Update README.md

README.md

@@ -1,57 +1,202 @@
-library_name: transformers
+---
 license: mit
 language:
-  - en
+- en
 base_model: meta-llama/Llama-2-7b-hf
-
+library_name: transformers
 ---
 
-# Model Card for Clinical Studies Instruction-Finetuned LLaMA-7B
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).
 
 ## Model Details
 
 ### Model Description
 
-This model is a fine-tuned version of the LLaMA-2-7B model from Meta, optimized using 1.5 lakh open-source clinical studies data for instruction-based tasks. The fine-tuning process has tailored the model to better understand and generate responses in the domain of clinical studies, making it particularly useful for tasks involving medical and clinical research data.
+<!-- Provide a longer summary of what this model is. -->
+
+
 
 - **Developed by:** Shudhanshu Shekhar
-- **Model type:** Instruction-finetuned LLaMA-2-7B
-- **Language(s) (NLP):** English
-- **License:** MIT
-- **Finetuned from model:** Meta's LLaMA-2-7B
+- **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 model can be directly used for generating responses or insights from clinical studies data, facilitating tasks such as summarization, information retrieval, and instruction following in the medical and clinical research fields.
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+
+[More Information Needed]
+
+### Downstream Use [optional]
 
-### Downstream Use
+<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
 
-The model can be further fine-tuned or integrated into applications focused on clinical decision support, medical research analysis, or healthcare-related natural language processing tasks.
+[More Information Needed]
 
 ### Out-of-Scope Use
 
-This model is not suitable for real-time medical diagnosis, treatment recommendations, or any other critical medical decision-making processes without human oversight. Misuse in contexts requiring precise and validated medical information could lead to incorrect or harmful outcomes.
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+
+[More Information Needed]
 
 ## Bias, Risks, and Limitations
 
-While the model has been fine-tuned on clinical studies data, it may still exhibit biases present in the original data. Users should be cautious when interpreting outputs, particularly in sensitive or critical contexts such as healthcare. The model may also produce outdated or incorrect information if the underlying data is not current.
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+
+[More Information Needed]
 
 ### Recommendations
 
-Users should critically evaluate the model's outputs and consider the context in which it is being used. It is advisable to have human oversight when deploying the model in healthcare or clinical environments.
+<!-- 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
 
-```python
-from transformers import AutoModelForCausalLM, AutoTokenizer
+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]
 
-tokenizer = AutoTokenizer.from_pretrained("dextersud/sudbits_llama_pharma_clincal_model")
-model = AutoModelForCausalLM.from_pretrained("dextersud/sudbits_llama_pharma_clincal_model")
+## Model Card Contact
 
-input_text = "Summarize the following clinical study on..."
-inputs = tokenizer(input_text, return_tensors="pt")
-outputs = model.generate(**inputs)
-print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+[More Information Needed]