Instructions to use lxucan/teleport-nav with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use lxucan/teleport-nav with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="lxucan/teleport-nav",
	filename="teleport-nav-Q8_0.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use lxucan/teleport-nav 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 lxucan/teleport-nav:Q8_0
# Run inference directly in the terminal:
llama cli -hf lxucan/teleport-nav:Q8_0

Install from WinGet (Windows)

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

Use Docker

docker model run hf.co/lxucan/teleport-nav:Q8_0

LM Studio
Jan
Ollama
How to use lxucan/teleport-nav with Ollama:
```
ollama run hf.co/lxucan/teleport-nav:Q8_0
```

Unsloth Studio

How to use lxucan/teleport-nav 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 lxucan/teleport-nav 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 lxucan/teleport-nav to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for lxucan/teleport-nav to start chatting

Atomic Chat new
Docker Model Runner
How to use lxucan/teleport-nav with Docker Model Runner:
```
docker model run hf.co/lxucan/teleport-nav:Q8_0
```

Lemonade

How to use lxucan/teleport-nav with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull lxucan/teleport-nav:Q8_0

Run and chat with the model

lemonade run user.teleport-nav-Q8_0

List all available models

lemonade list

teleport-nav — Q8_0 GGUF (Qwen2.5-1.5B)

Fine-tuned Qwen2.5-1.5B-Instruct that turns French/English natural-language navigation requests into the strict scenario JSON consumed by the Teleport Control app, and refuses off-domain requests as valid empty-step JSON.


Base model	`Qwen/Qwen2.5-1.5B-Instruct` (Apache-2.0)
Quantization	`Q8_0` (~1.6 GB)
Languages	French + English
Prompt format	Qwen2.5 ChatML
Sampling	`temperature 0.3`, `format: json`
Release eval	150 held-out prompts, 100% valid JSON / 100% step type / 100% guardrail
GGUF SHA256	`a435608db1988fbc8b262276c1e1b359ffbfc4b1433d7db30a27b8736a061b13`

Trained for multi-destination chains ("va à X, puis Y, ensuite Z, puis rentre"), timed pauses (stayAt), returns (returnToOrigin), transport modes, and robustness to messy real-world phrasing (lowercase, no accents, abbreviations). Step type is restricted to moveTo, moveToCategory, moveDirection, stayAt, returnToOrigin.

System prompt (train == serve):

Tu es l'expert de navigation de Teleport Control. Tu traduis les requêtes en scénarios JSON stricts.

Run with Ollama

ollama pull hf.co/lxucan/teleport-nav:Q8_0
ollama run hf.co/lxucan/teleport-nav:Q8_0 "va à la boulangerie, puis à la pharmacie, et rentre"

Output is strict JSON: {name, description, isLooping, steps:[{type, mode, label, …}]}.

Validation

The release GGUF was validated through Ollama as teleport-nav-1.5b on the held-out finetune/data/test.jsonl set:

Metric	Score
JSON validity	100%
Step type correctness	100%
Guardrail recall	100%
Coordinates / direction / road style / transport buckets	100%
Target correctness	89.33%
Overall strict scorer	78.67%

Downloads last month: 41

GGUF

Model size

2B params

Architecture

qwen2

Hardware compatibility

8-bit

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Model tree for lxucan/teleport-nav

Base model

Qwen/Qwen2.5-1.5B

Finetuned

Qwen/Qwen2.5-1.5B-Instruct

Quantized

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