Repository: localaiLicense: mit
This model is a fine-tuned version of Qwen/Qwen3-8B designed to answer personal finance queries. It has been trained on a specialized dataset of real Reddit queries with synthetically curated responses, focusing on understanding both the financial necessities and the psychological context of the user. The model aims to provide empathetic and practical advice for a wide range of personal finance topics. It leverages a base model's strong language understanding and generation capabilities, further enhanced by targeted fine-tuning on domain-specific data. A key feature of this model is its training to consider the emotional and psychological state of the person asking the query, alongside the purely financial aspects.
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Repository: localaiLicense: llama3.1
Model has been further finetuned on a set of newly generated 50m high quality tokens related to Financial topics covering topics such as Economics, Fixed Income, Equities, Corporate Financing, Derivatives and Portfolio Management. Data was gathered from publicly available sources and went through several stages of curation into instruction data from the initial amount of 250m+ tokens. To aid in mitigating forgetting information from the original finetune, the data was mixed with instruction sets on the topics of Coding, General Knowledge, NLP and Conversational Dialogue. The model has shown to improve over a number of benchmarks over the original model, notably in Math and Economics. This model represents the first time a 8B model has been able to convincingly get a passing score on the CFA Level 1 exam, requiring a typical 300 hours of studying, indicating a significant improvement in Financial Knowledge.
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Repository: localaiLicense: apache-2.0
### **Financial GPT-OSS 20B (Base Model)** **Model Type:** Causal Language Model (Fine-tuned for Financial Analysis) **Architecture:** Mixture of Experts (MoE) – 20B parameters, 32 experts (4 active per token) **Base Model:** `unsloth/gpt-oss-20b-unsloth-bnb-4bit` **Fine-tuned With:** LoRA (Low-Rank Adaptation) on financial conversation data **Training Data:** 22,250 financial dialogue pairs covering stocks (AAPL, NVDA, TSLA, etc.), technical analysis, risk assessment, and trading signals **Context Length:** 131,072 tokens **Quantization:** Q8_0 GGUF (for efficient inference) **License:** Apache 2.0 **Key Features:** - Specialized in financial market analysis: technical indicators (RSI, MACD), risk assessments, trading signals, and price forecasts - Handles complex financial queries with structured, actionable insights - Designed for real-time use with low-latency inference (GGUF format) - Supports S&P 500 stocks and major asset classes across tech, healthcare, energy, and finance sectors **Use Case:** Ideal for traders, analysts, and developers building financial AI tools. Use with caution—**not financial advice**. **Citation:** ```bibtex @misc{financial-gpt-oss-20b-q8, title={Financial GPT-OSS 20B Q8: Fine-tuned Financial Analysis Model}, author={beenyb}, year={2025}, publisher={Hugging Face Hub}, url={https://huggingface.co/beenyb/financial-gpt-oss-20b-q8} } ```
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