Update README.md

README.md

@@ -1,202 +1,68 @@
 ---
 library_name: peft
-base_model: DAMO-NLP-MT/polylm-1.7b
+base_model: meta-llama/Llama-2-7b-hf
 ---
 
-# Model Card for Model ID
+# [Reproducing] Stanford Alpaca: An Instruction-following LLaMA Model
+This is the repo for reproducing [Stanford Alpaca : An Instruction-following LLaMA Model](https://github.com/tatsu-lab/stanford_alpaca/blob/main/README.md). We finetune some of LlaMa2-based large language model using medical QA dataset. The repo contains:
 
-<!-- Provide a quick summary of what the model is/does. -->
+- The [5K data](#dataset) conversations between patients and physicians used for fine-tuning the model.
+- The code for [Preparation data](#data-preparation).
+- The code for [Fine Tuning the Model](#fine-tuning).
+- The link for [Testing the Model](#testing-the-model).
 
+## Dataset
+We using the 5k generated dataset by [Chat Doctor](https://github.com/Kent0n-Li/ChatDoctor). The dataset is a generated conversations between patients and physicians from ChatGPT GenMedGPT-5k and disease database. Dataset also currated and modified to Indonesian Language Based. 
 
+[`GenMedGPT-5k-id.json`](https://github.com/gilangcy/stanford-alpaca/blob/main/GenMedGPT-5k-id.json) contains 5K instruction-following data we used for fine-tuning the LlaMa model. This JSON file is a list of dictionaries, each dictionary contains the following fields:
 
-## Model Details
+- `instruction`: `str`, describes the task the model should perform. Each of the 52K instructions is unique.
+- `input`: `str`, optional context or input for the task. For example, when the instruction is "Summarize the following article", the input is the article. Around 40% of the examples have an input.
+- `output`: `str`, the answer to the instruction as generated by `text-davinci-003`.
 
-### Model Description
+If you're interested in fine-tuning with your own data, it's essential to adhere to the default prompt format that the model used during its pre-training phase. The prompt for LlaMa 2 is structured similarly to this:
 
-<!-- Provide a longer summary of what this model is. -->
+```
+<s>[INST] <<SYS>>
+{{ instruction }}
+<</SYS>>
 
+{{ input }} [/INST] {{ output }} </s>
+```
 
+Meanwhile, the prompt for PolyLM and InternLM (adapted to Indonesian) is structured similarly to this:
 
-- **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]
+```
+Di bawah ini adalah instruksi yang menjelaskan tugas, dipasangkan dengan masukan yang memberikan konteks lebih lanjut. Tulis tanggapan yang melengkapi permintaan dengan tepat.
 
-### Model Sources [optional]
+Instruksi:
+{instruction}
 
-<!-- Provide the basic links for the model. -->
+Masukan:
+{input}
 
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+Tanggapan:
+{output}
+```
 
-## Uses
+## Finetuning the Model
+We fine-tune our models based on the step from Stanford Alpaca. We choose to train some LLama-based model. The model that we finetune are PolyLM-1.7B, LlaMa-2-7B, InternLM-7B with the following hyperparameters:
 
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+| Hyperparameter | PolyLM-1.7B | LLaMA-7B | InternLM-7B |
+|----------------|------------ |----------|-------------|
+| Batch size     | 128         | 128      | 128         |
+| Learning rate  | 3e-4        | 3e-4     | 3e-4        |
+| Epochs         | 3           | 3        | 3           |
+| Max length     | 256         | 256      | 256         |
+| Weight decay   | 0           | 0        | 0           |
 
-### Direct Use
+To reproduce our fine-tuning runs for LLaMA, first install the requirements
 
-<!-- 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 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 
-
-<!-- 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 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
-
-[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]
+```
+pip install -r requirements.txt
+```
 
+The code for finetuning is available at [`fine-tuning.ipynb`](https://github.com/gilangcy/stanford-alpaca/blob/main/fine-tuning.ipynb) with four sections of pre-preocessing data, fine-tuning with LlaMa 2, fine-tuning with PolyLM, and fine-tuning with InternLM.
 
 ## Training procedure
 
@@ -217,3 +83,22 @@ The following `bitsandbytes` quantization config was used during training:
 
 
 - PEFT 0.6.0.dev0
+
+
+## Testing the Model 
+
+These are link for test the fine-tuned model :
+
+1. [PolyLM-1.7B](https://huggingface.co/spaces/dennyaw/polylm1.7b)
+2. [LlaMa-2-7B](https://huggingface.co/spaces/dennyaw/Llama-2-7b-finetuned)
+3. [InternLM-7B](https://huggingface.co/spaces/dennyaw/internlm-7b-finetuned)
+
+### Authors
+
+All interns below contributed equally and the order is determined by random draw.
+
+- [Denny Andriana Wahyu](https://www.linkedin.com/in/denny-aw/)
+- [Fadli Aulawi Al Ghiffari](https://www.linkedin.com/in/fadli-aulawi-al-ghiffari-9b4990148/)
+- [Gilang Catur Yudishtira](https://www.linkedin.com/in/gilangcy/)
+
+All advised by [Firqa Aqilla Noor Arasyi](https://www.linkedin.com/in/firqaana/)