| --- |
| base_model: unsloth/Qwen2.5-3B-Instruct-bnb-4bit |
| library_name: transformers |
| tags: |
| - unsloth |
| - trl |
| - qlora |
| - astrology |
| - vedic-astrology |
| - hindi |
| - hinglish |
| - lora |
| license: apache-2.0 |
| --- |
| |
| # Vedaz: AI Vedic Astrologer |
|
|
| Vedaz is a fine-tuned LoRA adapter on top of `unsloth/Qwen2.5-3B-Instruct-bnb-4bit`, designed to provide **compassionate**, **empathetic**, and **balanced** Vedic astrological guidance. |
| It focuses on sensitive life areas such as career, relationships, finance, health, and spiritual growth, while maintaining clear ethical boundaries and avoiding fatalistic or harmful predictions. |
|
|
| --- |
|
|
| ## Model Details |
|
|
| - **Developed by:** FinetunerHegde |
| - **Model type:** Causal Language Model (LoRA fine-tuned adapter) |
| - **Base model:** Qwen2.5-3B-Instruct (4-bit, via Unsloth) |
| - **Languages:** English, Hindi, Hinglish (code-mixed) |
| - **Training framework:** Unsloth + TRL (QLoRA) |
| - **Intended domain:** Vedic astrology chat assistant |
| - **Input style:** Instruction/chat format (system + user messages) |
|
|
| ### Model Motivation |
|
|
| The goal of Vedaz is to make Vedic astrology guidance more accessible, gentle, and practical for everyday life decisions. |
| Instead of deterministic predictions, the model encourages reflection, self-awareness, and constructive actions aligned with the user’s context. |
|
|
| --- |
|
|
| ## Quick Start (Unsloth Inference) |
|
|
| Vedaz is a LoRA adapter and should be loaded with the base model using the `unsloth` library for best performance and low memory usage. |
|
|
| ```python |
| from unsloth import FastLanguageModel |
| import torch |
| |
| model, tokenizer = FastLanguageModel.from_pretrained( |
| model_name = "FinetunerHegde/vedaz", |
| max_seq_length = 2048, |
| load_in_4bit = True, |
| ) |
| FastLanguageModel.for_inference(model) |
| |
| messages = [ |
| { |
| "role": "system", |
| "content": ( |
| "You are Vedaz, an AI Vedic astrologer. " |
| "You give compassionate, balanced guidance based on Vedic principles. " |
| "You avoid absolute predictions and encourage practical, ethical actions." |
| ), |
| }, |
| { |
| "role": "user", |
| "content": "Meri shaadi kab hogi? DOB: 15 August 1996, 7:00 AM, Pune.", |
| }, |
| ] |
| |
| inputs = tokenizer.apply_chat_template( |
| messages, |
| tokenize=True, |
| add_generation_prompt=True, |
| return_tensors="pt", |
| ).to("cuda") |
| |
| outputs = model.generate( |
| input_ids=inputs, |
| max_new_tokens=300, |
| temperature=0.7, |
| do_sample=True, |
| ) |
| |
| print(tokenizer.decode(outputs[inputs.shape[-1]:], skip_special_tokens=True)) |
| ``` |
|
|
| --- |
|
|
| ## Recommended Prompting Style |
|
|
| For best results: |
|
|
| - Provide **DOB, time, and place** of birth in a single message. |
| - Mention the **area of concern** (career, marriage, finance, health, spiritual growth, etc.). |
| - Use natural language in English, Hindi, or Hinglish. |
|
|
| Example prompts: |
|
|
| - `"DOB: 02 Jan 1998, 3:45 PM, Bengaluru. Career-wise next 2–3 years ka overview batao."` |
| - `"Meri relationship ke bare mein guidance chahiye. DOB: 10 March 1995, 9:15 AM, Delhi."` |
|
|
| --- |
|
|
| ## Intended Uses |
|
|
| ### Direct Use |
|
|
| - Vedic astrology–style chat assistant for: |
| - High-level life guidance and reflection |
| - Relationship and career counseling in a gentle, non-fatalistic manner |
| - Spiritual and emotional support aligned with Vedic concepts |
|
|
| ### Downstream Use |
|
|
| - Integrating Vedaz into: |
| - Telegram/WhatsApp/Discord bots |
| - Web apps (e.g., Gradio/Streamlit frontends) |
| - Personal assistants or dashboards that offer astrology-themed insights |
|
|
| ### Out-of-Scope Use |
|
|
| Vedaz **must not** be used for: |
|
|
| - Medical diagnosis, emergency advice, or mental health treatment |
| - Financial, legal, or investment decisions where professional advice is required |
| - Absolute predictions about death, accidents, or harmful outcomes |
| - Any form of discrimination, hate speech, or harassment |
|
|
| --- |
|
|
| ## Bias, Risks, and Limitations |
|
|
| - The model is trained on curated chat data with Vedic astrology–style content and may reflect biases present in the training set. |
| - It can generate culturally specific interpretations which may not align with all belief systems or personal philosophies. |
| - Outputs are **not** guaranteed to be accurate, and should be treated as guidance or reflection, not as “truth”. |
|
|
| ### Recommendations |
|
|
| - Always treat the model as a **supportive conversational tool**, not an authority. |
| - Cross-check important decisions with qualified professionals (career counselors, doctors, financial advisors, etc.). |
| - Avoid using the model with vulnerable users without proper supervision or safety layers. |
|
|
| --- |
|
|
| ## Training Details |
|
|
| > Note: The following is a high-level overview. Update with exact numbers if available. |
|
|
| ### Training Data |
|
|
| - Custom chat dataset focused on: |
| - Vedic astrology–style Q&A |
| - Conversational guidance on life topics |
| - Hindi, English, and Hinglish mixed dialogues |
| - Sensitive topics (health, relationships, finance) labeled or filtered to encourage empathetic and safe responses. |
|
|
| ### Training Procedure |
|
|
| - **Method:** QLoRA fine-tuning on `unsloth/Qwen2.5-3B-Instruct-bnb-4bit` |
| - **Frameworks:** Unsloth + TRL |
| - **Objective:** Instruction-following and chat-style responses with strong safety and empathy constraints. |
|
|
| ### Example Hyperparameters (to customize) |
|
|
| - LoRA rank and alpha tuned for compactness |
| - Max sequence length: 2048 tokens |
| - Optimization target: fast inference on consumer GPUs using 4-bit quantization. |
|
|
| --- |
|
|
| ## Evaluation |
|
|
| > This section is indicative. Fill in with your actual evaluation setup and metrics when available. |
|
|
| - **Qualitative evaluation:** |
| - Manually reviewed responses for empathy, safety, and adherence to Vedic framing. |
| - Checked behavior on sensitive queries (marriage, job loss, health anxiety). |
|
|
| - **Key focus areas:** |
| - Non-fatalistic guidance |
| - Constructive, actionable suggestions |
| - Avoidance of explicit harmful content |
|
|
| --- |
|
|
| ## Technical Specifications |
|
|
| ### Architecture |
|
|
| - Base: Qwen2.5-3B-Instruct (decoder-only transformer) |
| - Adapter: LoRA layers via Unsloth (QLoRA on 4-bit base model) |
| - Max context: 2048 tokens (recommended) |
|
|
| ### Hardware & Inference |
|
|
| - Optimized for: |
| - Single consumer GPU (e.g., 8–12 GB VRAM) |
| - Fast inference with 4-bit quantization |
| - Compatible with: |
| - `unsloth` API for loading and generation |
| - Standard Hugging Face ecosystem via adapters |
|
|
| --- |
|
|
| ## Ethical Considerations |
|
|
| - Vedaz is designed to **support** users emotionally, not to control or dictate life choices. |
| - It should always: |
| - Encourage self-responsibility and practical steps. |
| - Avoid deterministic or fear-based predictions. |
| - Respect user autonomy and diversity of beliefs. |
|
|
| If you deploy Vedaz publicly, please clearly mention that it is an AI system and not a certified astrologer, therapist, or advisor. |
|
|
| --- |
|
|
| ## Citation |
|
|
| If you use Vedaz in academic work, projects, or demos, you can cite it as: |
|
|
| ```bibtex |
| @misc{vedaz2026, |
| title = {Vedaz: AI Vedic Astrologer (LoRA on |
