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_0Use 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_0Build 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_0Use Docker
docker model run hf.co/lxucan/teleport-nav:Q8_0teleport-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% |
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8-bit
Install (macOS, Linux)
# 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