[
  {
    "league": "La Liga",
    "team_1": "Real Madrid",
    "team_2": "Barcelona",
    "prediction": "1X",
    "date": "25/03/2026",
    "odds": 1.42
  }
]

Handles:

Single and multi-tip messages (up to 4 tips)
Bold unicode text (Telegram formatting)
Missing fields → null
Varied formats, emojis, noise

Models

File	Size	Description
`football-extractor-q4.gguf`	1.8GB	Q4_K_M quantized — recommended
`football-extractor-f16.gguf`	5.8GB	Full f16 precision

Quick start

With llama-cpp-python (recommended)

python

from llama_cpp import Llama
import json

llm = Llama(model_path="football-extractor-q4.gguf", n_ctx=2048, n_gpu_layers=-1)

response = llm.create_chat_completion(
    messages=[
        {"role": "system", "content": "Extract structured data and return ONLY a valid JSON array. Keys: league, team_1, team_2, prediction, date, odds. Use null for missing fields."},
        {"role": "user", "content": "YOUR TIP TEXT HERE"}
    ],
    temperature=0.0,
    max_tokens=512,
)
print(json.loads(response["choices"][0]["message"]["content"]))

With Ollama

bash

ollama pull philippotiger/forecast-extractor
ollama run philippotiger/forecast-extractor

Training details

Base model: Qwen/Qwen2.5-3B-Instruct
Method: QLoRA (4-bit NF4) with LoRA r=8
Dataset: 300 synthetic examples generated from real team data
- 70% single-tip, 30% multi-tip (2-4 events)
- 10 message templates with emoji injection, typos, missing fields
Epochs: 3
Final val loss: ~0.24

Intended use

Parsing football prediction messages from Telegram channels or similar sources into structured data for further processing or storage.