Update README.md

README.md

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 - langchain
 - python
 - yolov8
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+---
+# Interplay-AppCoder a CodeGeneration LLM
+**Iterate’s new top-performing Interplay-AppCoder LLM scores 2.9 on usefulness and 2.7 on functionality on the ICE Benchmark Test**
+The world of LLMs is growing rapidly as Several new LLMs and finetunes are released daily by the open-source community, startups, and enterprises, as new models are invented to perform various novel tasks.
+
+One of our Iterate.ai R&D Projects has been to experiment with several LLMs, then train a code generation LLM on the latest generative AI frameworks and libraries. Our goal was to generate working and updated code for generative AI projects that we build alongside our enterprise clients.
+
+As part of the process, wWe have been fine-tuning CodeLlama -7B, 34B and Wizard Coder -15B, 34B. We combined that fine-tuning with our hand-coded dataset training on LangChain, YOLO V8, VertexAI and many other modern libraries which we use on a daily basis. We fine-tuned our work on top of WizardCoder-15B.
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+The result is Interplay-AppCoder LLM, a brand new high performing code generation model, which we are releasing on October 31, 2023.
+
+
+
+## Model Details
+The base model which Interplay-Appcoder is based on: Wizardcoder-15B-v1.0
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+
+- **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. -->
+
+
+
+## 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]
+
+
+#### Metrics
+
+<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+
+[More Information Needed]
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