Update README.md

README.md

@@ -25,25 +25,33 @@ pipeline_tag: text-generation
 
 ### Model Description
 
-<!-- Provide a longer summary of what this model is. -->
+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]
+- **Developed by:** [Zubair Arshad Raoter CEO of SafeGuard.AI]
+- **Funded by [optionl]:** [Self-funded using 100% free and open-source resources
+]
+- **Shared by [Zubair Arshad Raoter
+]:** [More Information Needed]
+- **Model type:** [  (Text Generation)
+]
+- **Language(s) (NLP):** [  (English)
+]
+- **License:** [MIT]
+- **Finetuned from model [optional]:** [distilGPT2 by Hugging Face
+]
 
 ### Model Sources [optional]
 
 <!-- Provide the basic links for the model. -->
 
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+- **Repository:** [https://huggingface.co/Zubiiiiiii294/textbuddy]
+- **Paper [optional]:** [No formal paper yet. This is an independent experimental build.
+]
+- **Demo [optional]:** [Demo under development. Will be shared soon.
+
+]
 
 ## Uses
 
@@ -53,29 +61,75 @@ This is the model card of a 🤗 transformers model that has been pushed on the
 
 <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
 
-[More Information Needed]
+[For learning how LLMs work
+
+For generating text, stories, content ideas
+
+For experimenting with text prompts
+
+For research, education & testing small-scale NLP models
+
+]
 
 ### 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]
+[This model can be further fine-tuned for:
+
+Chatbots
+
+Educational writing assistants
+
+Text-based games
+
+Idea generation tools
+]
 
 ### Out-of-Scope Use
 
 <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
 
-[More Information Needed]
+[Not recommended for sensitive content
+
+Not for decision-making in medical, legal, or security domains
+
+Not for multi-language tasks (supports English only)
+]
 
 ## Bias, Risks, and Limitations
 
 <!-- This section is meant to convey both technical and sociotechnical limitations. -->
 
-[More Information Needed]
+[This model may reflect biases present in the Wikitext dataset
+
+Text output may be inaccurate or incomplete
+
+Doesn’t understand emotional or moral context
+
+Doesn’t support multilingual tasks
+
+Not suitable for commercial or mission-critical use (yet!)
+This model may reflect biases present in the Wikitext dataset
+
+Text output may be inaccurate or incomplete
+
+Doesn’t understand emotional or moral context
+
+Doesn’t support multilingual tasks
+
+Not suitable for commercial or mission-critical use (yet!)
+]
 
 ### Recommendations
+Use in controlled environments (research, testing, education)
+
+Don’t rely on its outputs as factual
+
+Carefully evaluate before using in any product or service
+
+Encourage transparency on limitations if shared publicly
 
-<!-- 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.
 
@@ -83,38 +137,76 @@ Users (both direct and downstream) should be made aware of the risks, biases and
 
 Use the code below to get started with the model.
 
-[More Information Needed]
+[from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_name = "Zubiiiiiii294/textbuddy"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name)
+
+prompt = "The future of AI in Pakistan is"
+inputs = tokenizer(prompt, return_tensors="pt")
+outputs = model.generate(**inputs, max_length=50)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+]
 
 ## 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. -->
+<!-- Dataset: WikiText-2 (raw, preprocessed)
+
+Source: Hugging Face Datasets
+
+Type: General English knowledge, Wikipedia-style text
 
-[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. -->
+Platform: Google Colab
+
+Tokenizer: distilgpt2 (EOS padded)
+
+Model: distilgpt2
+
+Run-time: ~30 minutes total
+
+Finetuning: Light-touch to demonstrate model building
+
+
 
 #### 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 -->
+- Precision: fp32
+
+Epochs: 1-2 (basic)
+
+GPU: Colab T4 GPU
+
+Batch size: Small (default)
+
 
 #### Speeds, Sizes, Times [optional]
 
 <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
 
-[More Information Needed]
+[Model size: 0.1B (117M parameters)
+
+File size: ~328MB
+
+Upload: Hugging Face model repo
+]
 
 ## Evaluation
 
-<!-- This section describes the evaluation protocols and provides the results. -->
+Testing Data
+
+Manual test prompts used during Colab testing
+
 
 ### Testing Data, Factors & Metrics
 
@@ -128,17 +220,24 @@ Use the code below to get started with the model.
 
 <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
 
-[More Information Needed]
+[Focused on simple generation
+
+No subpopulation or fairness breakdown]
 
 #### Metrics
 
 <!-- These are the evaluation metrics being used, ideally with a description of why. -->
 
-[More Information Needed]
-
 ### Results
 
-[More Information Needed]
+[Model is functional and responsive
+
+Good for basic prompt-based text generation
+
+Excellent for learning & showcasing LLMs from Pakistan
+
+
+]
 
 #### Summary
 
@@ -152,7 +251,15 @@ Use the code below to get started with the model.
 
 ## Environmental Impact
 
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+<!-- Hardware Type: Google Colab (shared cloud GPU)
+
+Hours used: ~2 hours
+
+Cloud Provider: Google
+
+Compute Region: Not specified
+
+Carbon Emitted: Minimal (low-resource training)
 
 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).
 
@@ -162,7 +269,16 @@ Carbon emissions can be estimated using the [Machine Learning Impact calculator]
 - **Compute Region:** [More Information Needed]
 - **Carbon Emitted:** [More Information Needed]
 
-## Technical Specifications [optional]
+## Technical Specifications [ Model Architecture and Objective
+
+Base model: distilgpt2
+
+Task: Text generation
+
+Objective: Predict next word/token in prompt-based context
+
+
+]
 
 ### Model Architecture and Objective
 
@@ -170,7 +286,10 @@ Carbon emissions can be estimated using the [Machine Learning Impact calculator]
 
 ### Compute Infrastructure
 
-[More Information Needed]
+[Cloud: Google Colab (T4 GPU)
+
+Software: Hugging Face Transformers, PyTorch, Datasets
+]
 
 #### Hardware
 
@@ -186,17 +305,30 @@ Carbon emissions can be estimated using the [Machine Learning Impact calculator]
 
 **BibTeX:**
 
-[More Information Needed]
+[@misc{zubair2025textbuddy,
+  title={TextBuddy: Pakistan’s 1st Open-Source Chat AI Model},
+  author={Zubair Arshad Raoter},
+  year={2025},
+  howpublished={\url{https://huggingface.co/Zubiiiiiii294/textbuddy}},
+  note={Self-trained via Colab}
+}
+]
 
 **APA:**
 
-[More Information Needed]
+[Zubair Arshad Raoter. (2025). TextBuddy: Pakistan’s 1st Open-Source Chat AI Model. Hugging Face. Retrieved from https://huggingface.co/Zubiiiiiii294/textbuddy
+]
 
 ## Glossary [optional]
 
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+[LLM – Large Language Model
+
+Tokenization – Breaking down sentences into model-readable pieces
+
+Finetuning – Training a model from an existing base on a custom dataset
 
-[More Information Needed]
+
+]
 
 ## More Information [optional]
 
@@ -204,8 +336,18 @@ Carbon emissions can be estimated using the [Machine Learning Impact calculator]
 
 ## Model Card Authors [optional]
 
-[More Information Needed]
+[Zubair Arshad Raoter – CEO, SafeGuard.AI
+
+Contributor to: Youth AI Movement in Pakistan
+
+Model inspired by ChatGPT & Grok logic
+
+]
 
 ## Model Card Contact
 
-[More Information Needed]
+[Email: [[email protected]]
+
+LinkedIn: www.linkedin.com/in/zubair-arshad-raoter-7b1210289
+
+Location: Karachi, Pakistan ]