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README.md

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----
-library_name: transformers
-tags: []
----
-
-# Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
-
-
+---
+library_name: transformers
+tags:
+- vaccination
+- immunization
+- chatbot
+- healthcare
+license: apache-2.0
+language:
+- en
+---
+
+# Model Card for HelpMumHQ Vax-Llama-1
+
+The HelpMumHQ Vax-Llama-1 is an advanced language model designed to provide accurate and relevant information about vaccinations and immunizations. It is fine-tuned from the Llama3 model and built using the Hugging Face Transformers framework. This model has 8 billion parameters and is optimized for delivering precise responses to queries related to vaccination safety, schedules, and more.
 
 ## Model Details
 
 ### Model Description
 
-<!-- Provide a longer summary of what this model is. -->
-
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
+The HelpMumHQ Vax-Llama-1 model is a specialized chatbot model developed to enhance the dissemination of vaccination-related information. It has been fine-tuned from the Llama3 base model with 8 billion parameters, using a diverse dataset of vaccination queries and responses. This model aims to provide reliable information to users, helping them make informed decisions about vaccinations.
 
-- **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]
+- **Developed by:** HelpMumHQ
+- **Funded by:** HelpMumHQ
+- **Shared by:** HelpMumHQ
+- **Model type:** Transformer-based language model
+- **Language(s) (NLP):** English
+- **License:** Apache 2.0
+- **Finetuned from model:** Llama3
 
-### Model Sources [optional]
+### Model Sources
 
-<!-- Provide the basic links for the model. -->
-
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+- **Repository:** [HelpMumHQ/vax-llama-1](https://huggingface.co/HelpMumHQ/vax-llama-1)
+- **Demo:** [HelpMumHQ Vax-Llama-1 Demo](https://huggingface.co/HelpMumHQ/vax-llama-1-demo)
 
 ## 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]
+The model can be directly used to answer queries related to vaccinations and immunizations without any further fine-tuning. It is suitable for integration into chatbots and other automated response systems in healthcare settings.
 
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+### Downstream Use
 
-[More Information Needed]
+The model can be fine-tuned for specific tasks or integrated into larger ecosystems and applications that require accurate vaccination information dissemination.
 
 ### Out-of-Scope Use
 
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-[More Information Needed]
+The model is not intended for use in generating medical advice beyond vaccination information. It should not be used for diagnosing medical conditions or providing treatment recommendations.
 
 ## Bias, Risks, and Limitations
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
+The model is trained on a dataset of vaccination-related information, which may not cover all possible queries or scenarios. Users should be aware of potential biases in the data and limitations in the model's knowledge. It is essential to consult healthcare professionals for personalized medical advice.
 
 ### 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.
+Users should ensure that the model is used in contexts where it can provide valuable information while being aware of its limitations. For critical medical decisions, consultation with healthcare professionals is recommended.
 
 ## How to Get Started with the Model
 
-Use the code below to get started with the model.
+Use the following code to get started with the Vax-Llama-1 model:
 
-[More Information Needed]
+```python
+from transformers import AutoModel, AutoTokenizer
 
-## Training Details
+tokenizer = AutoTokenizer.from_pretrained("HelpMumHQ/vax-llama-1")
+model = AutoModel.from_pretrained("HelpMumHQ/vax-llama-1")
 
-### Training Data
+messages = [
+    {
+        "role": "user",
+        "content": "Are vaccines safe for pregnant women?"
+    }
+]
 
-<!-- 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. -->
+prompt = tokenizer.apply_chat_template(messages, tokenize=False, 
+                                       add_generation_prompt=True)
 
-[More Information Needed]
+inputs = tokenizer(prompt, return_tensors='pt', padding=True, 
+                   truncation=True).to("cuda")
 
-### Training Procedure
+outputs = model.generate(**inputs, max_length=150, 
+                         num_return_sequences=1)
 
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+text = tokenizer.decode(outputs[0], skip_special_tokens=True)
 
-#### Preprocessing [optional]
+print(text.split("assistant")[1])
+```
 
-[More Information Needed]
+## Training Details
 
+### Training Data
 
-#### Training Hyperparameters
+The training data consists of a diverse set of questions and answers related to vaccinations, collected from authoritative medical sources to ensure the reliability and accuracy of the information.
 
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+### Training Procedure
 
-#### Speeds, Sizes, Times [optional]
+The model was fine-tuned on the vaccination dataset using the following hyperparameters:
 
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+- **Training regime:** Mixed precision (fp16)
+- **Batch Size:** 32
+- **Learning Rate:** 2e-5
+- **Epochs:** 5
 
-[More Information Needed]
+#### Preprocessing
 
-## Evaluation
+The data was cleaned and tokenized to ensure high-quality input for the model training process.
+
+#### Speeds, Sizes, Times
+
+- **Training Time:** Approximately 72 hours
+- **Checkpoint Size:** 8GB
 
-<!-- This section describes the evaluation protocols and provides the results. -->
+## Evaluation
 
 ### Testing Data, Factors & Metrics
 
 #### Testing Data
 
-<!-- This should link to a Dataset Card if possible. -->
-
-[More Information Needed]
+The testing data was a separate subset of vaccination-related queries to evaluate the model's performance accurately.
 
 #### Factors
 
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-
-[More Information Needed]
+The evaluation considered various factors, including the accuracy and relevance of responses, latency, and token allowance.
 
 #### Metrics
 
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
+- **Accuracy:** 92%
+- **Response Relevance:** 90%
+- **Average Latency:** 200ms
+- **Max Tokens per Response:** 150
 
 ### Results
 
-[More Information Needed]
+The Vax-Llama-1 model performed well in delivering accurate and relevant responses to vaccination queries, with high user satisfaction and efficiency.
 
 #### Summary
 
+The model demonstrated robust performance across various evaluation metrics, making it a reliable tool for vaccination information dissemination.
 
+## Model Examination
 
-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
+The model underwent rigorous testing and evaluation to ensure it meets the desired performance standards for accuracy and relevance.
 
 ## 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 for training the model can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute).
 
-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:** NVIDIA A100 GPUs
+- **Hours used:** 72
+- **Cloud Provider:** Google Cloud Platform
+- **Compute Region:** us-central1
+- **Carbon Emitted:** Approximately 250 kg CO2
 
-- **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]
+## Technical Specifications
 
 ### Model Architecture and Objective
 
-[More Information Needed]
+The Vax-Llama-1 is a transformer-based language model built on the Llama3 architecture, designed to generate accurate responses to vaccination-related queries.
 
 ### Compute Infrastructure
 
-[More Information Needed]
-
 #### Hardware
 
-[More Information Needed]
+- **GPUs:** NVIDIA A100
 
 #### Software
 
-[More Information Needed]
+- **Framework:** Transformers (Hugging Face)
+- **Programming Language:** Python
 
-## 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. -->
+## Citation
 
 **BibTeX:**
 
-[More Information Needed]
-
-**APA:**
-
-[More Information Needed]
-
-## Glossary [optional]
+```bibtex
+@misc {helpmumhq_2024,
+    author       = { {HelpMumHQ} },
+    title        = { vax-llama-1 (Revision 033a456) },
+    year         = 2024,
+    url          = { https://huggingface.co/HelpMumHQ/vax-llama-1 },
+    doi          = { 10.57967/hf/2755 },
+    publisher    = { Hugging Face }
+}
+```
 
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+## Glossary
 
-[More Information Needed]
+- **Transformer:** A type of neural network architecture used for natural language processing tasks.
+- **Fine-Tuning:** The process of taking a pre-trained model and further training it on a specific task or dataset.
+- **Tokenization:** The process of converting text into a format that can be used by the model, typically involving splitting text into tokens.
 
-## More Information [optional]
+## More Information
 
-[More Information Needed]
+For more details and access to the model, visit [HelpMumHQ/vax-llama-1](https://huggingface.co/HelpMumHQ/vax-llama-1).
 
-## Model Card Authors [optional]
+## Model Card Authors
 
-[More Information Needed]
+HelpMumHQ Team
 
 ## Model Card Contact
 
-[More Information Needed]
+For questions or feedback, please contact [HelpMumHQ](mailto:[email protected]).