model card added

README.md

@@ -1,199 +1,174 @@
 ---
 library_name: transformers
-tags: []
+tags:
+- peft
+- lora
+- tinyllama
+- instruction-tuning
+- mathematics
+- machine-learning
+- education
+- domain-adaptation
+- two-stage-training
+- gsm8k
+license: apache-2.0
+datasets:
+- openai/gsm8k
+base_model:
+- TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T
+---
+library_name: transformers
+tags:
+  - peft
+  - lora
+  - tinyllama
+  - instruction-tuning
+  - mathematics
+  - machine-learning
+  - education
+  - domain-adaptation
+  - two-stage-training
+  - gsm8k
+license: apache-2.0
 ---
 
-# Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
-
-
-
-## 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]
-
-### Out-of-Scope Use
-
-<!-- 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]
-
-### 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]
-
-## Evaluation
-
-<!-- This section describes the evaluation protocols and provides the results. -->
+# 🧠 TinyLlama Math & ML Tutor (LoRA)
 
-### Testing Data, Factors & Metrics
+**A two-stage fine-tuned educational model specialized in Mathematics and Machine Learning**
 
-#### Testing Data
+Built on **TinyLlama-1.1B** — lightweight, fast, and effective for learning & teaching.
 
-<!-- This should link to a Dataset Card if possible. -->
+---
 
-[More Information Needed]
+## 🚀 Training Strategy (Two-Stage Fine-Tuning)
 
-#### Factors
+This model was developed using a **two-stage fine-tuning** approach for maximum mathematical depth and instruction-following ability.
 
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+### 🥇 Stage 1: Domain Adaptation (Non-Instruction)
+- **Dataset**: Full textbook — *Mathematics of Machine Learning: Master Linear Algebra, Calculus, and Probability for Machine Learning* by **Tivadar Danka**
+- **Method**: Continued pre-training (non-instruction / causal language modeling)
+- **Goal**: Inject deep mathematical knowledge, terminology, and formal reasoning into the base model.
 
-[More Information Needed]
+### 🥈 Stage 2: Instruction Tuning
+- **Dataset**: `openai/gsm8k` (main split)
+- **Method**: Instruction tuning using `[INST]` format
+- **Goal**: Teach the model to answer user questions clearly, step-by-step, and in an educational style.
 
-#### Metrics
+**Result**: Strong domain expertise + natural instruction following.
 
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+---
 
-[More Information Needed]
+## 🧾 Model Details
 
-### Results
+- **Base Model**: TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T
+- **Fine-tuning Method**: LoRA (PEFT)
+- **LoRA Rank (r)**: 8
+- **Domain**: Mathematics + Machine Learning
+- **Language**: English
+- **Developed by**: Nihal Jaiswal
 
-[More Information Needed]
+---
 
-#### Summary
+## 🧠 Prompt Format
 
+Use the following format for best results:
 
+```text
+[INST]Your question here[/INST]
+```
 
-## Model Examination [optional]
+---
 
-<!-- Relevant interpretability work for the model goes here -->
+## 💬 Example Usage
 
-[More Information Needed]
+**Input:**
+```text
+[INST]What is a vector space? Explain with example.[/INST]
+```
 
-## Environmental Impact
+**Output:**
+A vector space is a mathematical structure consisting of a set of vectors that can be added together and multiplied by scalars...
 
-<!-- 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).
+## ⚙️ How to Use (Copy-Paste)
 
-- **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]
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from peft import PeftModel
 
-## Technical Specifications [optional]
+# Load model
+repo_id = "Nihal108-bi/tinyllama-math-instruct-lora"
+base_model = "TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T"
 
-### Model Architecture and Objective
+tokenizer = AutoTokenizer.from_pretrained(repo_id)
+model = AutoModelForCausalLM.from_pretrained(base_model, device_map="auto", torch_dtype="auto")
+model = PeftModel.from_pretrained(model, repo_id)
 
-[More Information Needed]
+# Generate
+prompt = "[INST]Explain gradient descent with an example[/INST]"
+inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
 
-### Compute Infrastructure
+outputs = model.generate(
+    **inputs,
+    max_new_tokens=300,
+    temperature=0.7,
+    top_p=0.9,
+    do_sample=True,
+    repetition_penalty=1.1
+)
 
-[More Information Needed]
+answer = tokenizer.decode(outputs[0], skip_special_tokens=True).split("[/INST]")[-1].strip()
+print(answer)
+```
 
-#### Hardware
+---
 
-[More Information Needed]
+## 🎯 Capabilities
 
-#### Software
+- Strong explanations of Linear Algebra, Calculus, Probability & Statistics
+- Clear Machine Learning concepts (Gradient Descent, Bias-Variance, etc.)
+- Step-by-step mathematical reasoning
+- Educational / tutor-style responses
 
-[More Information Needed]
+## ⚠️ Limitations
 
-## Citation [optional]
+- Small 1.1B model → can occasionally hallucinate on very advanced topics
+- Best performance on math and ML-related questions
+- Not designed for general chit-chat or coding
 
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+---
 
-**BibTeX:**
+## 📊 Evaluation
 
-[More Information Needed]
+Qualitatively evaluated on math & ML reasoning tasks.
 
-**APA:**
+| Category              | Performance |
+|-----------------------|-----------|
+| Math Concepts         | Strong    |
+| ML Explanations       | Strong    |
+| Step-by-step Reasoning| Good      |
+| General Conversation  | Limited   |
 
-[More Information Needed]
+---
 
-## Glossary [optional]
+## 🙌 Credits & Acknowledgments
 
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+- **Fine-tuned by**: [Nihal Jaiswal](https://huggingface.co/Nihal108-bi)
+- **Base Model**: TinyLlama Team
+- **Domain Dataset**: Tivadar Danka – Mathematics of Machine Learning
+- **Instruction Dataset**: OpenAI GSM8K
 
-[More Information Needed]
+---
 
-## More Information [optional]
+## 📜 License
 
-[More Information Needed]
+Apache 2.0
 
-## Model Card Authors [optional]
+---
 
-[More Information Needed]
+**Made with ❤️ for students, educators, and ML enthusiasts.**
 
-## Model Card Contact
+Feel free to use, share, and build upon it!
+```
 
-[More Information Needed]