Instructions to use CodeStrux-Tech/tac-1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use CodeStrux-Tech/tac-1 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="CodeStrux-Tech/tac-1")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("CodeStrux-Tech/tac-1")
model = AutoModelForCausalLM.from_pretrained("CodeStrux-Tech/tac-1")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use CodeStrux-Tech/tac-1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "CodeStrux-Tech/tac-1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "CodeStrux-Tech/tac-1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/CodeStrux-Tech/tac-1

SGLang

How to use CodeStrux-Tech/tac-1 with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "CodeStrux-Tech/tac-1" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "CodeStrux-Tech/tac-1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "CodeStrux-Tech/tac-1" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "CodeStrux-Tech/tac-1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use CodeStrux-Tech/tac-1 with Docker Model Runner:
```
docker model run hf.co/CodeStrux-Tech/tac-1
```

tac-1 / README.md

Araya2001

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	---
	license: apache-2.0
	language:
	- es
	base_model: Qwen/Qwen3-4B-Instruct-2507
	pipeline_tag: text-generation
	library_name: transformers
	tags:
	- address-parsing
	- structured-output
	- costa-rica
	- geocoding
	- qwen3
	---

	# tac-1 — Costa Rican reference-point address parser

	## What is tac-1

	Costa Rica has no street-address system. Addresses are reference-point narratives — "del antiguo higuerón de San Pedro, 100 metros sur" or "de la esquina noroeste del Mercado Central, 200 metros norte, casa esquinera." tac-1 parses these into a structured, leg-based JSON record. It performs parsing only: geometry (resolving the anchor point to coordinates and walking the OSM street graph along each leg) belongs to a separate deterministic grounder (the companion `tico` package, not part of this model release).

	## Architecture

	tac-1 is a hybrid system. This model extracts the structured address fields (anchor name, anchor type, and a list of legs with direction and distance). The `tico` package then grounds the parse by walking the OSM street graph — it resolves the anchor to a coordinate via `resolve_anchor`, then traces each leg along the graph using the same geometry core that generated the training data.

	## Evaluation

	### gold-47 vs teacher

	\| metric \| tac-1 (bf16) \| tac-1 (FP8) \| teacher gpt-4.1 \|
	\|---\|---\|---\|---\|
	\| parse rate \| 1.000 \| 1.000 \| 1.000 \|
	\| anchor_name F1 \| 0.979 \| 0.979 \| 0.872 \|
	\| direction F1 \| 0.995 \| 0.984 \| 1.000 \|
	\| distance F1 \| 0.995 \| 0.984 \| 1.000 \|
	\| perfect-anchor ≤250 m \| 93.6% \| 93.6% \| 93.6% \|
	\| perfect-anchor median \| 0.0 m \| 0.0 m \| 0.0 m \|
	\| gazetteer ≤250 m \| 80.9% \| 80.9% \| 83.0% \|

	Headline: anchor_name F1 beats the gpt-4.1 teacher, with zero geographic overfit on never-trained districts.

	### heldout-805

	\| metric \| tac-1 (bf16) \|
	\|---\|---\|
	\| parse rate \| 1.000 \|
	\| anchor_name F1 \| 1.000 \|
	\| direction F1 \| 1.000 \|
	\| distance F1 \| 1.000 \|
	\| perfect-anchor median \| 89.5 m \|
	\| perfect-anchor ≤250 m \| 81.0% \|
	\| gazetteer median \| 148.8 m \|
	\| gazetteer ≤250 m \| 65.0% \|

	Grounding-baseline medians on heldout-805: hybrid_walk 143.8 m vs calibrated_project 198.7 m vs naive_project 201.9 m (the graph walk beats projection on unseen geography).

	The three never-trained districts (grecia, curridabat, go-guadalupe) show anchor_name/direction/distance F1 all at 1.000 — zero geographic overfit.

	## Quickstart

	```bash
	vllm serve CodeStrux-Tech/tac-1 --served-model-name tac-1 --max-model-len 4096
	# On hosts without nvcc, disable the FlashInfer sampler JIT:
	# VLLM_USE_FLASHINFER_SAMPLER=0 vllm serve ...
	```

	```python
	from tico.extractor.clients.vllm import VLLMExtractor

	extractor = VLLMExtractor() # VLLM_BASE_URL=http://localhost:8000/v1, TICO_VLLM_MODEL=tac-1
	addr = extractor.extract("del antiguo higuerón de San Pedro, 100 metros sur")
	print(addr.model_dump_json(indent=2))
	```

	## Limitations

	- confidence is a constant 0.0 — uncalibrated; do not consume this value.
	- anchor lat/lon/osm_id are always null by design — coordinates belong to the grounder's `resolve_anchor`, not the model.
	- Spanish/Costa-Rica-specific — the model was trained and evaluated exclusively on Costa Rican Spanish reference-point addresses.
	- Remaining system headroom is gazetteer/calibration coverage, not the model — the extractor is at or near ceiling on the evaluation sets; further accuracy gains require broader OSM ingestion and calibration.

	## Training

	- Base model: Qwen/Qwen3-4B-Instruct-2507
	- Method: QLoRA (r=16, α=32)
	- Learning rate: 2e-4
	- Epochs: 2
	- Max sequence length: 4096
	- Steps: 692
	- Final train loss: 0.043
	- Hardware: ~2 h 50 m on an RTX 4080 16 GB
	- Stack: unsloth 2025.11.1 / transformers 4.57.2 / trl 0.23.0
	- Corpus: `CodeStrux-Tech/tac-1-corpus` — 5,532 train examples (seed 0), 805 heldout examples (seed 1); `--max-legs 4`; 22 districts ingested, 19,042 POIs, 11 griddable; holdout districts grecia, curridabat, go-guadalupe excluded from training.

	### Training data attribution

	Contains information from OpenStreetMap (https://www.openstreetmap.org/copyright), which is made available under the Open Database License (ODbL) 1.0. © OpenStreetMap contributors.

	Licensing. The tac-1 corpus is a derivative database of OpenStreetMap and is released under the Open Database License (ODbL) 1.0, with attribution: © OpenStreetMap contributors (https://www.openstreetmap.org/copyright). The tac-1 model weights are a produced work trained on that corpus and are released under Apache-2.0; the corpus's ODbL provenance is disclosed in every model card. Code is Apache-2.0 (LICENSE); data is ODbL (DATA_LICENSE); see NOTICE. Sources are OpenStreetMap + INEC/IGN + community DTA tables only — no Google or Waze data anywhere in the pipeline, training data, or release.

	## Related

	- [`CodeStrux-Tech/tac-1-fp8-dynamic`](https://huggingface.co/CodeStrux-Tech/tac-1-fp8-dynamic) — FP8 dynamic quantization
	- [`CodeStrux-Tech/tac-1-gguf`](https://huggingface.co/CodeStrux-Tech/tac-1-gguf) — GGUF (Q5_K_M + f16) for llama.cpp / Ollama
	- [`CodeStrux-Tech/tac-1-lora`](https://huggingface.co/CodeStrux-Tech/tac-1-lora) — QLoRA adapter weights
	- [`CodeStrux-Tech/tac-1-corpus`](https://huggingface.co/CodeStrux-Tech/tac-1-corpus) — training corpus (ODbL)

	tac-1 is a derivative work of [Qwen/Qwen3-4B-Instruct-2507](https://huggingface.co/Qwen/Qwen3-4B-Instruct-2507), Copyright 2024 Alibaba Cloud, licensed under the Apache License, Version 2.0. The upstream LICENSE is included in this repository.