metadata
license: mit
base_model: Qwen/Qwen2.5-3B-Instruct
language:
- en
pipeline_tag: text-generation
tags:
- football
- sports
- json-extraction
- gguf
- lora
- qwen2.5
library_name: llama-cpp
forecast-extractor
A fine-tuned version of Qwen2.5-3B-Instruct for extracting structured JSON from football prediction messages (e.g. Telegram tip channels).
What it does
Given a raw football prediction message, it returns a structured JSON array:
[
{
"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)
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
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.