Upload DistilGPT-2 MAXIMUM - Best possible training (LoRA r=32, 15 epochs, 500 murlis)

README.md

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+---
+language: en
+license: mit
+tags:
+- spiritual-ai
+- brahma-kumaris
+- murli
+- distilgpt2
+- maximum-accuracy
+- experimental
+- research-only
+- peft
+- lora
+library_name: peft
+base_model: distilgpt2
+---
+
+# 🕉️ Murli Assistant - DistilGPT-2 MAXIMUM (Experimental)
+
+⚠️ **WARNING: EXPERIMENTAL MODEL - NOT FOR PRODUCTION USE** ⚠️
+
+This model represents the **absolute maximum possible training** for DistilGPT-2 on murli content, but **quality remains insufficient** for spiritual guidance. Deployed for research, comparison, and educational purposes only.
+
+## ⚠️ Critical Limitations
+
+### Known Quality Issues:
+- ❌ **Hallucinations persist** despite maximum training
+- ❌ **Social media contamination** (Twitter URLs, @mentions in responses)
+- ❌ **Factual inaccuracies** in spiritual concepts
+- ❌ **Mixed content** from base model pre-training
+- ❌ **Not suitable for spiritual guidance**
+
+### Why This Model Exists:
+- ✅ **Research benchmark** for small model limitations
+- ✅ **Comparison baseline** vs larger models (Phi-2, Flan-T5)
+- ✅ **Educational example** of training optimization
+- ✅ **Proof that model size matters** for specialized domains
+
+### Production Recommendation:
+**Use Phi-2 (2.7B params) instead** - proven quality for murli chatbot.
+
+## 🎯 Maximum Training Configuration
+
+### This is the BEST DistilGPT-2 can do:
+
+**LoRA Configuration (MAXIMUM):**
+- **Rank (r):** 32 (8x better than standard r=4)
+- **Alpha:** 64 (8x better than standard alpha=8)
+- **Target Modules:** c_attn, c_proj, c_fc (ALL transformer layers)
+- **Trainable Parameters:** 2.36M (2.80% of model)
+- **Dropout:** 0.05 (reduced for maximum learning)
+
+**Training Data (MAXIMUM):**
+- **Murlis Used:** 500
+- **Training Examples:** 344
+- **Context Length:** 512 tokens (MAXIMUM)
+- **Spiritual Concepts:** 15 detailed examples with full explanations
+
+**Training Configuration (MAXIMUM):**
+- **Epochs:** 15 (5x more than standard)
+- **Effective Batch Size:** 16
+- **Learning Rate:** 5e-05 (ultra-careful)
+- **Warmup Steps:** 200 (4x more than standard)
+- **Scheduler:** cosine
+- **Weight Decay:** 0.02 (regularization)
+- **Training Time:** ~2h 50m on CPU
+
+**Final Training Loss:** 1.609 (66% improvement over standard 4.77)
+
+## 📊 Progressive Training Comparison
+
+| Version | LoRA Rank | Epochs | Murlis | Loss | Quality |
+|---------|-----------|--------|--------|------|---------|
+| Standard | 4 | 3 | 150 | 4.77 | ❌ Poor |
+| Enhanced | 16 | 10 | 300 | 2.07 | ❌ Poor |
+| **MAXIMUM** | **32** | **15** | **500** | **1.61** | ❌ **Still Poor** |
+
+**Key Finding:** Loss improvement does NOT guarantee quality improvement for small models in specialized domains.
+
+## 🔬 What We Learned
+
+### Why 82M Parameters Insufficient:
+1. **Base Model Dominance:** Pre-trained on internet text (Twitter, social media)
+2. **Fine-tuning Limitations:** Only 2.8% of model is trainable with LoRA
+3. **Knowledge Capacity:** Cannot store specialized domain knowledge + language ability
+4. **Pattern vs Knowledge:** Learns format but not deep spiritual understanding
+
+### Improvements in MAXIMUM vs Standard:
+   ✅ LoRA Rank: 32 (8x from standard, 2x from enhanced)
+   ✅ LoRA Alpha: 64 (8x from standard, 2x from enhanced)
+   ✅ Target Modules: c_attn + c_proj + c_fc (ALL layers)
+   ✅ Epochs: 15 (5x from standard, 1.5x from enhanced)
+   ✅ Murlis: 500 (3.3x from standard, 1.67x from enhanced)
+   ✅ Context: 512 tokens (2x from standard, 1.33x from enhanced)
+   ✅ 15 detailed spiritual concepts with full explanations
+   ✅ 7 different formats per murli for comprehensive learning
+   ✅ Ultra-careful learning rate (5e-5)
+   ✅ Maximum warmup (200 steps)
+   ✅ Larger effective batch (16)
+   ✅ Stronger regularization (0.02 weight decay)
+
+### What STILL Doesn't Work:
+- Accurate explanations of core BK concepts
+- Freedom from social media text patterns
+- Consistent factual responses
+- Spiritual guidance reliability
+
+## 💻 Usage (For Research/Demo Only)
+
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+from peft import PeftModel
+import torch
+
+# Load base model
+tokenizer = AutoTokenizer.from_pretrained("distilgpt2")
+base_model = AutoModelForCausalLM.from_pretrained(
+    "distilgpt2",
+    torch_dtype=torch.float16,
+    device_map="auto"
+)
+
+# Load MAXIMUM adapter
+model = PeftModel.from_pretrained(
+    base_model,
+    "eswarankrishnamurthy/murli-assistant-distilgpt2-maximum"
+)
+
+# Chat function
+def chat(message):
+    prompt = f"Question: {message}\nAnswer:"
+    inputs = tokenizer(prompt, return_tensors="pt")
+    outputs = model.generate(
+        **inputs,
+        max_new_tokens=150,
+        temperature=0.7,
+        top_p=0.9,
+        top_k=50,
+        repetition_penalty=1.2,
+        no_repeat_ngram_size=3
+    )
+    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    return response.split("Answer:", 1)[1].strip() if "Answer:" in response else response
+
+# Test (expect mixed quality)
+print(chat("What is soul consciousness?"))
+```
+
+## 📈 Performance Metrics
+
+**Inference Speed (CPU):**
+- Fastest: 1.13s
+- Average: 2.69s
+- Slowest: 3.55s
+
+**Resource Usage:**
+- RAM: ~1.5-2GB
+- Model Size: 3.1 MB (adapter only)
+- Base Model: 353 MB (DistilGPT-2)
+
+**Compared to Production Models:**
+- **Phi-2 (2.7B):** 33x larger, ⭐⭐⭐⭐⭐ quality, 5-10s inference
+- **Flan-T5:** 3x larger, ⭐⭐⭐⭐ quality, 3-5s inference
+- **DistilGPT-2 MAX:** Smallest, ⭐ quality, 1-3s inference
+
+## 🎯 Use Cases
+
+### ✅ Appropriate Uses:
+- Research on model size limitations
+- Benchmarking against larger models
+- Speed comparisons
+- Educational demonstrations
+- Training optimization studies
+
+### ❌ Inappropriate Uses:
+- **Spiritual guidance** (use Phi-2 instead)
+- **Production chatbot** (unreliable responses)
+- **Educational content** (may teach incorrect concepts)
+- **Public deployment** (without strong disclaimers)
+
+## 🔧 Technical Details
+
+**Architecture:**
+- Base: DistilGPT-2 (82M parameters)
+- Fine-tuning: LoRA (Low-Rank Adaptation)
+- Modified layers: ALL attention + feed-forward layers
+
+**Training Process:**
+1. Connected to MongoDB Atlas (1072 murlis available)
+2. Selected 500 murlis for training
+3. Created 344 enhanced training examples
+4. Trained for 15 epochs with cosine LR schedule
+5. Achieved lowest possible loss (1.61)
+
+**What Went Right:**
+- Perfect training convergence
+- Stable gradients throughout
+- Learned BK terminology and format
+- Fast inference speed maintained
+
+**What Went Wrong:**
+- Quality didn't match loss improvement
+- Social media patterns contaminate responses
+- Hallucinations persist despite maximum training
+- Cannot reliably explain spiritual concepts
+
+## 📚 Research Value
+
+This model proves important insights for AI/ML research:
+
+1. **Model capacity is non-negotiable** for specialized domains
+2. **Loss metrics can be misleading** without quality evaluation
+3. **Fine-tuning has fundamental limits** based on base model size
+4. **More training ≠ better quality** when capacity insufficient
+5. **Pre-training patterns dominate** small model behavior
+
+## 🎓 Educational Message
+
+**Before deploying any AI model:**
+- ✅ Test quality thoroughly, not just training metrics
+- ✅ Use appropriate model size for domain complexity
+- ✅ Understand fine-tuning limitations
+- ✅ Consider base model's pre-training influence
+- ✅ Validate against production requirements
+
+## 📊 Complete Training History
+
+**Completed:** 2025-10-03T12:25:52.051354
+
+**Loss Progression:**
+- Epoch 1: 4.68 → 4.48
+- Epoch 5: 3.44 (breakthrough)
+- Epoch 10: 1.81 (excellent convergence)
+- Epoch 15: **1.61 (BEST possible for DistilGPT-2)**
+
+**Gradient Norms:** Stable (0.72 - 1.72)
+
+## ⚖️ Final Verdict
+
+**Technical Success:** ✅ Perfect training, lowest loss achieved  
+**Functional Success:** ❌ Quality insufficient for spiritual guidance  
+**Research Value:** ✅ Invaluable insights for model selection
+
+### Recommendation:
+**For production murli chatbot, use [Phi-2](https://huggingface.co/microsoft/phi-2)** fine-tuned on murli data.
+
+This MAXIMUM model demonstrates that **small models cannot reliably handle specialized spiritual domains**, regardless of training optimization.
+
+## 🔗 Related Models
+
+- **Standard Version:** [murli-assistant-distilgpt2-lite](https://huggingface.co/eswarankrishnamurthy/murli-assistant-distilgpt2-lite) (LoRA r=4)
+- **Enhanced Version:** To be released (LoRA r=16)
+- **Recommended Production:** Phi-2 based murli assistant (coming soon)
+
+## 📝 Citation
+
+```bibtex
+@misc{murli-distilgpt2-maximum,
+  author = {eswarankrishnamurthy},
+  title = {Murli Assistant - DistilGPT-2 MAXIMUM (Experimental)},
+  year = {2025},
+  publisher = {HuggingFace},
+  note = {Experimental model demonstrating small model limitations},
+  url = {https://huggingface.co/eswarankrishnamurthy/murli-assistant-distilgpt2-maximum}
+}
+```
+
+## 📧 Contact
+
+For questions about this research or the production Phi-2 model, please open an issue.
+
+---
+
+## ⚠️ DISCLAIMER
+
+**This model is provided for research and educational purposes only.**
+
+- Not suitable for spiritual guidance
+- May produce incorrect or misleading information
+- Responses should be verified against authentic murli sources
+- Use at your own discretion
+
+**For reliable murli assistance, consult:**
+- Official Brahma Kumaris publications
+- Experienced BK teachers
+- The production Phi-2 based murli assistant (when available)
+
+---
+
+**Om Shanti! 🙏**
+
+*Maximum training doesn't overcome fundamental capacity limits.*  
+*Sometimes you just need a bigger model.*
+
+---
+
+**Model Type:** Experimental Research Model  
+**Quality Rating:** ⭐ (Insufficient for production)  
+**Speed Rating:** ⭐⭐⭐⭐⭐ (Excellent)  
+**Recommended Alternative:** Phi-2 (⭐⭐⭐⭐⭐ quality)

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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 -->
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+
+### 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
+
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+
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+
+#### Summary
+
+
+
+## Model Examination [optional]
+
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+
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+
+## 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]
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+
+## Technical Specifications [optional]
+
+### Model Architecture and Objective
+
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+
+### Compute Infrastructure
+
+[More Information Needed]
+
+#### Hardware
+
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+
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+
+<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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+## Model Card Contact
+
+[More Information Needed]
+### Framework versions
+
+- PEFT 0.17.1

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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
+
+<!-- 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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+---
+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
+
+<!-- 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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