maximaverick/LFM2.5-1.2B-Financial-Analyst-Thinking

LFM2.5-1.2B-Thinking-Financial-Analyst (LFM2.5 1.2B 金融分析专家版)

Overview | 概述

This model is a specialized version of the Liquid LFM2.5-1.2B-Thinking model, fine-tuned to act as a professional Financial Analyst. It is specifically optimized for analyzing Chinese A-share individual stocks, interpreting CFA-level financial principles, and generating structured investment logic.

本模型是基于 Liquid LFM2.5-1.2B-Thinking 的深度微调版本，旨在打造专业的金融分析助手。模型针对中国A股个股咨询、CFA专业财务知识以及结构化投资逻辑进行了深度优化。

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What's New | 模型特性

• Enhanced A-Share Analysis (A股深度分析): Learned the specific narrative style and logic of Chinese equity research reports. 更擅长以中国证券行研报告的风格和逻辑进行个股分析。
• CFA Professional Knowledge (CFA专业知识支撑): Integrated high-quality data covering accounting standards, valuation models, and ethical frameworks from the CFA curriculum. 整合了涵盖会计准则、估值模型和CFA体系下的专业财务知识。
• Thinking Process (逻辑推理过程): Retains and refines the "Thinking" capability of the base model, providing a step-by-step logical deduction before outputting the final financial conclusion. 继承并优化了原模型的“思考”能力，在给出金融结论前进行严密的逻辑推导。

Data & Direction | 微调资料与方向

The fine-tuning involved a vast amount of specialized financial data, moving away from general conversational AI toward a domain-specific expert:

1. Chinese Equity Research (中国行研数据): Massive collection of A-share individual stock analyses and market commentary. 累计了大量A股个股研报及市场评论。
2. CFA Knowledge Base (CFA财务知识库): Structured data on financial statement analysis, corporate finance, and accounting logic. 系统化的财务报表分析、公司理财及会计逻辑数据。
3. Specialized Financial Topics (金融专项课题): Deep dives into niches like Green Bonds (based on 2021 data) and the impact of cross-border capital flows. 涵盖绿色债券（基于2021年数据）及跨境资金流动影响等专项课题。

Origin | 模型渊源

• Base Model (原模型): liquidai/lfm-2.5-1.2b-thinking.
• Transformation (演变): Transformed from a general-purpose reasoning model into a structured, data-driven financial analyst. 从通用型逻辑模型演变为结构化、数据驱动的金融领域专家。

Usage | 使用方法

Option 1: LM Studio (Recommended)

1. Download the .gguf file.
   • LFM2.5-1.2B-Thinking-F16.gguf: Full precision (Best quality, ~2.3GB).
   • LFM2.5-1.2B-Thinking-Q8_0.gguf: 8-bit quantization (Faster, smaller, ~1.3GB).
2. Import via lms import or Drag & Drop.
3. The model is optimized for structured financial queries.

Option 2: Transformers (Python)

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "maximaverick/LFM2.5-1.2B-Financial-Analyst-Thinking" 
model = AutoModelForCausalLM.from_pretrained(model_id, trust_remote_code=True, device_map="cuda")
tokenizer = AutoTokenizer.from_pretrained(model_id)

prompt = "User: 请从CFA财务分析角度，评价某A股公司的现金流质量。\n\nAssistant:"
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
output = model.generate(**inputs, max_new_tokens=1024)
print(tokenizer.decode(output[0]))

Disclaimer | 免责声明

This model is for informational purposes only and does not constitute financial advice. Small models (1.2B) may produce hallucinations; always verify critical data. 本模型仅供参考，不构成任何投资建议。1.2B量级模型可能产生幻觉，请务必核实关键数据。

Project Links

• GitHub Repository: https://github.com/SirusAI/LFM2.5-Financial-Analyst-Finetune.git