This model is a parameter-efficient fine-tuned version of Qwen3-8B, adapted using LoRA (Low-Rank Adaptation) for financial sentiment analysis.

It is trained as an instruction-following text generation model that classifies financial sentences into:

positive
negative
neutral

The model generates a single-word output, making it suitable for lightweight classification pipelines while retaining generative flexibility.

Developed by: Authors of the referenced paper
Model type: Causal Language Model (decoder-only transformer)
Language(s): English (financial domain)
License: Same as base model (Qwen license – verify before use)
Finetuned from model: Qwen/Qwen3-8B

Model Sources

Base Model: Qwen/Qwen3-8B
Paper: https://arxiv.org/pdf/2512.00946

Uses

Direct Use

This model is intended for:

Financial sentiment classification
Text generation with constrained outputs
Research on instruction-tuned LLM classifiers

Example applications:

Market sentiment analysis
News sentiment tagging
Signal generation for trading pipelines

Downstream Use

Integration into trading or analytics systems
Combination with time-series models (e.g., LSTM, TFT)
Use in RAG pipelines for financial reasoning

Out-of-Scope Use

High-stakes financial decision-making without validation
Legal or medical analysis
Tasks requiring guaranteed factual correctness
General sentiment analysis outside financial domain (may degrade)

Prompt Format Requirement

This model is highly sensitive to prompt structure.

It was trained using a fixed instruction template, and performance depends heavily on using the same format at inference time.

Required Prompt

text

You are an AI language model trained to detect the sentiment of each sentence for finance trends.
Analyze the following sentence and determine if the sentiment is: positive or negative or neutral.
Return only a single word, either positive or negative or neutral.

<your input text>

Alvaropar/FinQwen3-8B-adapter

Model Card for Qwen3-8B Finetuned (Financial Sentiment - LoRA)

Model Details

Model Description