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
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

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 the deterministic grounder in the tico-address repo (https://github.com/CodeStrux/tac-1).

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. See https://github.com/CodeStrux/tac-1 for the full grounder implementation.

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

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 ...

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.

CodeStrux-Tech/tac-1-fp8-dynamic — FP8 dynamic quantization
CodeStrux-Tech/tac-1-gguf — GGUF (Q5_K_M + f16) for llama.cpp / Ollama
CodeStrux-Tech/tac-1-lora — QLoRA adapter weights
CodeStrux-Tech/tac-1-corpus — training corpus (ODbL)
Source code: https://github.com/CodeStrux/tac-1

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Safetensors

Model size

4B params

Tensor type

BF16

Model tree for CodeStrux-Tech/tac-1

Base model

Qwen/Qwen3-4B-Instruct-2507

Finetuned

(1822)

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Quantizations

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