71 lines
4.2 KiB
Markdown
71 lines
4.2 KiB
Markdown
---
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language:
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- zh
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- en
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license: apache-2.0
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tags:
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- finance
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- a-share
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- cfa
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- lfm
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- liquid
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- fine-tuned
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base_model: liquidai/lfm-2.5-1.2b-thinking
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pipeline_tag: text-generation
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library_name: transformers
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---
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# LFM2.5-1.2B-Thinking-Financial-Analyst (LFM2.5 1.2B 金融分析专家版)
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## Overview | 概述
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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.
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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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---
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## What's New | 模型特性
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- **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。
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- **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。
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- **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。
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## Data & Direction | 微调资料与方向
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The fine-tuning involved a vast amount of specialized financial data, moving away from general conversational AI toward a domain-specific expert:
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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。
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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。
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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。
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## Origin | 模型渊源
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- **Base Model (原模型)**: `liquidai/lfm-2.5-1.2b-thinking`.
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- **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。
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## Usage | 使用方法
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### Option 1: LM Studio (Recommended)
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1. Download the **.gguf** file.
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- `LFM2.5-1.2B-Thinking-F16.gguf`: Full precision (Best quality, ~2.3GB).
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- `LFM2.5-1.2B-Thinking-Q8_0.gguf`: 8-bit quantization (Faster, smaller, ~1.3GB).
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2. Import via `lms import` or Drag & Drop.
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3. The model is optimized for structured financial queries.
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### Option 2: Transformers (Python)
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model_id = "maximaverick/LFM2.5-1.2B-Financial-Analyst-Thinking"
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model = AutoModelForCausalLM.from_pretrained(model_id, trust_remote_code=True, device_map="cuda")
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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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:"
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inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
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output = model.generate(**inputs, max_new_tokens=1024)
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print(tokenizer.decode(output[0]))
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```
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## Disclaimer | 免责声明
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*This model is for informational purposes only and does not constitute financial advice. Small models (1.2B) may produce hallucinations; always verify critical data.*
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*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。*
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## Project Links
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- **GitHub Repository**: [https://github.com/SirusAI/LFM2.5-Financial-Analyst-Finetune.git](https://github.com/SirusAI/LFM2.5-Financial-Analyst-Finetune.git)
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