Update README.md

README.md

@@ -6,197 +6,111 @@ tags:
 
 # Model Card for Model ID
 
-<!-- Provide a quick summary of what the model is/does. -->
+This a Mistral 7b Quantized trained on Academic Short QA model . It is fine tuned using Qlora technique and it is trainde till around 500 step with loss around 0.450
 
 
 
-## Model Details
+## Requirements
 
-### Model Description
+```python
 
-<!-- Provide a longer summary of what this model is. -->
+!pip install gradio
+!pip install -U xformers --index-url https://download.pytorch.org/whl/cu121
+!pip install "unsloth[kaggle-new] @ git+https://github.com/unslothai/unsloth.git"
 
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
+import os
+os.environ["WANDB_DISABLED"] = "true"
 
-- **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
+### Gradio App
+```python
+import gradio as gr
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import re
 
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+model_id = "DisgustingOzil/Academic-ShortQA-Generator"
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(model_id)
 
-[More Information Needed]
+alpaca_prompt = """Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.
 
-### Downstream Use [optional]
+### Instruction:
+{}
 
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+### Input:
+{}
 
-[More Information Needed]
+### Response:
+{}"""
 
-### Out-of-Scope Use
+def partition_text(text, partition_size):
+    words = text.split()
+    total_words = len(words)
+    words_per_partition = total_words // partition_size
+    partitions = []
+    for i in range(0, total_words, words_per_partition):
+        partition = " ".join(words[i:i+words_per_partition])
+        if len(partition) > 100:  # Ensuring meaningful length for MCQ generation
+            partitions.append(partition)
+    return partitions
 
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+def generate_mcqs_for_partition(Instruction, partition, temperature, top_k):
+    inputs = tokenizer(alpaca_prompt.format(Instruction, partition, ""), return_tensors="pt")
+    outputs = model.generate(
+        **inputs,
+        max_length=512,
+        num_return_sequences=1,
+        temperature=temperature,
+        top_k=top_k
+    )
+    output_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    return output_text
 
-[More Information Needed]
+def generate_mcqs(Instruction, text, partition_count, temperature, top_k):
+    partitions = partition_text(text, partition_count)
+    mcqs_output = []
 
-## Bias, Risks, and Limitations
+    for part in partitions:
+        output_text = generate_mcqs_for_partition(Instruction, part, temperature, top_k)
+        pattern = r'<question>(.*?)</question>.*?<answer>(.*?)</answer>'
+        matches = re.findall(pattern, output_text, re.DOTALL)
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+        for match in matches:
+            question = match[0].strip()
+            correct_answer = match[1].strip()
+            mcqs_output.append(f"Question: {question}\nCorrect Answer: {correct_answer}\n")
 
-[More Information Needed]
+    return "\n".join(mcqs_output) if mcqs_output else "No MCQs could be generated from the input."
 
-### Recommendations
+iface = gr.Interface(
+    fn=generate_mcqs,
+    inputs=[
+        gr.Textbox(label="Instruction"),
+        gr.Textbox(lines=10, label="Input Biology Text"),
+        gr.Slider(minimum=1, maximum=10, step=1, label="Partition Count"),
+        gr.Slider(minimum=0.5, maximum=1.0, step=0.05 , label="Temperature"),
+        gr.Slider(minimum=1, maximum=50, step=1, label="Top K")
+    ],
+    outputs="text",
+    title="ShortQA Generator",
+    description="Enter a text about Biology to generate MCQs. Adjust the sliders to change the model's generation parameters."
+)
 
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+if __name__ == "__main__":
+    iface.launch(debug=True, share=True)
 
-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. -->
-
-### 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]
-
-### Results
-
-[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]
-
-## 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]