How to use from
llama.cppInstall from WinGet (Windows)
winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf rileyseaburg/distillix-spotless:F16# Run inference directly in the terminal:
llama cli -hf rileyseaburg/distillix-spotless:F16Use 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:F16Build 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:F16Use Docker
docker model run hf.co/rileyseaburg/distillix-spotless:F16Quick Links
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
Links
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Hardware compatibility
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16-bit
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Install (macOS, Linux)
# Start a local OpenAI-compatible server with a web UI: llama serve -hf rileyseaburg/distillix-spotless:F16# Run inference directly in the terminal: llama cli -hf rileyseaburg/distillix-spotless:F16