected text. No explanations. No commentary. No emojis or hashtags.`
+- target_examples: `50000`
+## Dataset Configuration
+- Dataset Mix | 3 sources | seed 42 (mixture; 35,000 rows)
+## Recent Training Metrics
+Accuracy is marked `N/A` because this run type logs causal language-model loss, not a publishable evaluation accuracy.
+| Step | Loss | Accuracy | LR | Epoch | Timestamp |
+| --- | ---: | ---: | ---: | ---: | --- |
+| 2130 / 2188 | 0.8876 | - | - | 0.97 | 2026-04-30T09:14:13.929849+00:00 |
+| 2140 / 2188 | 0.8566 | - | - | 0.98 | 2026-04-30T09:15:41.870227+00:00 |
+| 2150 / 2188 | 0.8243 | - | - | 0.98 | 2026-04-30T09:17:06.787695+00:00 |
+| 2160 / 2188 | 0.8341 | - | - | 0.99 | 2026-04-30T09:18:34.679929+00:00 |
+| 2170 / 2188 | 0.8131 | - | - | 0.99 | 2026-04-30T09:20:01.712890+00:00 |
+| 2180 / 2188 | 0.8141 | - | - | 1.0 | 2026-04-30T09:21:24.802808+00:00 |
+| 2188 / 2188 | 0.8404612632730544 | - | - | - | 2026-04-30T09:22:42.250939+00:00 |
+## Notes
+Generated by Kitsune Training Suite. Review limitations, intended use, safety notes, and licensing before publishing.