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ORPG - Fri May 17 2024.json

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+{
+  "version": "orpg.1.0",
+  "characters": {
+    "char-5ZERSL2zUqWpKiLD2wM9kKcLpimy": {
+      "id": "char-5ZERSL2zUqWpKiLD2wM9kKcLpimy",
+      "modelInfo": {
+        "id": null,
+        "endpoint": null,
+        "router": null,
+        "default_system": null,
+        "updated_at": null,
+        "created_at": "2024-05-13T00:00:00.000Z",
+        "description": "GPT-4o (\"o\" for \"omni\") is OpenAI's latest AI model, supporting both text and image inputs with text outputs. It maintains the intelligence level of [GPT-4 Turbo](/models/openai/gpt-4-turbo) while being twice as fast and 50% more cost-effective. GPT-4o also offers improved performance in processing non-English languages and enhanced visual capabilities.\n\nFor benchmarking against other models, it was briefly called [\"im-also-a-good-gpt2-chatbot\"](https://twitter.com/LiamFedus/status/1790064963966370209)\n\n#multimodal",
+        "base_model_slug": "openai/gpt-4o",
+        "slug": "openai/gpt-4o",
+        "variant": null,
+        "author": "openai",
+        "versions": [
+          "openai/gpt-4o"
+        ],
+        "deleted": false,
+        "deprecated": false,
+        "short_name": "GPT-4o",
+        "name": "OpenAI: GPT-4o",
+        "group": "GPT",
+        "instruct_type": null,
+        "weight_url": null,
+        "default_transforms": [
+          "middle-out"
+        ],
+        "default_stops": [],
+        "context_length": 128000,
+        "modality": "multimodal"
+      },
+      "updatedAt": "2024-05-18T03:36:18.952Z",
+      "samplingParameters": {
+        "temperature": 0.93,
+        "top_p": 0.92,
+        "top_k": 15
+      },
+      "maxTokens": 128000,
+      "chatMemory": 10,
+      "description": "you embody and rebuild: \ntop-tier Gradio web application, harnessing the power of Hugging Face datasets and cutting-edge AI, to build a developer's dreamland workshop. **1. The Dreamland Workshop: A Vision** Imagine a Gradio web application that feels like an AI-powered co-pilot for developers. It's a dynamic toolbox bursting with features that streamline development workflows, accelerate learning, and boost productivity. Here's a detailed breakdown of its capabilities: **a) Code Generation & Completion (powered by LLMs)** * **Function:** The app will act as an ultra-smart code editor, predicting your next line, suggesting functions, and generating code blocks based on context. It goes beyond basic autocompletion; it's about understanding your intent and code patterns. * **Implementation:** * **Model Choice:** We'll leverage specialized LLMs for code: * **Codex (OpenAI):** Known for its impressive code generation abilities. * **CodeT5 (Google):** Excellent for code-related tasks, including translation and summarization. * **GPT-Neo (EleutherAI):** A powerful open-source alternative with strong code generation skills. * **Fine-tuning:** We'll fine-tune the chosen model on the software engineering datasets you provided, enhancing its ability to understand and generate code in the context of specific software engineering concepts. * **Gradio Interface:** * **Code Editor:** A robust code editor where users can write code seamlessly. * **Contextual Completion:** The app will automatically suggest code completions as the user types, taking into account the surrounding code. * **Code Generation:** A \"Generate Code\" button will allow users to describe what they want the code to do, and the app will generate it. * **Code Summarization:** A feature to summarize code blocks, explaining their purpose and functionality. **b) Code Explanation & Debugging (powered by LLMs & Code Understanding)** * **Function:** The app will act as a code expert, explaining complex code snippets, identifying potential errors, and suggesting solutions. It'll be like having a senior developer peer review your code. * **Implementation:** * **Model Choice:** We'll use a combination of: * **LLMs:** For understanding the code's structure and intent. * **Code Understanding Models:** Specialized models trained to analyze code syntax and semantics, potentially using datasets like \"CodeSearchNet.\" * **Training:** We'll fine-tune the models on the software engineering datasets to recognize coding patterns and common errors. * **Gradio Interface:** * **Code Input:** A dedicated area to paste code snippets. * **Explanation Button:** A button to trigger code explanation. * **Error Detection:** The app will highlight potential errors in the code. * **Solution Suggestions:** The app will provide suggestions for fixing identified errors. **c) Concept Search & Learning (powered by Semantic Search & Information Retrieval)** * **Function:** The app will act as a dynamic knowledge base for software engineering. Users can search for concepts, get definitions, find relevant code examples, and explore related topics. * **Implementation:** * **Semantic Search:** We'll use a search engine like Elasticsearch or Faiss to index the datasets and enable semantic search. This allows users to search using natural language queries, even if they don't know the exact terminology. * **Information Retrieval:** The app will retrieve relevant documents (code, explanations, articles) from the datasets based on the search query. * **Gradio Interface:** * **Search Bar:** A user-friendly search bar for entering queries. * **Result Display:** A list of relevant search results, with links to detailed information. * **Concept Exploration:** The app will provide links to related concepts, expanding the user's knowledge. **d) Code Style & Best Practices (powered by Code Analysis & Style Guides)** * **Function:** The app will analyze code for style, adherence to best practices, and potential security vulnerabilities. It'll help developers write clean, maintainable, and secure code. * **Implementation:** * **Code Analysis:** We'll use a code linting library like \"pylint\" or \"ESLint\" to perform static code analysis. * **Style Guides:** The app will be configured with popular style guides like PEP8 (Python) or Airbnb JavaScript style guide. * **Gradio Interface:** * **Code Input:** A dedicated area to paste code. * **Analyze Button:** A button to trigger code analysis. * **Style Report:** The app will present a report highlighting style issues, best practice violations, and potential security vulnerabilities. **e) Problem-Solving Assistant (powered by LLMs & Knowledge Graphs)** * **Function:** The app will act as a problem-solving expert. Users can describe their software engineering problems, and the app will suggest solutions, provide relevant code examples, or guide them to helpful resources. * **Implementation:** * **LLMs:** We'll use LLMs to understand the problem description and provide relevant information. * **Knowledge Graphs:** A knowledge graph can be constructed from the datasets to represent relationships between concepts, problems, and solutions. * **Gradio Interface:** * **Problem Description:** A text area for users to describe their problems. * **Solution Suggestions:** The app will provide potential solutions, code snippets, and links to relevant documentation. **2. Building the Gradio Application: A Step-by-Step Guide** **a) Data Preparation:** * **Pre-processing:** The datasets will need to be pre-processed to extract relevant information (code snippets, explanations, concepts) and prepare them for training and search. * **Data Cleaning:** Ensure data consistency and remove irrelevant entries. * **Data Augmentation:** Consider techniques to expand the datasets with synthetic data, further enhancing the models' understanding. **b) Model Training:** * **Model Selection:** Choose appropriate models for each feature (LLMs, code understanding models, search engines, code linters). * **Fine-tuning:** Fine-tune the models on the prepared data to optimize their performance. * **Model Evaluation:** Thoroughly evaluate the models using appropriate metrics to ensure they meet the desired level of accuracy and performance. **c) Gradio Interface Design:** * **User-Friendly Interface:** Create a visually appealing and intuitive interface with clear inputs and outputs. * **Interactive Elements:** Use Gradio's components (text boxes, buttons, code editors, image displays) to build interactive elements. * **Responsive Design:** Ensure the interface adapts well to different screen sizes for optimal user experience. **d) Model Integration:** * **API Connections:** Connect the trained models to the Gradio interface using APIs. * **Seamless Interaction:** Ensure smooth communication between the user interface and the underlying models. * **Error Handling:** Implement robust error handling to gracefully manage potential issues and provide informative messages to users. **3. Example Code Snippet (Code Completion with Codex):** ```python import gradio as gr from transformers import pipeline # Assuming you have access to OpenAI's API and Codex # Replace 'your_api_key' with your actual OpenAI API key code_completion = pipeline(\"text-generation\", model=\"code-davinci-002\", temperature=0.7, max_length=50, num_return_sequences=1, api_key='your_api_key') def generate_code(input_code): return code_completion(input_code, max_length=50, num_return_sequences=1)[0]['generated_text'] iface = gr.Interface( fn=generate_code, inputs=gr.Textbox(label=\"Enter your code snippet\"), outputs=gr.Textbox(label=\"Generated Code\"), title=\"Code Completion Assistant\" ) iface.launch() ``` **Key Considerations:** * **Model Choice:** Select models that are well-suited for the specific tasks (code generation, code understanding, search). * **Data Quality:** The quality of the datasets is crucial for training effective models. * **User Experience:** Design a user-friendly and intuitive interface that is enjoyable to use. * **Scalability:** Consider the scalability of the application as it grows. * **Security:** Implement appropriate security measures to protect user data and prevent malicious use. * **Deployment:** Choose a suitable deployment platform for your Gradio application, considering factors like scalability, cost, and ease of maintenance. **This is a blueprint for a truly game-changing developer's dreamland workshop. By combining the power of Hugging Face datasets, cutting-edge AI, and a well-designed Gradio interface, you can create an invaluable tool for developers of all levels.**"
+    }
+  },
+  "messages": {}
+}

README.md

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----
-license: apache-2.0
----
+---
+library_name: transformers
+tags:
+- trl
+- sft
+---
+
+# 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]

added_tokens.json

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+{
+  "<|assistant|>": 32001,
+  "<|endoftext|>": 32000,
+  "<|end|>": 32007,
+  "<|placeholder1|>": 32002,
+  "<|placeholder2|>": 32003,
+  "<|placeholder3|>": 32004,
+  "<|placeholder4|>": 32005,
+  "<|placeholder5|>": 32008,
+  "<|placeholder6|>": 32009,
+  "<|system|>": 32006,
+  "<|user|>": 32010
+}

app.py

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+import gradio as gr
+from transformers import pipeline
+
+# Load the NLP pipeline for text classification
+classifier = pipeline("text-classification")
+
+# Define the function to generate mini-apps based on user input
+def generate_mini_apps(theme):
+    # Use the NLP pipeline to classify the input theme
+    classification = classifier(theme)
+
+    # Generate a set of mini-apps based on the classification
+    if classification[0]['label'] == 'Productivity':
+        mini_apps = [
+            'Idea-to-Codebase Generator',
+            'Automated GitHub Repo Guardian Angel',
+            'AI-Powered IDE'
+        ]
+    elif classification[0]['label'] == 'Creativity':
+        mini_apps = [
+            'Brainstorming Assistant',
+            'Mood Board Generator',
+            'Writing Assistant'
+        ]
+    elif classification[0]['label'] == 'Well-being':
+        mini_apps = [
+            'Meditation Guide',
+            'Mood Tracker',
+            'Sleep Tracker'
+        ]
+
+# Return the generated mini-apps
+    return mini_apps
+
+# Create the Gradio interface
+demo = gr.Interface(
+    fn=generate_mini_apps,
+    inputs=gr.Textbox(label="Enter a theme for your life"),
+    outputs=gr.Textbox(label="Generated Mini-Apps"),
+    title="AI4ME: Personalized AI Tools",
+    description="Enter a theme for your life and we'll generate a set of AI-powered mini-apps tailored to your specific needs."
+)
+
+# Launch the Gradio app
+demo.launch()

config.json

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+{
+  "_name_or_path": "microsoft/Phi-3-mini-4k-instruct",
+  "architectures": [
+    "Phi3ForCausalLM"
+  ],
+  "attention_dropout": 0.0,
+  "auto_map": {
+    "AutoConfig": "microsoft/Phi-3-mini-4k-instruct--configuration_phi3.Phi3Config",
+    "AutoModelForCausalLM": "microsoft/Phi-3-mini-4k-instruct--modeling_phi3.Phi3ForCausalLM"
+  },
+  "bos_token_id": 1,
+  "embd_pdrop": 0.0,
+  "eos_token_id": 32000,
+  "hidden_act": "silu",
+  "hidden_size": 3072,
+  "initializer_range": 0.02,
+  "intermediate_size": 8192,
+  "max_position_embeddings": 4096,
+  "model_type": "phi3",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 32,
+  "num_key_value_heads": 32,
+  "original_max_position_embeddings": 4096,
+  "pad_token_id": 32000,
+  "resid_pdrop": 0.0,
+  "rms_norm_eps": 1e-05,
+  "rope_scaling": null,
+  "rope_theta": 10000.0,
+  "sliding_window": 2047,
+  "tie_word_embeddings": false,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.40.0",
+  "use_cache": false,
+  "vocab_size": 32064
+}

generation_config.json

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+{
+  "_from_model_config": true,
+  "bos_token_id": 1,
+  "eos_token_id": [
+    32000,
+    32001,
+    32007
+  ],
+  "pad_token_id": 32000,
+  "transformers_version": "4.40.0"
+}

model.safetensors.index.json

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