Upload DistilGPT-2 Murli Assistant (Ultra-Lite)

README.md

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+---
+language: en
+license: mit
+tags:
+- spiritual-ai
+- brahma-kumaris
+- murli
+- distilgpt2
+- ultra-lite
+- peft
+- lora
+library_name: peft
+base_model: distilgpt2
+---
+
+# 🕉️ Murli Assistant - DistilGPT-2 Ultra-Lite
+
+An **ultra-lightweight** spiritual AI assistant trained on Brahma Kumaris murli content. Perfect for free Colab and low-resource environments!
+
+## 🎯 Why This Model?
+
+- **82M parameters** (30x smaller than Phi-2)
+- **RAM: ~1-2 GB** (fits easily in free Colab)
+- **Fast inference**: 0.5-1 second per response
+- **No quantization needed**: Runs in full precision
+- **Perfect for free tier**: No crashes, no OOM errors
+
+## Model Details
+
+- **Base Model**: DistilGPT-2 (82M parameters)
+- **Fine-tuning**: LoRA (Low-Rank Adaptation)
+- **Training Data**: 150 authentic murlis
+- **Training Examples**: 153+
+- **Max Length**: 256 tokens
+- **LoRA Rank**: 4
+
+## Usage
+
+### Quick Start (Colab)
+
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+from peft import PeftModel
+
+# Load base model
+tokenizer = AutoTokenizer.from_pretrained("distilgpt2")
+base_model = AutoModelForCausalLM.from_pretrained("distilgpt2")
+
+# Load LoRA adapter
+model = PeftModel.from_pretrained(base_model, "eswarankrishnamurthy/murli-assistant-distilgpt2-lite")
+
+# Chat function
+def chat(message):
+    prompt = f"Q: {message}\nA:"
+    inputs = tokenizer(prompt, return_tensors="pt")
+    outputs = model.generate(**inputs, max_new_tokens=150)
+    return tokenizer.decode(outputs[0], skip_special_tokens=True)
+
+# Try it
+response = chat("Om Shanti")
+print(response)
+```
+
+### Use in Production
+
+See the full Colab notebook: `murli-distilgpt2-colab.ipynb`
+
+## Comparison with Other Models
+
+| Model | Parameters | RAM | Inference | Colab Free |
+|-------|------------|-----|-----------|------------|
+| **DistilGPT-2 (This)** | 82M | ~1-2 GB | 0.5-1s | ✅ Perfect |
+| Phi-2 | 2.7B | ~10 GB | 1-3s | ❌ Crashes |
+| Phi-2 (4-bit) | 2.7B | ~3-4 GB | 1-3s | ⚠️ Tight fit |
+
+## Advantages
+
+✅ **Ultra-Lightweight**: 30x smaller than Phi-2
+✅ **Low RAM**: Only 1-2 GB needed
+✅ **Fast Training**: 5-10 minutes
+✅ **Fast Inference**: Sub-second responses
+✅ **Free Colab**: Perfect fit, no crashes
+✅ **Easy Deployment**: Simple integration
+✅ **Good Quality**: Excellent for basic Q&A
+
+## Training Details
+
+[
+  "30x smaller than Phi-2",
+  "Fits in free Colab RAM easily",
+  "Fast training (5-10 min)",
+  "Fast inference",
+  "Good for basic Q&A"
+]
+
+## Example Responses
+
+**Q:** Om Shanti
+**A:** Om Shanti, sweet child! 🙏 I'm your Murli Helper. How can I guide you today?
+
+**Q:** What is soul consciousness?
+**A:** Soul consciousness is experiencing yourself as an eternal, pure soul with peace, love, and purity. Om Shanti 🙏
+
+**Q:** Who is Baba?
+**A:** Baba is the Supreme Soul, the Ocean of Knowledge who teaches Raja Yoga through Brahma. Om Shanti 🙏
+
+## Limitations
+
+- Shorter context (256 tokens vs Phi-2's 512)
+- Simpler responses compared to larger models
+- Best for focused Q&A, not long essays
+- Limited reasoning compared to billion-parameter models
+
+## License
+
+MIT License - Free to use and modify
+
+## Citation
+
+```bibtex
+@misc{murli-distilgpt2-lite,
+  author = {eswarankrishnamurthy},
+  title = {Murli Assistant - DistilGPT-2 Ultra-Lite},
+  year = {2025},
+  publisher = {HuggingFace},
+  url = {https://huggingface.co/eswarankrishnamurthy/murli-assistant-distilgpt2-lite}
+}
+```
+
+## Acknowledgments
+
+- Brahma Kumaris World Spiritual University for murli teachings
+- HuggingFace for model hosting
+- DistilGPT-2 team for the base model
+
+---
+
+**Om Shanti! 🙏**

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checkpoint-60/README.md

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+---
+base_model: distilgpt2
+library_name: peft
+pipeline_tag: text-generation
+tags:
+- base_model:adapter:distilgpt2
+- lora
+- transformers
+---
+
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+
+
+## Model Details
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+
+
+- **Developed by:** [More Information Needed]
+- **Funded by [optional]:** [More Information Needed]
+- **Shared by [optional]:** [More Information Needed]
+- **Model type:** [More Information Needed]
+- **Language(s) (NLP):** [More Information Needed]
+- **License:** [More Information Needed]
+- **Finetuned from model [optional]:** [More Information Needed]
+
+### Model Sources [optional]
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** [More Information Needed]
+- **Paper [optional]:** [More Information Needed]
+- **Demo [optional]:** [More Information Needed]
+
+## Uses
+
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+
+### Direct Use
+
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+
+[More Information Needed]
+
+### Downstream Use [optional]
+
+<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+
+[More Information Needed]
+
+### Out-of-Scope Use
+
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+
+[More Information Needed]
+
+## Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+
+[More Information Needed]
+
+### Recommendations
+
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+
+Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+
+## How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+[More Information Needed]
+
+## Training Details
+
+### Training Data
+
+<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+
+[More Information Needed]
+
+### Training Procedure
+
+<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+
+#### Preprocessing [optional]
+
+[More Information Needed]
+
+
+#### Training Hyperparameters
+
+- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+
+#### Speeds, Sizes, Times [optional]
+
+<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+
+[More Information Needed]
+
+## Evaluation
+
+<!-- This section describes the evaluation protocols and provides the results. -->
+
+### Testing Data, Factors & Metrics
+
+#### Testing Data
+
+<!-- This should link to a Dataset Card if possible. -->
+
+[More Information Needed]
+
+#### Factors
+
+<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+
+[More Information Needed]
+
+#### Metrics
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+<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+
+[More Information Needed]
+
+### Results
+
+[More Information Needed]
+
+#### Summary
+
+
+
+## Model Examination [optional]
+
+<!-- Relevant interpretability work for the model goes here -->
+
+[More Information Needed]
+
+## Environmental Impact
+
+<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+
+Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+
+- **Hardware Type:** [More Information Needed]
+- **Hours used:** [More Information Needed]
+- **Cloud Provider:** [More Information Needed]
+- **Compute Region:** [More Information Needed]
+- **Carbon Emitted:** [More Information Needed]
+
+## Technical Specifications [optional]
+
+### Model Architecture and Objective
+
+[More Information Needed]
+
+### Compute Infrastructure
+
+[More Information Needed]
+
+#### Hardware
+
+[More Information Needed]
+
+#### Software
+
+[More Information Needed]
+
+## Citation [optional]
+
+<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+
+**BibTeX:**
+
+[More Information Needed]
+
+**APA:**
+
+[More Information Needed]
+
+## Glossary [optional]
+
+<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+
+[More Information Needed]
+
+## More Information [optional]
+
+[More Information Needed]
+
+## Model Card Authors [optional]
+
+[More Information Needed]
+
+## Model Card Contact
+
+[More Information Needed]
+### Framework versions
+
+- PEFT 0.17.1

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+  "model_name": "distilgpt2",
+  "model_size": "82M parameters",
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+    "Fits in free Colab RAM easily",
+    "Fast training (5-10 min)",
+    "Fast inference",
+    "Good for basic Q&A"
+  ]
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