Instructions to use bnjlebron/chipotle-support-qwen3.5-0.8b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use bnjlebron/chipotle-support-qwen3.5-0.8b with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf bnjlebron/chipotle-support-qwen3.5-0.8b
# Run inference directly in the terminal:
llama cli -hf bnjlebron/chipotle-support-qwen3.5-0.8b

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf bnjlebron/chipotle-support-qwen3.5-0.8b
# Run inference directly in the terminal:
llama cli -hf bnjlebron/chipotle-support-qwen3.5-0.8b

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 bnjlebron/chipotle-support-qwen3.5-0.8b
# Run inference directly in the terminal:
./llama-cli -hf bnjlebron/chipotle-support-qwen3.5-0.8b

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 bnjlebron/chipotle-support-qwen3.5-0.8b
# Run inference directly in the terminal:
./build/bin/llama-cli -hf bnjlebron/chipotle-support-qwen3.5-0.8b

Use Docker

docker model run hf.co/bnjlebron/chipotle-support-qwen3.5-0.8b

LM Studio
Jan

vLLM

How to use bnjlebron/chipotle-support-qwen3.5-0.8b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "bnjlebron/chipotle-support-qwen3.5-0.8b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "bnjlebron/chipotle-support-qwen3.5-0.8b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/bnjlebron/chipotle-support-qwen3.5-0.8b

Ollama
How to use bnjlebron/chipotle-support-qwen3.5-0.8b with Ollama:
```
ollama run hf.co/bnjlebron/chipotle-support-qwen3.5-0.8b
```
Unsloth Desktop

How to use bnjlebron/chipotle-support-qwen3.5-0.8b with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf bnjlebron/chipotle-support-qwen3.5-0.8b

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "bnjlebron/chipotle-support-qwen3.5-0.8b"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use bnjlebron/chipotle-support-qwen3.5-0.8b with Docker Model Runner:
```
docker model run hf.co/bnjlebron/chipotle-support-qwen3.5-0.8b
```

Lemonade

How to use bnjlebron/chipotle-support-qwen3.5-0.8b with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull bnjlebron/chipotle-support-qwen3.5-0.8b

Run and chat with the model

lemonade run user.chipotle-support-qwen3.5-0.8b-{{QUANT_TAG}}

List all available models

lemonade list

Hermes Agent

How to use bnjlebron/chipotle-support-qwen3.5-0.8b with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf bnjlebron/chipotle-support-qwen3.5-0.8b

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default bnjlebron/chipotle-support-qwen3.5-0.8b

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use bnjlebron/chipotle-support-qwen3.5-0.8b with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf bnjlebron/chipotle-support-qwen3.5-0.8b

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "bnjlebron/chipotle-support-qwen3.5-0.8b" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Chipotle Support Qwen3.5 0.8B (Q8_0)

A Q8_0 quantized GGUF of Qwen3.5-0.8B fine-tuned on 685 distilled question/answer pairs harvested from the Chipotle support chatbot (Pepper).

This is a small persona model. It was trained as a personal project to see how far a 0.8B model could go with ~1M tokens of support-bot Q&A, adversarial red-teaming, and instruction-format training.

Model details

Property	Value
Base model	Qwen3.5-0.8B
Architecture	`qwen35`
File type	Q8_0 (8-bit quantization, ~1.4 GB)
Tensor count	320
Context length	2048 (though it can be used with up to 256k because neither i nor my deepseek agent figured out how to pull down the context length)
Chat template	Qwen chat template (system / user / assistant)

Training data

The training set was built from:

~685 cleaned Q&A pairs distilled from the Chipotle support chatbot
Adversarial red-team rephrasings (edge cases, out-of-scope deflection)
Identity / persona pairs

Known junk patterns (self-referential Q&A, accordion artifacts) were programmatically filtered out of the raw harvest.

Behavior

The model answers in the persona of a fast-food support chatbot. It:

Handles common questions (menu, hours, orders, rewards) with ground-truth answers from the harvest
Deflects out-of-scope questions with canned boundary replies
Has been red-teamed against over-claiming and adversarial rephrasings
May still hallucinate or get details wrong — it's a 0.8B, treat it accordingly

Usage

llama.cpp

llama-cli -m chipotle-support-qwen3.5-0.8b.gguf \
  -p "system: You are Chipotle support. Respond helpfully and briefly.\nuser: Do you have vegan options?\nassistant:" \
  -n 256000

LM Studio

Drop the GGUF in your models folder and load it — the Qwen chat template is baked into the file. it thinks the quant is nonexistent tho lmao

Disclaimer

This is an unofficial, fan-made project. It is not affiliated with, endorsed by, or connected to Chipotle Mexican Grill or its support systems. The model's answers are generated from a small distilled dataset and should not be treated as accurate, official, or current information about Chipotle.

License

The base model (Qwen3.5-0.8B) is licensed under Apache 2.0 (Qwen license). This fine-tune is provided for research / fun purposes. Training data was harvested from a public chatbot and cleaned; check your local terms before redistributing commercially.