Qwen2.5-0.5B-Instruct Financial JSON Extractor

A fine-tuned version of Qwen/Qwen2.5-0.5B-Instruct trained with QLoRA and merged into standalone FP16 weights. Given a financial tweet or headline, it outputs strict JSON containing market sentiment (bearish, bullish, neutral) and extracted uppercase stock cashtags.

Evaluation Results

Split / Setup	JSON-Valid %	Sentiment Macro-F1	Ticker Exact-Match
Zero-Shot Baseline (`Qwen2.5-0.5B-Instruct`)	83.0%	0.257	0.060
Internal Validation Split (Day 4 QLoRA)	100.0%	0.862	0.996
Official Held-Out Test Split (`validation`, $N=2,388$)	99.9%	0.863	0.994

Known Limitations & Edge Cases

Max Token Truncation: The generation limit is set to max_new_tokens=64. Tweets exceptionally dense with cashtags (15+ tickers) will have their ticker lists truncated mid-array. (Recommended to raise to max_new_tokens=128 if processing long-form multi-ticker posts in production).

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

MODEL_ID = "Badrdeen/qwen-financial-json-extractor"
tok = AutoTokenizer.from_pretrained(MODEL_ID)
model = AutoModelForCausalLM.from_pretrained(MODEL_ID, torch_dtype=torch.float16, device_map="auto")

SYSTEM = (
    'Return only a JSON object with two keys: "sentiment" (one of bearish, bullish, neutral) '
    'and "tickers" (list of cashtag symbols in the text, uppercase, without $; [] if none). No other text.'
)

msgs = [
    {"role": "system", "content": SYSTEM},
    {"role": "user", "content": "$NVDA surges 8\% on strong AI demand while$INTC slips."}
]

prompt = tok.apply_chat_template(msgs, tokenize=False, add_generation_prompt=True)
inputs = tok(prompt, return_tensors="pt").to(model.device)
out = model.generate(**inputs, max_new_tokens=128, do_sample=False)

print(tok.decode(out[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True))

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