Update README.md

README.md

@@ -1,199 +1,241 @@
 ---
 library_name: transformers
-tags: []
+license: mit
+datasets:
+- HuggingFaceFW/fineweb-edu
+language:
+- en
+base_model:
+- openai-community/gpt2
+pipeline_tag: text-generation
+tags:
+- GPT
+- GPT-3 Small
+- GPT-3 Medium
+- GPT-3 Large
+- GPT-3 XL
+- GPT-3 2.7B
+- GPT-3 6.7B
+- GPT-3 13B
+- GPT-3 175B
+- GPT-3
+- GPT-2
+- GPT-2 124M
+- transformers
+- mit
+- HuggingFace
+- fineweb-edu
+- Decoder-Only
 ---
+# Model Card for GPT-124M
 
-# Model Card for Model ID
+## Overview
 
-<!-- Provide a quick summary of what the model is/does. -->
+GPT-124M is a decoder-only transformer model based on OpenAI’s GPT-2 architecture. It is trained for text generation and other natural language processing (NLP) tasks. The model is designed for general-purpose language modeling, making it useful for applications such as text completion.
 
+- **Library:** 🤗 `transformers`
+- **License:** MIT
+- **Datasets:** `HuggingFaceFW/fineweb-edu`
+- **Language:** English
+- **Base Model:** `openai-community/gpt2`
+- **Pipeline Tag:** `text-generation`
+- **Developer:** Samkeet Sangai
+- **Funded By:** Samkeet Sangai
+- **Shared By:** Samkeet Sangai
+- **Model Type:** GPT Decoder-Only
 
+## Model Sources
+
+- **Paper:** [Language Models are Unsupervised Multitask Learners](https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf)
+- **Paper:** [Language Models are Few-Shot Learners](https://arxiv.org/pdf/2005.14165)
+- **Paper:** [Training Compute-Optimal Large Language Models](https://arxiv.org/pdf/2203.15556)
+- **Video:** [Andrej Karpathy-Let's reproduce GPT-2 (124M)](https://youtu.be/l8pRSuU81PU?si=KAo1y9dHYQAGJmj5)
+- **Demo:** [GPT 124M Demo](https://huggingface.co/spaces/samkeet/GPT_124M)
 
 ## Model Details
 
 ### Model Description
-
-<!-- Provide a longer summary of what this model is. -->
-
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
-
-- **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]
+GPT-124M is a lightweight generative language model fine-tuned on the `fineweb-edu` dataset. It can generate coherent and contextually relevant text but is not fine-tuned for instruction-following, safety, or factual accuracy.
+
+### Training Configuration
+- **Block Size:** `1024`
+- **Vocabulary Size:** `50304`
+- **Number of Layers:** `12`
+- **Number of Attention Heads:** `12`
+- **Embedding Size:** `768`
+- **Hardware:** `8x NVIDIA RTX 4090 GPUs`
+- **Training Duration:** `13 hours`
+- **Dataset:** `fineweb-edu` (10 billion tokens)
+- **Training Date:** `January 2025`
+- **Validation Dataset:** 100 million tokens of HuggingFaceFW/fineweb-edu
+  
+## Usage
+You can use this model for text generation using the `transformers` library.
+
+### Method 2: Using Pipeline
+```python
+# Import necessary modules from transformers
+from transformers import pipeline, AutoModelForCausalLM, AutoTokenizer
+
+# Load tokenizer and model
+model_name = "samkeet/GPT_124M"
+tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
+
+# Create text generation pipeline
+pipe = pipeline("text-generation", model=model_name, tokenizer=tokenizer, trust_remote_code=True, device="cpu")
+
+# Generate text
+result = pipe("Earth revolves around the", do_sample=True, max_length=40, temperature=0.9, top_p=0.5, top_k=50)
+print("Pipeline Output:", result)
+```
+
+### Method 1: Direct Generation
+```python
+# Import necessary libraries
+import torch
+
+# Function for direct tokenization and text generation
+def generate_text(input_text, device='cpu'):
+    tokens = tokenizer.encode(input_text, return_tensors='pt').to(device)
+    model.to(device)
+    
+    # Generate output
+    output = model.generate(
+        tokens, 
+        do_sample=True, 
+        max_length=40, 
+        temperature=0.9,
+        top_p=0.5,
+        top_k=50,
+    )
+    
+    # Decode generated text
+    generated_sentence = tokenizer.decode(output)
+    return generated_sentence
+
+# Generate text
+input_text = "Earth revolves around the"
+print("Direct Output:", generate_text(input_text))
+```
+
+### Fine-tuning & Downstream Use
+This model can be fine-tuned for specific NLP applications like:
+- Dialogue generation
+- Text summarization
+- Creative writing
+- Code generation
+
+## Limitations & Risks
 
 ### Out-of-Scope Use
+- The model is **not instruction-tuned** for safety, ethics, or factual accuracy.
+- It may produce **biased, misleading, or unsafe outputs**.
+- It should **not** be used for tasks requiring high reliability, such as medical, legal, or financial applications.
 
-<!-- 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]
+### Bias, Risks, and Limitations
+- The dataset may contain biases present in public web data.
+- The model does not filter or detect offensive content.
+- The model may **hallucinate** incorrect facts.
 
 ### 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]
+- Always **verify** generated content before use.
+- Implement **content filtering mechanisms** for deployment.
+- Use in supervised environments only.
 
 ## 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]
+### Training & Validation Loss
+Validation was conducted using `100 million tokens` from the `HuggingFaceFW/fineweb-edu` dataset. The training and validation loss graph indicates a stable convergence with minimal overfitting. The training loss achieved a minimum value of 2.88, while the validation loss stabilized at 2.97.
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/670142e648894dfbedacacaf/fAwiSHr4f9SmO9PYiCntY.png)
 
 ### Results
+The model was benchmarked against OpenAI’s GPT-2 Small and GPT-3 Small (both ~124M parameters). Remarkably, despite being trained on only `10 billion tokens`, compared to GPT-3 Small's `300 billion tokens`, GPT-124M was able to outperform both models in `HellaSwag` evaluation. This performance advantage is attributed to the specialized training data (educational content), which contrasts with GPT-3 Small’s broader multilingual and multi-domain training data.
 
-[More Information Needed]
-
-#### Summary
-
-
+According to Chinchilla’s scaling laws, an optimal token-to-parameter ratio suggests that a 124M-parameter model ideally requires `2.48 billion tokens` for training. The excess training tokens used in GPT-3 Small might have led to diminishing returns in performance.
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/670142e648894dfbedacacaf/Ne2MYAB2C0yHWFJLjCww3.png)
 
-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
+### Key Insights from Evaluation
+- **Efficient Training:** The model demonstrates impressive performance relative to its training token count, suggesting an efficient use of resources due to training using the Distributed Data Parallel (DDP) technique.
+- **Data-Specific Advantage:** Training exclusively on educational data may have given GPT-124M an edge in evaluation metrics like `HellaSwag`.
+- **Scaling Considerations:** GPT-3 Small, despite being trained on 300B tokens, does not exhibit proportionally better performance due to scaling limitations.
 
 ## 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]
+- **Hardware Used:** `8x NVIDIA RTX 4090 GPUs`
+- **Training Time:** `13 hours -> 104 GPU hours`
+- **Estimated Carbon Emissions:** `13.48 kg CO2 eq.`
+- **Equivalent to:**
+  - `54.5 km` driven by an average ICE car
+  - `6.75 kg` of coal burned
+  - `0.22` tree seedlings sequestering carbon for 10 years
 
-## Technical Specifications [optional]
+## Technical Specifications
 
-### Model Architecture and Objective
-
-[More Information Needed]
+### Model Architecture
+GPT-124M follows the architecture of OpenAI's GPT-2, which consists of:
+- **Transformer-based decoder model**
+- **Self-attention mechanism**
+- **Layer normalization & feed-forward networks**
 
 ### Compute Infrastructure
+- **Hardware:** 8x NVIDIA RTX 4090 GPUs
+- **Software:** PyTorch, Hugging Face Transformers
+- **Precision:** FP32
 
-[More Information Needed]
-
-#### Hardware
+Gotcha — here’s a **tight, concise section** you can drop in **as-is**.
+It keeps only the essentials: **data, setup, choices, Kaggle**, no fluff.
 
-[More Information Needed]
+## Instruction-Tuned Model
 
-#### Software
+### Training Data
 
-[More Information Needed]
+The instruction-tuned GPT-124M is fine-tuned on the **`tatsu-lab/alpaca`** dataset, containing high-quality instruction–response pairs across reasoning, explanation, summarization, and creative tasks. Samples are **length-filtered** to fit the 1024-token context window, counting instruction, input, response, and EOS tokens.
 
-## Citation [optional]
+### Prompt & Objective
 
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+Training follows an Alpaca-style format:
 
-**BibTeX:**
+```
+### Instruction:
+<instruction and optional input>
 
-[More Information Needed]
+### Response:
+<target output>
+```
 
-**APA:**
+Causal language modeling is used, with **loss applied only to response tokens** (prompt tokens masked with `-100`) and an explicit EOS token appended.
 
-[More Information Needed]
+### Training Setup
 
-## Glossary [optional]
+* **Platform:** Kaggle (GPU-backed notebooks)
+* **Framework:** PyTorch
+* **Precision:** FP32
+* **Optimizer:** AdamW with warmup + cosine decay
+* **Stability:** Gradient clipping and fixed-length batching
 
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+### Fine-Tuning Choices
 
-[More Information Needed]
+* Supports **full fine-tuning** and **LoRA-based parameter-efficient tuning**
+* LoRA can be merged into base weights for a standalone instruct model
+* Supervised fine-tuning (SFT) chosen for simplicity and reproducibility
+* No RLHF or safety-specific tuning applied
 
-## More Information [optional]
+### Outcome
 
-[More Information Needed]
+Instruction tuning improves instruction following, output structure, and task performance while preserving the base model’s generative capabilities. The model remains non-aligned and may hallucinate.
 
-## Model Card Authors [optional]
+## Citation
 
-[More Information Needed]
+If you use this model, please cite:
 
-## Model Card Contact
+```bibtex
+@article{gpt124m,
+  title={GPT-124M: A Compact Transformer Model for NLP},
+  author={Samkeet Sangai},
+  year={2024},
+  url={https://huggingface.co/samkeet/GPT_124M}
+}
+```
 
-[More Information Needed]
+## Contact
+For inquiries, contact [Samkeet Sangai](https://www.linkedin.com/in/samkeet-sangai/).