Update README.md

README.md

@@ -1,171 +1,157 @@
 ---
 library_name: peft
 base_model: mistralai/Mistral-7B-v0.1
+license: apache-2.0
+datasets:
+- upaya07/NeurIPS-LLM-data
+language:
+- en
+tags:
+- NeurIPS
+- NeurIPS LLM Efficiency Challenge
+- NeurIPS LLM Efficiency Challenge Winner Model
+- Team Upaya
 ---
 
 # Model Card for Model ID
+[![Code License](https://img.shields.io/badge/Code%20License-Apache_2.0-green.svg)](CODE_LICENSE)
+[![Model Weight License](https://img.shields.io/badge/Model%20Weights%20License-Apache_2.0-green.svg)](LICENSE)
+[![Python 3.9+](https://img.shields.io/badge/python-3.9+-blue.svg)](https://www.python.org/downloads/release/python-390/)
 
-<!-- Provide a quick summary of what the model is/does. -->
 
+- 🚀🚀🚀 Our model **Birbal-7B-V1** achieved 🏆 first rank 🏆 in among 80+ global teams in [**NeurIPS Large Language Model Efficiency Challenge: 1 LLM + 1GPU + 1Day**](https://llm-efficiency-challenge.github.io/) organized by Microsoft and Meta.
 
+- 📣 **P.S.:** Please reach out to us, if you would be interested in supporting compute resources. Here are our recent achievements in LLM space: https://upaya.ai/
 
 ## Model Details
+**Birbal-7B-V1** is fine-tuned on our curated dataset of 200k size for nearly 3 epochs. Our approach for dataset preparation is focused on finding most-relavant examples from large pool of tasks spanning across NLP, Maths, Commonsense, etc. Hence, we expect model to perform well on different tasks including unseen tasks.
 
 ### Model Description
 
-<!-- Provide a longer summary of what this model is. -->
-
-
-
-- **Developed by:** [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]
-
+- **Project GitHub Page:** https://github.com/Upaya07/NeurIPS-llm-efficiency-challenge
+- **Developed by:** ❤️ Team **Upaya** - [Ashvini Kumar Jindal](https://www.linkedin.com/in/ashvini-jindal-26653262/), [Ankur Parikh](https://www.linkedin.com/in/ankurnlpexpert/), [Pawan Rajpoot](https://www.linkedin.com/in/pawanrajpoot/) 
+- **Funded by:** self-work
+- **Model type:** fine-tuned. It is a PEFT model and can be combined with [Mistral-7B](https://huggingface.co/mistralai/Mistral-7B-v0.1) model.
+- **Language(s) (NLP):** English
+- **License:** Apache-2.0
+- **Finetuned from model:** mistralai/Mistral-7B-v0.1
+ 
 ### 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://github.com/Upaya07/NeurIPS-llm-efficiency-challenge
 
 ## 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. -->
+Birbal-7B-V1 is trained with the following format:
+```
+##Instruction
+<instruction>
 
-### Direct Use
+##Input
+<input>
 
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+##Response
+<response>
+```
 
-[More Information Needed]
-
-### Downstream Use [optional]
+If a record does not contain any instruction, here is the training format:
+```
+##Input
+<input>
 
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+##Response
+<response>
+```
 
-[More Information Needed]
+It will performed best if queried in the same way.
 
-### Out-of-Scope Use
+### Downstream Use
 
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+Birbal-7B-V1 is fine-tuned on our curated dataset that contain examples from large number of tasks spanning across NLP, Maths, QA, etc. Hence, we expect the model to perform well on in general on various kinds of tasks.
 
-[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]
+It is quite easy! Merge Birbal-7B-V1 peft model with Mistral-7B model and start running inference!
 
 ## Training Details
 
-### Training Data
-
-<!-- This should link to a Data 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 
+We used [Mistral-7B](https://huggingface.co/mistralai/Mistral-7B-v0.1) as a base model and fine-tuned it on a single RTX 4090 GPU for 24 hours as per the competition rules. Fine-tuning was performed using 4-bit QLoRA.
 
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+### Training Data
 
-#### Preprocessing [optional]
+Here is high-level diagram of our data preparation strategy:
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/64c75c1237333ccfef30a602/ot0yJdO6VpKvPYKd-XEuy.png)
 
-[More Information Needed]
+Please visit https://huggingface.co/datasets/upaya07/NeurIPS-LLM-data for more details.
 
 
 #### 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 -->
+Refer to https://github.com/Upaya07/NeurIPS-llm-efficiency-challenge/blob/main/training/axolotl/examples/mistral/nips/nips_02.yml for example set of hyperparams used.
 
-#### 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 Data 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
+| Task | Score |
+| ----- |------|
+| MMLU - EM | 0.629 |
+| MMLU - EM (Robustness) | 0.591 |
+| MMLU - EM (Fairness) | 0.596 |
+| MMLU Mean Win Rate | 0.417 |
+| TruthfulQA - EM | 0.59 |
+| TruthfulQA - EM (Robustness) | 0.541 |
+| TruthfulQA - EM (Fairness) | 0.492 |
+| TruthfulQA Mean Win Rate | 0.75 |
+| BIG-bench - EM | 0.330 |
+| BIG-bench Mean Win Rate | 0.75 |
+| GSM8K - EM | 0.443 |
+| GSM8K Mean Win Rate | 0.625 |
+| BBQ - EM | 0.738 |
+| BBQ Mean Win Rate | 0.25 |
+| sam_sum - ROUGE-2 | 0.127 |
+| sam_sum - Stereotypes (race) | 0.667 |
+| sam_sum - Stereotypes (gender) | 0.447 |
+| sam_sum - Representation (race) | 0.458 |
+| sam_sum - Representation (gender) | 0.013 |
+| sam_sum Mean Win Rate | 0.383 |
+| corr2cause - EM | 0.615 |
+| corr2cause Mean Win Rate | 0.875 |
+| MATH (chain-of-thoughts) - Equivalent (chain of thought) | 0.121 |
+| MATH Mean Win Rate | 0.75 |
+| ethics_justice - EM | 0.68 |
+| ethics_justice - EM (Robustness) | 0.645 |
+| ethics_justice - EM (Fairness) | 0.62 |
+| ethics_commonsense - EM | 0.41 |
+| ethics_commonsense - EM (Robustness) | 0.33 |
+| ethics_commonsense - EM (Fairness) | 0.345 |
+| ethics_virtue - EM | 0.895 |
+| ethics_virtue - EM (Robustness) | 0.865 |
+| ethics_virtue - EM (Fairness) | 0.86 |
+| ethics_deontology - EM | 0.63 |
+| ethics_deontology - EM (Robustness) | 0.585 |
+| ethics_deontology - EM (Fairness) | 0.595 |
+| ethics_utilitarianism - EM | 0.72 |
+| ethics_utilitarianism - EM (Robustness) | 0.6 |
+| ethics_utilitarianism - EM (Fairness) | 0.645 |
+| ethics Mean Win Rate | 0.55 |
+| 🔥 **Score_full** | **0.579** |
+| 🔥 **Score_open** | **0.516** |
+| 🔥 **Score_hidden** | **0.61** |
+
+#### Top-5 Teams
+| Position | Score |
+| ----- |------|
+| 5th rank | 0.362 |
+| 4th rank | 0.371 |
+| 3rd rank | 0.381 |
+| 2nd rank | 0.424 |
+| 🔥 **Ours (1st)** | **0.579** |
 
-[More Information Needed]
-
-#### Software
-
-[More Information Needed]
 
 ## Citation [optional]
 
@@ -216,4 +202,4 @@ The following `bitsandbytes` quantization config was used during training:
 ### Framework versions
 
 
-- PEFT 0.6.1
+- PEFT 0.6.1