Falconsai
/

phi-2-chaos

@@ -1,201 +1,79 @@
----
 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
+tags: [chaos-engineering, IT-infrastructure, H.A.N.D.S, python, AI]
+# Model Card for phi-2-chaos-gen
+This model card describes the phi-2-chaos-gen, a fine-tuned version of the PHI-2 model, specialized in generating insights and strategies for chaos engineering in IT infrastructures.
 ## Model Details
 ### Model Description
+The phi-2-chaos-gen is a fine-tuned version of the PHI-2 model, developed to assist in chaos engineering for IT infrastructures. It utilizes a unique methodology called H.A.N.D.S (Hardware, Application, Network, Data, Security) to generate relevant strategies and insights. This model aims to provide comprehensive chaos engineering solutions, focusing on each aspect of IT infrastructure.
+- **Developed by:** Webnizam
+- **Model type:** Text generation
+- **Language(s) (NLP):** English
+- **License:** MIT License
+- **Finetuned from model:** Microsoft's PHI-2
+### Model Sources
+- **Repository:** [webnizam/phi-2-chaos-gen](https://huggingface.co/webnizam/phi-2-chaos-gen)
 ## Uses
 ### Direct Use
+The model can be directly used by IT professionals and organizations to generate strategies and insights for chaos engineering in their IT infrastructure, focusing on hardware, application, network, data, and security aspects.
 ## Bias, Risks, and Limitations
+The model, while powerful, may have limitations in understanding highly specialized or newly emerging IT concepts. Users should verify the model's recommendations with current IT standards and practices.
 ### Recommendations
+It's recommended to use this model as a starting point or a complement to existing chaos engineering practices, not as a sole source of truth.
 ## How to Get Started with the Model
+To use the phi-2-chaos-gen model, follow these steps:
+1. **Installation:**
+    Install the transformers library using pip:
+    ```bash
+    pip install transformers
+    ```
+2. **Loading the Model:**
+    Load the phi-2-chaos-gen model using the transformers package. Ensure you have an internet connection as the model will be downloaded the first time you run this code.
+    ```python
+    from transformers import AutoModelForCausalLM, AutoTokenizer
+    ```
+    tokenizer = AutoTokenizer.from_pretrained("webnizam/phi-2-chaos-gen")
+    model = AutoModelForCausalLM.from_pretrained("webnizam/phi-2-chaos-gen")
+3. **Using the Model:**
+    You can now use the model to generate text. For example:
+    ```python
+    input_text = "Scenario: Network failure in multi-cloud environment. Strategy:"
+    input_ids = tokenizer.encode(input_text, return_tensors='pt')
+    output = model.generate(input_ids, max_length=100, num_return_sequences=1)
+    print(tokenizer.decode(output[0], skip_special_tokens=True))
+    ```
 ### Results
+The model showed proficiency in generating relevant and practical strategies for different scenarios within the scope of H.A.N.D.S.
 ## Environmental Impact
+The training of this model was conducted with a focus on minimizing its carbon footprint. Specific details on the environmental impact are available upon request.
+## Technical Specifications
 ### Model Architecture and Objective
+The model follows the architecture of the PHI-2 model, fine-tuned for text generation in the context of chaos engineering.