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.

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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.

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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.

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))

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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."

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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

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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.

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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.

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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

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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

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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.

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Citation

If you use Vedaz in academic work, projects, or demos, you can cite it as:

@misc{vedaz2026,
  title        = {Vedaz: AI Vedic Astrologer (LoRA on