Update README.md

README.md

@@ -4,195 +4,154 @@ license: apache-2.0
 
 # Model Card for Model ID
 
-<!-- Provide a quick summary of what the model is/does. -->
-
-This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).
-
 ## Model Details
 
-### Model Description
-
-<!-- Provide a longer summary of what this model is. -->
-
-
+This project fine-tunes Microsoft's Phi-2 language model using parameter-efficient fine-tuning (LoRA) on the Nemotron-Personas-India dataset. The model is loaded using 4-bit NF4 quantization through BitsAndBytes to reduce memory consumption while maintaining training and inference capability on limited hardware.
 
-- **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 Description
 
-### Model Sources [optional]
 
-<!-- Provide the basic links for the model. -->
+- **Developed by:** Sachin Singh
+- **Model type:** Causal Language Model
+- **Base model:** Phi-2
+- **Language(s):** English
+- **Quantization:** 4-bit NF4 (BitsAndBytes)
+- **Fine-tuning method:** LoRA (PEFT)
+- **Dataset:** NVIDIA Nemotron-Personas-India (`en_IN` split)
 
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+### Model Sources
 
-## Uses
+- **Base Model:** microsoft/phi-2
+- **Dataset:** nvidia/Nemotron-Personas-India
 
-<!-- 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. -->
+This model is intended for:
 
-[More Information Needed]
+- Persona-conditioned text generation
+- Instruction-following experiments
+- Low-memory LLM deployment research
+- Quantization benchmarking
+- LoRA fine-tuning demonstrations
+- LLM performance analytics studies
 
-### Downstream Use [optional]
+### Downstream Use
 
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+The fine-tuned model can serve as a foundation for:
 
-[More Information Needed]
+- Persona-based conversational agents
+- Lightweight chatbot deployments
+- LLM optimization research
+- Quantization and efficiency studies
 
 ### Out-of-Scope Use
 
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+This model is not intended for:
 
-[More Information Needed]
+- Medical advice
+- Legal advice
+- Financial decision making
+- Safety-critical systems
+- High-risk automated decision systems
 
 ## Bias, Risks, and Limitations
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
+The model inherits limitations from:
 
-### Recommendations
+- The Phi-2 base model
+- The Nemotron-Personas-India dataset
+- Quantization-induced approximation errors
+- Limited fine-tuning duration
 
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+Generated responses may contain inaccuracies, hallucinations, biases, or incomplete information.
 
-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.
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
+import torch
 
-[More Information Needed]
+model_id = "microsoft/phi-2"
 
-## Training Details
+bnb_config = BitsAndBytesConfig(
+    load_in_4bit=True,
+    bnb_4bit_quant_type="nf4",
+    bnb_4bit_compute_dtype=torch.float16,
+    bnb_4bit_use_double_quant=True
+)
 
-### Training Data
+tokenizer = AutoTokenizer.from_pretrained(model_id)
 
-<!-- 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. -->
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    quantization_config=bnb_config,
+    device_map="auto"
+)
+```
 
-[More Information Needed]
+## Training Details
 
-### Training Procedure
+### Training Data
 
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+The model is fine-tuned using:
 
-#### Preprocessing [optional]
+- Dataset: `nvidia/Nemotron-Personas-India`
+- Split: `en_IN`
+- Sample Size: 5,000 records
 
-[More Information Needed]
+Persona records are transformed into instruction-response training examples before fine-tuning.
 
 
 #### 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. -->
+- Fine-tuning Method: LoRA
+- Quantization: 4-bit NF4
+- Epochs: 1
+- Compute Type: FP16
+- Double Quantization: Enabled
 
-[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
 
+The project evaluates the trade-offs between model efficiency and generation capability when applying 4-bit quantization and LoRA fine-tuning to Phi-2.
 
 
-## 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]
+- Architecture: Phi-2 Transformer
+- Objective: Causal Language Modeling
+- Adaptation Method: LoRA
+- Quantization Method: BitsAndBytes NF4 4-bit Quantization
 
 ### Compute Infrastructure
 
-[More Information Needed]
-
-#### Hardware
-
-[More Information Needed]
+GPU T4 x2
 
-#### 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]
+```bibtex
+@misc{phi2,
+  title={Phi-2: The surprising power of small language models},
+  author={Microsoft Research}
+}
+```
 
-## More Information [optional]
+### Dataset
 
-[More Information Needed]
+```bibtex
+@misc{nemotron_personas_india,
+  title={Nemotron Personas India Dataset},
+  author={NVIDIA}
+}
+```
 
-## Model Card Authors [optional]
+## Model Card Authors 
 
-[More Information Needed]
+Sachin Singh
 
-## Model Card Contact
+## Model in Notebook
 
-[More Information Needed]
+[[More Information Needed]](https://www.kaggle.com/code/shreyasraghav/4-bit-quantization-with-phi-2-with-more-analytics)