Point your phone at a gig poster pinned to a wall, and this model returns structured JSON with the event name, venue, date, start time, and a short description — ready to turn into a calendar invite.

Built for the Build Small Hackathon (June 2026), GigScan competes in the Backyard AI track.

Model description

Base model: openbmb/MiniCPM-V-4.6 (1.3B parameters, multimodal vision-language)
Fine-tuning method: LoRA (all linear layers) via LLaMA-Factory
Training data: 276 labeled gig poster images + 110 non-poster negatives
Format: Q4_K_M quantized GGUF for local CPU/GPU inference with llama.cpp

Intended use

GigScan is designed to be deployed in a mobile-friendly web app where users photograph gig posters and receive structured event data. The intended output is:

json

{
  "is_live_music_poster": true,
  "event_name": "The Chats",
  "venue": "The Tote",
  "date": "06-06",
  "time_start": "20:00",
  "description": "Live at The Tote with Leatherman. Tickets $20."
}

Non-poster images return:

json

{
  "is_live_music_poster": false,
  "event_name": "",
  "venue": null,
  "date": null,
  "time_start": null,
  "description": ""
}

Performance

Evaluated on a held-out test set of 70 images (47 positives, 23 negatives):

Metric	Base Model	Fine-tuned
Valid JSON output	92.9%	100%
All fields present	92.9%	100%
Date format (DD-MM)	34.3%	100%
Time format (HH:MM)	81.4%	100%
Poster detection accuracy	78.6%	100%
Hallucination on negatives	Yes	None

The fine-tuned model eliminated date formatting errors, poster misclassification, and hallucinated events on non-poster images entirely on the test set.

Training procedure

Training data

The dataset consists of 386 labeled images:

Positives (276): Scraped gig posters from Instagram and other sources, labeled automatically by GPT-5-nano with the target JSON schema, then manually reviewed.
Negatives (110): Non-poster images sampled from Food101 and Places365 datasets.

The dataset is available at kieranadair/gigscan-training.

Training configuration

Parameter	Value
Framework	LLaMA-Factory
Method	LoRA (all linear layers)
Epochs	3
Batch size (effective)	8
Learning rate	5e-5
Warmup	10% of total steps
Precision	BF16
GPU	NVIDIA A10G (24 GB)
Training time	~45 minutes

Conversion to GGUF

The merged LoRA model was converted to GGUF using llama.cpp's convert_hf_to_gguf.py (release b9049+) then quantized to Q4_K_M:

bash

python convert_hf_to_gguf.py /merged-model --outfile gigscan-f16.gguf --outtype f16
python convert_hf_to_gguf.py /merged-model --mmproj --outfile mmproj-gigscan-f16.gguf
./llama-quantize gigscan-f16.gguf gigscan-q4_k_m.gguf Q4_K_M

The vision projector (mmproj) is kept at F16 precision. The language model is quantized to approximately 900 MB.

How to use

With llama.cpp server

bash

./llama-server \
  -m gigscan-q4_k_m.gguf \
  --mmproj mmproj-gigscan-f16.gguf \
  -c 4096 \
  -ngl 999 \
  --reasoning-budget 0 \
  --host 127.0.0.1 \
  --port 8080

Then send requests to http://127.0.0.1:8080/v1/chat/completions:

python

import requests

response = requests.post("http://127.0.0.1:8080/v1/chat/completions", json={
    "model": "gigscan-minicpm-v",
    "temperature": 0,
    "max_tokens": 300,
    "messages": [
        {"role": "system", "content": "Return only the answer."},
        {"role": "user", "content": [
            {"type": "image_url", "image_url": {"url": "data:image/jpeg;base64,..."}},
            {"type": "text", "text": "Extract gig poster details. Return JSON."}
        ]}
    ]
})

print(response.json()["choices"][0]["message"]["content"])

Recommended prompt

For best results, use a prompt that explicitly lists the expected keys and allows null for missing fields. The fine-tuned model was trained on "Extract gig poster details from this image." but benefits from a more detailed prompt at inference time that specifies the JSON contract and null behavior.

Limitations

Poster format: Trained primarily on clean digital poster images. Performance may degrade on blurry, angled, or poorly lit real-world photos.
Single event assumption: Designed for single-event posters. Multi-date tour posters or festival lineups may produce unreliable results.
Language: Training data is English-language posters only.
Date inference: The model returns DD-MM format. Year inference is handled by application logic, not the model.
Venue and time extraction: Can be conservative — missing venue or time is returned as null rather than guessed. This is intentional.

Deployment

GigScan is deployed as a Hugging Face Space at kieranadair/gigscan. The Space runs the quantized model locally via llama.cpp server on a T4 GPU.

Acknowledgements

Built with MiniCPM-V 4.6 by OpenBMB
Fine-tuned with LLaMA-Factory
Inference powered by llama.cpp
Training infrastructure provided by Modal
Built for the Build Small Hackathon

build-small-hackathon/gigscan-minicpm-v-gguf

GigScan — Fine-tuned MiniCPM-V 4.6 for Gig Poster Extraction