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  base_model: internlm/internlm-7b
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  ---
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- # Model Card for Model ID
 
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- <!-- Provide a quick summary of what the model is/does. -->
 
 
 
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- ## Model Details
 
 
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- ### Model Description
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- <!-- Provide a longer summary of what this model is. -->
 
 
 
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- - **Developed by:** [More Information Needed]
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- - **Shared by [optional]:** [More Information Needed]
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- - **Model type:** [More Information Needed]
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- - **Language(s) (NLP):** [More Information Needed]
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- - **License:** [More Information Needed]
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- - **Finetuned from model [optional]:** [More Information Needed]
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- ### Model Sources [optional]
 
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- <!-- Provide the basic links for the model. -->
 
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- - **Repository:** [More Information Needed]
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- - **Paper [optional]:** [More Information Needed]
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- - **Demo [optional]:** [More Information Needed]
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- ## Uses
 
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- <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
 
 
 
 
 
 
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- ### Direct Use
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- <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
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- [More Information Needed]
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- ### Downstream Use [optional]
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- <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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- [More Information Needed]
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- ### Out-of-Scope Use
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- <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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- [More Information Needed]
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- ## Bias, Risks, and Limitations
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- <!-- This section is meant to convey both technical and sociotechnical limitations. -->
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- [More Information Needed]
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- ### Recommendations
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- <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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- Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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- ## How to Get Started with the Model
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- Use the code below to get started with the model.
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- [More Information Needed]
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- ## Training Details
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- ### Training Data
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- <!-- 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. -->
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- [More Information Needed]
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- ### Training Procedure
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- <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
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- #### Preprocessing [optional]
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- [More Information Needed]
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- #### Training Hyperparameters
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- - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
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- #### Speeds, Sizes, Times [optional]
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- <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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- [More Information Needed]
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- ## Evaluation
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- <!-- This section describes the evaluation protocols and provides the results. -->
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- ### Testing Data, Factors & Metrics
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- #### Testing Data
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- <!-- This should link to a Data Card if possible. -->
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- [More Information Needed]
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- #### Factors
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- <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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- [More Information Needed]
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- #### Metrics
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- <!-- These are the evaluation metrics being used, ideally with a description of why. -->
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- [More Information Needed]
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- ### Results
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- #### Summary
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- ## Model Examination [optional]
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- <!-- Relevant interpretability work for the model goes here -->
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- [More Information Needed]
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- ## Environmental Impact
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- <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
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- 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).
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- - **Hardware Type:** [More Information Needed]
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- - **Hours used:** [More Information Needed]
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- - **Cloud Provider:** [More Information Needed]
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- - **Compute Region:** [More Information Needed]
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- - **Carbon Emitted:** [More Information Needed]
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- ## Technical Specifications [optional]
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- ### Model Architecture and Objective
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- ### Compute Infrastructure
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- #### Hardware
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- #### Software
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- ## Citation [optional]
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- <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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- **BibTeX:**
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- [More Information Needed]
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- **APA:**
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- ## Glossary [optional]
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- <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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- [More Information Needed]
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- ## More Information [optional]
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- ## Model Card Authors [optional]
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- ## Model Card Contact
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  ## Training procedure
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  - PEFT 0.6.0.dev0
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  base_model: internlm/internlm-7b
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  ---
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+ # [Reproducing] Stanford Alpaca: An Instruction-following LLaMA Model
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+ 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:
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+ - The [5K data](#dataset) conversations between patients and physicians used for fine-tuning the model.
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+ - The code for [Preparation data](#data-preparation).
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+ - The code for [Fine Tuning the Model](#fine-tuning).
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+ - The link for [Testing the Model](#testing-the-model).
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+ ## Dataset
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+ 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.
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+ [`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:
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+ - `instruction`: `str`, describes the task the model should perform. Each of the 52K instructions is unique.
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+ - `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.
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+ - `output`: `str`, the answer to the instruction as generated by `text-davinci-003`.
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+ 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:
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+ ```
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+ <s>[INST] <<SYS>>
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+ {{ instruction }}
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+ <</SYS>>
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+ {{ input }} [/INST] {{ output }} </s>
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+ ```
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+ Meanwhile, the prompt for PolyLM and InternLM (adapted to Indonesian) is structured similarly to this:
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+ ```
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+ Di bawah ini adalah instruksi yang menjelaskan tugas, dipasangkan dengan masukan yang memberikan konteks lebih lanjut. Tulis tanggapan yang melengkapi permintaan dengan tepat.
 
 
 
 
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+ Instruksi:
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+ {instruction}
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+ Masukan:
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+ {input}
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+ Tanggapan:
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+ {output}
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+ ```
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+ ## Finetuning the Model
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+ 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:
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+ | Hyperparameter | PolyLM-1.7B | LLaMA-7B | InternLM-7B |
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+ |----------------|------------ |----------|-------------|
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+ | Batch size | 128 | 128 | 128 |
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+ | Learning rate | 3e-4 | 3e-4 | 3e-4 |
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+ | Epochs | 3 | 3 | 3 |
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+ | Max length | 256 | 256 | 256 |
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+ | Weight decay | 0 | 0 | 0 |
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+ To reproduce our fine-tuning runs for LLaMA, first install the requirements
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+ ```
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+ pip install -r requirements.txt
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+ ```
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ 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.
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  ## Training procedure
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  - PEFT 0.6.0.dev0
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+ ## Testing the Model
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+ These are link for test the fine-tuned model :
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+ 1. [PolyLM-1.7B](https://huggingface.co/spaces/dennyaw/polylm1.7b)
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+ 2. [LlaMa-2-7B](https://huggingface.co/spaces/dennyaw/Llama-2-7b-finetuned)
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+ 3. [InternLM-7B](https://huggingface.co/spaces/dennyaw/internlm-7b-finetuned)
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+ ### Authors
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+ All interns below contributed equally and the order is determined by random draw.
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+ - [Denny Andriana Wahyu](https://www.linkedin.com/in/denny-aw/)
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+ - [Fadli Aulawi Al Ghiffari](https://www.linkedin.com/in/fadli-aulawi-al-ghiffari-9b4990148/)
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+ - [Gilang Catur Yudishtira](https://www.linkedin.com/in/gilangcy/)
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+ All advised by [Firqa Aqilla Noor Arasyi](https://www.linkedin.com/in/firqaana/)