Instructions to use rileyseaburg/distillix-spotless with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use rileyseaburg/distillix-spotless with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="rileyseaburg/distillix-spotless",
	filename="spotless-customer-service-50k-f16.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 rileyseaburg/distillix-spotless with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf rileyseaburg/distillix-spotless:F16
# Run inference directly in the terminal:
llama-cli -hf rileyseaburg/distillix-spotless:F16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf rileyseaburg/distillix-spotless:F16
# Run inference directly in the terminal:
llama-cli -hf rileyseaburg/distillix-spotless:F16

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 rileyseaburg/distillix-spotless:F16
# Run inference directly in the terminal:
./llama-cli -hf rileyseaburg/distillix-spotless:F16

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 rileyseaburg/distillix-spotless:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf rileyseaburg/distillix-spotless:F16

Use Docker

docker model run hf.co/rileyseaburg/distillix-spotless:F16

LM Studio
Jan
Ollama
How to use rileyseaburg/distillix-spotless with Ollama:
```
ollama run hf.co/rileyseaburg/distillix-spotless:F16
```

Unsloth Studio new

How to use rileyseaburg/distillix-spotless 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 rileyseaburg/distillix-spotless 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 rileyseaburg/distillix-spotless to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for rileyseaburg/distillix-spotless to start chatting

Docker Model Runner
How to use rileyseaburg/distillix-spotless with Docker Model Runner:
```
docker model run hf.co/rileyseaburg/distillix-spotless:F16
```

Lemonade

How to use rileyseaburg/distillix-spotless with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull rileyseaburg/distillix-spotless:F16

Run and chat with the model

lemonade run user.distillix-spotless-F16

List all available models

lemonade list

Spotless Customer Service - Distillix 100M BitNet

A 100M parameter BitNet b1.58 model fine-tuned for customer service conversations.

Model Details

Architecture: LLaMA-style with BitNet ternary weights {-1, 0, +1}
Parameters: 100M
Context Length: 256 tokens
Training: 5,000 steps on 8.7k customer service conversations
Use Case: Trash bin cleaning service customer support

Files

File	Size	Description
`spotless-customer-service-f16.gguf`	239 MB	For LM Studio / llama.cpp
`distillix-spotless-packed.pt`	30 MB	Compressed PyTorch (2-bit)
`distillix-spotless-final.pt`	382 MB	Full PyTorch checkpoint

Usage in LM Studio

Download spotless-customer-service-f16.gguf
Import into LM Studio
Use system prompt:

You are a customer service agent for Spotless Bin Co, a trash bin cleaning service.

Example Conversations

Customer: Hi, I need help with my service
Agent: Hello! I'm happy to assist you with your service today.

Customer: When is my next cleaning scheduled?
Agent: Let me look that up for you right now.

Customer: The truck didn't show up this week
Agent: I sincerely apologize for missing your scheduled cleaning. Let me look into this.

Training Data

spotless-customer-service-training - 8.7k customer service conversations

rileyseaburg
/

distillix-spotless