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--- |
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language: |
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- en |
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license: apache-2.0 |
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tags: |
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- brahma-kumaris |
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- murli |
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- spiritual |
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- lora |
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- phi-2 |
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base_model: microsoft/phi-2 |
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datasets: |
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- custom |
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library_name: peft |
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--- |
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# Murli Assistant - Fine-tuned Phi-2 with LoRA |
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This model is a fine-tuned version of [microsoft/phi-2](https://huggingface.co/microsoft/phi-2) using LoRA (Low-Rank Adaptation) on Brahma Kumaris Murli data. |
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## Model Description |
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- **Base Model:** microsoft/phi-2 (2.7B parameters) |
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- **Fine-tuning Method:** LoRA (r=8, alpha=16) |
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- **Training Data:** 100+ daily murlis from MongoDB database |
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- **Use Case:** Spiritual guidance and murli knowledge assistant |
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## Training Details |
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- **LoRA Rank (r):** 8 |
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- **LoRA Alpha:** 16 |
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- **Target Modules:** q_proj, o_proj, k_proj, v_proj |
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- **Training Examples:** 201 formatted instructions |
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- **Adapter Size:** ~15MB |
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## Usage |
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```python |
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from transformers import AutoTokenizer, AutoModelForCausalLM |
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from peft import PeftModel |
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import torch |
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# Load base model |
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base_model = AutoModelForCausalLM.from_pretrained( |
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"microsoft/phi-2", |
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torch_dtype=torch.float16, |
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device_map="auto", |
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trust_remote_code=True |
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) |
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# Load LoRA adapter |
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model = PeftModel.from_pretrained(base_model, "eswarankrishnamurthy/murli-assistant-phi2-lora") |
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tokenizer = AutoTokenizer.from_pretrained("microsoft/phi-2") |
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tokenizer.pad_token = tokenizer.eos_token |
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# Generate response |
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question = "What is the essence of today's murli?" |
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prompt = f"### Instruction:\n{question}\n\n### Response:\n" |
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inputs = tokenizer(prompt, return_tensors="pt").to(model.device) |
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outputs = model.generate(**inputs, max_new_tokens=200) |
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response = tokenizer.decode(outputs[0], skip_special_tokens=True) |
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print(response) |
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``` |
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## Inference API |
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This model is also available via Hugging Face Inference API: |
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```python |
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import requests |
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API_URL = "https://api-inference.huggingface.co/models/eswarankrishnamurthy/murli-assistant-phi2-lora" |
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headers = {"Authorization": f"Bearer {YOUR_HF_TOKEN}"} |
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def query(payload): |
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response = requests.post(API_URL, headers=headers, json=payload) |
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return response.json() |
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output = query({"inputs": "What is soul consciousness?"}) |
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print(output) |
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``` |
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## Training Information |
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The model was trained on diverse murli content including: |
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- Daily murli essence |
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- Blessings and slogans |
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- Questions and answers |
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- Spiritual teachings and guidance |
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## Limitations |
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- Best performance on spiritual/murli-related queries |
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- May require GPU for faster inference |
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- CPU inference is possible but slower |
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## Citation |
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If you use this model, please cite: |
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``` |
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@misc{murli-assistant-phi2, |
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author = {eswarankrishnamurthy}, |
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title = {Murli Assistant - Fine-tuned Phi-2}, |
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year = {2025}, |
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publisher = {Hugging Face}, |
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url = {https://huggingface.co/eswarankrishnamurthy/murli-assistant-phi2-lora} |
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} |
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``` |
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## Contact |
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For questions or feedback, please open an issue on the model repository. |
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