Update README.md

README.md

@@ -3,200 +3,115 @@ base_model: distilgpt2
 library_name: peft
 ---
 
-# Model Card for Model ID
+# Model Card for `gautam-raj/fine-tuned-distilgpt2`
 
-<!-- Provide a quick summary of what the model is/does. -->
+## Model Description
 
+This model is a fine-tuned version of the `distilgpt2` model, trained using the Alpaca dataset. It has been optimized for generating text based on instructions and responses, designed to assist in tasks where conversational text generation is required.
 
+## Model Architecture
 
-## Model Details
+The model is based on `distilgpt2`, a smaller, distilled version of GPT-2 (Generative Pretrained Transformer 2). DistilGPT2 maintains a balance between efficiency and performance, making it suitable for applications with resource constraints. The model has been fine-tuned using a custom dataset to improve its conversational abilities.
 
-### Model Description
+- **Base model**: `distilgpt2`
+- **Fine-tuned on**: Alpaca dataset
+- **Architecture type**: Causal language model (Autoregressive)
+- **Number of layers**: 6 layers
+- **Hidden size**: 768
+- **Attention heads**: 12
+- **Vocabulary size**: 50257
 
-<!-- Provide a longer summary of what this model is. -->
+## Intended Use
 
+This model can be used for various text generation tasks, such as:
+- Conversational AI
+- Dialogue systems
+- Text-based question answering
+- Instruction-based text generation
 
+**Examples of use cases**:
+- Chatbots
+- AI assistants
+- Story or content generation based on a given prompt
+- Educational tools for conversational learning
 
-- **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]
+## Limitations
 
-### Model Sources [optional]
+- **Bias**: Like many language models, this model may inherit biases present in the dataset it was trained on.
+- **Context length**: The model can process a maximum of 512 tokens in one forward pass. Longer inputs will need to be truncated.
+- **Specificity**: The model might not always generate highly accurate or context-specific answers, particularly in specialized domains outside its training data.
 
-<!-- Provide the basic links for the model. -->
+## Training Data
 
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+The model was fine-tuned on the Alpaca dataset, which is a collection of instruction-response pairs. This data is intended to enhance the model’s ability to follow instructions and respond in a conversational manner.
 
-## Uses
+### Alpaca Dataset
 
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+The Alpaca dataset consists of instruction-based examples and outputs, ideal for training conversational agents. It includes a diverse set of instructions across multiple domains and tasks.
 
-### Direct Use
+## How to Use
 
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+You can load this model and generate text using the following code:
 
-[More Information Needed]
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
 
-### Downstream Use [optional]
+# Load the fine-tuned model and tokenizer
+model_path = 'gautam-raj/fine-tuned-distilgpt2'  # Path to the model on Hugging Face
 
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+model = AutoModelForCausalLM.from_pretrained(model_path)
+tokenizer = AutoTokenizer.from_pretrained(model_path)
 
-[More Information Needed]
+# Input text
+input_text = "Give three tips for staying healthy."
 
-### Out-of-Scope Use
+# Tokenize the input text
+inputs = tokenizer(input_text, return_tensors="pt", padding=True, truncation=True)
 
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+# Generate the response from the model
+outputs = model.generate(
+    **inputs,  # Pass tokenized inputs to the model
+    max_length=100,  # Maximum length of the generated output
+    num_return_sequences=1,  # Number of sequences to generate
+    no_repeat_ngram_size=2,  # To avoid repetitive phrases
+    temperature=0.5,  # Control randomness in generation
+    top_p=0.9,  # Nucleus sampling
+    top_k=50,  # Top-k sampling
+    do_sample=True
+)
 
-[More Information Needed]
+# Decode the generated output
+generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
 
-## 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]
+print(generated_text)
+```
 
 ## 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. -->
+This model has not yet been evaluated in a formal benchmark, but it performs reasonably well on conversational and instructional tasks based on its fine-tuning with the Alpaca dataset.
 
-[More Information Needed]
+## License
 
-### Results
+Specify the license for the model. If you are using a license like the MIT License, you can indicate that here. Example:
 
-[More Information Needed]
+```
+The model is licensed under the MIT License.
+```
 
-#### Summary
+## Citation
 
+If you are publishing the model and want to cite it, you can add a citation format here. For example:
 
+```
+@article{gautam2024fine,
+  title={Fine-tuned DistilGPT2 for Instruction-based Text Generation},
+  author={Gautam Raj},
+  year={2024},
+  journal={Hugging Face},
+  url={https://huggingface.co/gautam-raj/fine-tuned-distilgpt2}
+}
+```
 
-## 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 0.13.2