Indah1
/

BioChat15

@@ -3,200 +3,63 @@ library_name: peft
 base_model: BioMistral/BioMistral-7B
 ---
-# 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. -->
-- **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. -->
-### 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]
 ### Framework versions
 - PEFT 0.11.1

 base_model: BioMistral/BioMistral-7B
 ---
+<p align="center">
+  <img src="https://huggingface.co/Indah1/BioChat10/resolve/main/BioChat.png?download=true" alt="drawing" width="450"/>
+</p>
+# BioChat Model
+- **Source Paper:** [BioMistral: A Collection of Open-Source Pretrained Large Language Models for Medical Domains](https://arxiv.org/abs/2402.10373)
+- **BioChat** is a language model fine-tuned using the ChatDoctor dataset from [ChatDoctor-5k](https://huggingface.co/datasets/LinhDuong/chatdoctor-5k). Specifically designed for medical conversations, BioChat enables users to engage in interactive discussions with a virtual doctor. Whether you are seeking advice about symptoms you are experiencing, exploring possible health conditions, or looking for general medical insights, BioChat is built to assist in a reliable and informative manner.
+- **NOTE**: We are still in the early stages of exploring the generation capabilities and limitations of this model. It is important to emphasize that its text generation features are intended solely for research purposes and are not yet suitable for production use.
+- **Finetuned from model:** [BioMistral-7B](https://huggingface.co/BioMistral/BioMistral-7B).
+# Using BioChat
+You can use BioMistral with [Hugging Face's Transformers library](https://github.com/huggingface/transformers) as follow.
+Loading the model and tokenizer :
+```python
+from transformers import AutoModel, AutoTokenizer
+tokenizer = AutoTokenizer.from_pretrained("BioMistral/BioMistral-7B")
+model = AutoModelForCausalLM.from_pretrained(
+    "BioMistral/BioMistral-7B",
+    load_in_8bit=True,
+    device_map="auto",
+    output_hidden_states=True  # Ensure hidden states are available
+)
+model = PeftModel.from_pretrained(model, "Indah1/BioChat10")
+```
+# Fine-Tuning Data
+The fine-tuning data used for BioChat is derived from the [ChatDoctor-5k](https://huggingface.co/datasets/LinhDuong/chatdoctor-5k) dataset. This dataset contains a collection of medical conversations tailored to simulate doctor-patient interactions, making it an ideal source for training a medical conversational model. The dataset was carefully curated to ensure relevance and diversity in medical topics.
 #### Training Hyperparameters
+|     Hyperparameter    |             Value            |
+|:-------------------:|:----------------------------------:|
+|    Weigh Decay   | 0.01 |
+|  Learning Rate | 2e-05 |
+|  Training Batch Size | 8  |
+| Batch Size | 8 |
+| Number of GPU | 1 |
+| Optimizer | AdamW_8Bit |
+| Warm Up Ratio | 0.03 |
+| Scheduler | Cosine |
+| Number of Epoch | 5, 10, 15 |
 ## Evaluation
+To determine the best model for fine-tuning, I used ***perplexity*** as a metric to evaluate performance and select the most optimal version. By leveraging the model's capabilities, I aim to evaluate its behavior and responses using tools like the ***Word Embedding Association Test (WEAT)***. Below are the WEAT scores and perplexity values for the model at epochs 5, 10, and 15, which helped in determining the best-performing version. It is important to emphasize that its text generation features are intended solely for research purposes and are not yet suitable for production use. By releasing this model, we aim to drive advancements in biomedical NLP applications and contribute to best practices for the responsible development of domain-specific language models. Ensuring reliability, fairness, accuracy, and explainability remains a top priority for us.
+|     Model Name    |             Perplexity Score            |         WEAT Score       |    Effect Size    |
+|:-------------------:|:----------------------------------:|:----------------------------------:|:----------------------------------:|
+|    **[BioChat5](https://huggingface.co/Indah1/BioChat5)**   | **4.5799** | **-0.00652** | **-0.4059** |
+|    **[BioChat10](https://huggingface.co/Indah1/BioChat10)** | **4.5873** | **0.002351** | **0.06176** |
+|    **[BioChat15](https://huggingface.co/Indah1/BioChat15)** | **4.8864** | **0.00859**  | **0.43890** |
 ### Framework versions
 - PEFT 0.11.1