Esperanto
/

Protein-Llama-3-8B

@@ -1,199 +1,74 @@
 ---
 library_name: transformers
-tags: []
 ---
-# 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. -->
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
-- **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]

 ---
 library_name: transformers
+base_model: meta-llama/Meta-Llama-3-8B
 ---
 ## Model Details
+Protein-Llama-3-8B is a specialized version of the Llama-3-8B large language model, fine-tuned for the task of protein language modeling.
+This model has been continually pre-trained using LoRA technique on extensive datasets of protein sequences, enabling it to generate novel protein sequences based on natural language prompts.
+It supports both uncontrollable and controllable protein generation, allowing users to specify desired characteristics for the proteins.
+The model is designed to facilitate advancements in protein engineering, making it a valuable tool for drug development, chemical synthesis, and other biotechnological applications.
+### Model Description
+Generating novel protein sequences possessing desired properties, termed as protein engineering,  is crucial for industries like drug development and chemical synthesis. Traditional protein engineering techniques often involve introducing random mutations into the gene encoding the protein of interest. This is followed by expression and screening to identify variants with improved or novel functions, which are then reproduced. While effective, these approaches are labor-intensive and time-consuming, as they rely on iterating over known protein sequences. This limits their ability to generate diverse protein sequences with entirely new capabilities, as they are constrained by existing protein templates. Moreover, the need to analyze numerous protein variants can waste valuable experimental resources.
+However, leveraging a Large Language Model (LLM) that has learned the "protein language" significantly accelerates this process. An LLM can generate and evaluate protein sequences in a matter of seconds. The inherent randomness of LLM-generated sequences enhances diversity, enabling the creation of completely novel proteins with potentially unprecedented functions. This not only streamlines the discovery and development process but also expands the scope of possibilities in protein engineering.
+This model is based on the Llama-3-8B architecture and is capable of generating proteins based on user defined characteristics.
+## Usage
+To download and use the Protein LLaMA 3 model for inference, follow these steps:
+### Installation
+Ensure you have the `transformers` library installed. You can install it using pip:
+```bash
+pip install transformers
+### Uncontrollable Generation
+Uncontrollable generation can be handled via prompting the model with the phrase 'Seq=<'.
+```
+generator = pipeline('text-generation', model=merged_model, tokenizer=tokenizer)
+sequences = generator("Seq=<",temperature=0.2,
+    top_k=40,
+    top_p=0.9,
+    do_sample=True,
+    repetition_penalty=1.2,
+    max_new_tokens=30,
+    num_return_sequences=500)
+for sequence in sequences:
+    print(sequence['generated_text'])
+```
+### Controllable Generation
+Controllable generation can be done by prompting the model with '[Generate xxx protein] Seq=<'. Here, xxx can be any family from the 10 classes supported by this model.
+```
+generator = pipeline('text-generation', model=merged_model, tokenizer=tokenizer)
+sequences = generator("[Generate Oxidoreductase protein] Seq=<",temperature=0.2,
+    top_k=40,
+    top_p=0.9,
+    do_sample=True,
+    repetition_penalty=1.2,
+    max_new_tokens=30,
+    num_return_sequences=500)
+for sequence in sequences:
+    print(sequence['generated_text'])
+```