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hadioma/RetrainedQwen3-8-FinGPT-SentimentSpecialist

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FinGPT — Qwen3-8B Sentiment Specialist (Round 1 LoRA)

A LoRA adapter fine-tuned on Qwen3-8B for financial sentiment analysis, trained on the FinGPT sentiment dataset.

Part of a self-taught multi-agent financial AI project. Full training code, architecture notes, and roadmap available on GitHub: 👉 [github.com/hadioma/FinGPT-Portfolio](https://github.com/hadioma/FinGPT-Portfolio)

What This Model Does

Classifies financial news and tweets into one of three sentiment labels:

—positive
—neutral
—negative

Example

python

from unsloth import FastLanguageModel
from safetensors.torch import load_file
from peft import set_peft_model_state_dict

# Load base model
model, tokenizer = FastLanguageModel.from_pretrained(
    model_name="unsloth/Qwen3-8B",
    max_seq_length=512,
    load_in_4bit=True,
)
model = FastLanguageModel.get_peft_model(
    model, r=8,
    target_modules=["q_proj","k_proj","v_proj","o_proj",
                    "gate_proj","up_proj","down_proj"],
    lora_alpha=16, lora_dropout=0, bias="none",
    use_gradient_checkpointing=False,
)

# Load this adapter
state_dict = load_file(hf_hub_download(
    "hadioma/RetrainedQwen3-7-FinGPT", "adapter_model.safetensors"
))
set_peft_model_state_dict(model, state_dict)
FastLanguageModel.for_inference(model)

# Run inference
SYSTEM = ("You are an expert financial analyst. "
          "Reason carefully, cite your logic, and provide structured, professional analysis.")

messages = [
    {"role": "system", "content": SYSTEM},
    {"role": "user",   "content": 
     "What is the sentiment of this news? Please choose an answer from "
     "{negative/neutral/positive}.\n"
     "Input: Apple reported record quarterly earnings, beating analyst expectations by 15%."},
]
text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(text, return_tensors="pt").to(model.device)

outputs = model.generate(**inputs, max_new_tokens=10, do_sample=False)
response = tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:], skip_special_tokens=True)
print(response)  # → positive

Training Details

Parameter	Value
Base model	`unsloth/Qwen3-8B` (4-bit quantized)
LoRA rank	r=8, alpha=16
Target modules	q/k/v/o/gate/up/down proj
Dataset	`FinGPT/fingpt-sentiment-train` (76.8K samples)
Max sequence length	512
Batch size (effective)	16 (1 device × 16 grad accum)
Epochs	1 (~10,131 steps)
Optimizer	pagedadamw8bit
Precision	bf16
Hardware	NVIDIA RTX 5070 Ti Laptop GPU (12GB VRAM)
Framework	Unsloth 2026.2 + HuggingFace PEFT 0.18

System Prompt Used During Training

You are an expert financial analyst. Reason carefully, cite your logic, 
and provide structured, professional analysis.

⚠️ You must use this exact system prompt at inference time for best results.

Intended Use

—Financial news sentiment classification
—Part of a multi-agent financial NLP pipeline
—Component in the FinGPT ecosystem

Out-of-Scope Use

—General-purpose chat or instruction following (use Round 2 adapter for that)
—Non-financial text sentiment
—Real trading decisions — this is not financial advice

Multi-Agent Architecture

This adapter is Agent 1 in a planned multi-agent system:

Base Qwen3-8B (frozen)
  ├── adapter_round1 ← this model  (sentiment specialist)
  ├── adapter_round2              (sentiment + Q&A + headline)
  └── adapter_forecaster          (stock price forecasting, planned)

All agents share the same frozen base model. The orchestrator hot-swaps adapters at inference time with no model reload.

Citation

If you use this model, please also cite the FinGPT paper:

bibtex

@article{yang2023fingpt,
  title={FinGPT: Open-Source Financial Large Language Models},
  author={Yang, Hongyang and Liu, Xiao-Yang and Wang, Christina Dan},
  journal={FinLLM Symposium at IJCAI 2023},
  year={2023}
}