Update README.md

README.md

@@ -8,126 +8,108 @@ language:
 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 Modeling with Transformers](https://arxiv.org/pdf/2005.14165)
+- **Demo:** [More Information Needed]
 
 ## Model Details
 
 ### Model Description
+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.
 
-<!-- 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]
+### 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:** `December 2024`
 
-## 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. -->
+## Usage
 
 ### Direct Use
-tokenizer = AutoTokenizer.from_pretrained("samkeet/GPT_124M", trust_remote_code=True)
-<!-- 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 -->
+You can use this model for text generation using the `transformers` library.
 
-[More Information Needed]
+```python
+from transformers import pipeline, AutoModelForCausalLM, AutoTokenizer
 
-### Out-of-Scope Use
+model_name = "samkeet/GPT_124M"
+tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
+model = AutoModelForCausalLM.from_pretrained(model_name, trust_remote_code=True)
 
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+pipe = pipeline("text-generation", model=model, tokenizer=tokenizer, trust_remote_code=True, device="cpu")
+result = pipe("Earth revolves around the", do_sample=True, max_length=40, temperature=0.9, top_p=0.5, top_k=50)
+print(result)
+```
 
-[More Information Needed]
+### Fine-tuning & Downstream Use
+This model can be fine-tuned for specific NLP applications like:
+- Dialogue generation
+- Text summarization
+- Creative writing
+- Code generation
 
-## Bias, Risks, and Limitations
+## Limitations & Risks
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+### 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.
 
-[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. -->
+### Testing Data & Metrics
 
 [More Information Needed]
 
@@ -135,72 +117,41 @@ Use the code below to get started with the model.
 
 [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]
+- **Hardware Used:** `8x NVIDIA RTX 4090 GPUs`
+- **Training Time:** `13 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:** FP16 mixed precision
 
-[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. -->
+## Citation
 
-**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]
+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/).