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StableCode LLM: Advancing Generative AI Coding

Exciting news for the coding community! StableCode, a revolutionary AI coding solution, has just been announced by Stability AI.

This innovation comes as a boon to developers seeking efficient and creative coding assistance. StableCode leverages the power of Generative AI to enhance the coding experience.

If you're interested in exploring the capabilities of StableCode, the official announcement has all the details you need.

For those ready to dive into action, there are already quantized models available:

If you're curious about how StableCode fares on the code generation leaderboard, you can find the results on the click here to read

Understanding the Web Integrity API Debate

Google's proposal for the Web Integrity API has generated a significant amount of discussion and controversy. This proposal aims to introduce a new API that would add functionality to web browsers, focusing on ensuring the integrity of the client environment.

However, the implementation of this API has raised concerns and debates among users and developers. Many proposals for new browser features are introduced every year, but they often only become meaningful when they reach production. The proposal can be found in the linked repository, along with a prototype standard.

One significant concern is that this API could potentially be evaded, as history has shown that measures introduced for security and monetization are often met with workarounds. Virtualizing and emulating hardware security measures could continue to challenge the effectiveness of such initiatives.

There is …
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UltraLM-13B on the Leaderboard

UltraLM-13B has now been tested on this open leaderboard. Click here to view the leaderboard. It's the 25th best 13B model on the leaderboard. If this is an accurate assessment, could its high AlpacaEval performance be a problem with UltraLM's dataset or an example of how bad AlpacaEval is and the concept of using LLMs to judge other LLMs? Edit: Quite bad on this leaderboard too. Here is the leaderboard.

Just have a look at the training dataset. If all of that was used during training it could be believed. That's 8 Gb of data! Here is the dataset.

UltraChat contains 1.5 million high-quality multi-turn dialogues and covers a wide range of topics and instructions.

This paper believes that the most …
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Suitable Open Source Recommendation Engine for Insurance Recommendations

When it comes to open source recommendation engines tailored for insurance recommendations, two popular choices are:

  • ActionML Engines: This open source project provides a collection of recommendation engines, including the Universal Recommender, which can be customized for insurance recommendations based on user behavior and other relevant data.
  • Cornac: Cornac is a flexible and scalable recommender system library in Python. It offers various recommendation algorithms that can be adapted to suit insurance recommendations by incorporating domain-specific features and data.

Both ActionML Engines and Cornac provide a solid foundation for building and customizing recommendation engines for insurance applications. You can explore their documentation, code repositories, and community support to determine which one aligns best with your requirements.

Tags: Insurance, Recommendation Engine

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Exciting News: Open Orca Dataset Released!

It's a moment of great excitement for the AI community as the highly anticipated Open Orca dataset has been released. This dataset has been the talk of the town ever since the research paper was published, and now it's finally here, thanks to the dedicated efforts of the team behind it.

The Open Orca dataset holds immense potential for advancing natural language processing and AI models. It promises to bring us closer to open-source models that can compete with the likes of GPT-4, which is a significant milestone in the field.

One of the key concerns in the community has been censorship and the need for uncensored variants. It's crucial for the dataset to strike the right balance between filtering out undesirable content and providing an open and uncensored training resource. The community eagerly awaits more …
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Exploring Outpainting: Enhancing Images with Stable Diffusion

Outpainting, a technique to expand the visual content of images beyond their original boundaries, has gained significant attention in the computer vision community. While this concept has been around for a while, recent advancements in AI models and inpainting techniques have brought about exciting developments in the field.

One such example is the application of Stable Diffusion, which allows us to zoom out images and fill the resulting blank areas with visually coherent content. This technique has been demonstrated using the Outpainting model by Graydient AI, which you can find here.

Additionally, ControlNet, a popular AI model, offers outpainting capabilities when properly configured.

Interestingly, you can achieve similar results even without specialized models. By manually resizing the canvas of an image and using a decent inpainting model at an adequate resolution, you …
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Tutorial: How to Use Langchain to Host FastChat-T5-3B-v1.0 on Runpod

Step 1: Install Required Packages

First, you need to install the necessary packages. Open your terminal or command prompt and run the following commands:

pip3 install langchain
pip3 install fschat

Step 2: Set Up the FastChat Server

To set up the FastChat server, you need to run three commands in separate terminal windows.

In the first terminal, run the following command to start the FastChat controller:

python3 -m fastchat.serve.controller --host

In the second terminal, run the following command to start the FastChat model worker:

python3 -m fastchat.serve.model_worker --model-names "gpt-3.5-turbo,text-davinci-003,text-embedding-ada-002" --model-path lmsys/fastchat-t5-3b-v1.0 --host

In the third terminal, run the following command to start the FastChat OpenAI API server:

python3 -m fastchat.serve.openai_api_server --host --port 8000

Step 3: Configure Langchain

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Building an AI-Powered Chatbot using lmsys/fastchat-t5-3b-v1.0 on Intel CPUs

Discover how you can harness the power of lmsys/fastchat-t5-3b-v1.0 language model and leverage Intel CPUs to build an advanced AI-powered chatbot. Let's dive in!

Python Code:

 # Installing the Intel® Extension for PyTorch* CPU version python -m pip install intel_extension_for_pytorch # Importing the required libraries import torch from transformers import T5Tokenizer, AutoModelForSeq2SeqLM import intel_extension_for_pytorch as ipex # Loading the T5 model and tokenizer tokenizer = T5Tokenizer.from_pretrained("lmsys/fastchat-t5-3b-v1.0") model = AutoModelForSeq2SeqLM.from_pretrained("lmsys/fastchat-t5-3b-v1.0", low_cpu_mem_usage=True) # Setting up the conversation prompt prompt = """\ ### Human: Write a Python script for Factorial of a number. ### Assistant:\ """ # Tokenizing the prompt inputs = tokenizer(prompt, return_tensors='pt') # Generating the response using the T5 model tokens = model.generate( **inputs, max_new_tokens=256, do_sample=True, temperature=1.0, top_p=1.0, ) # Printing the generated response print(tokenizer.decode(tokens[0], skip_special_tokens=True)) 

By utilizing the powerful lmsys/fastchat-t5-3b-v1.0 language …
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Exploring Chat Models: rwkv/raven 1.5B and fastchat-t5 3B

If you are looking for chat models to enhance your conversational AI applications, there are several options available. Two popular models worth exploring are rwkv/raven 1.5B and fastchat-t5 3B.

rwkv/raven 1.5B is a powerful model that can generate responses for conversations. You can find the model as ggml, which stands for "generalized generative model language." It offers an extensive corpus and has a context length of 4096, enabling it to handle long conversations effectively.

Another model to consider is fastchat-t5 3B. It's a chat model with a capacity of 3 billion tokens and is designed to hold engaging conversations. It utilizes the T5 architecture, which is known for its versatility in natural language processing tasks. The model has …
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Tutorial: Building a LlamaIndex for Efficient Document Searching

Welcome to this step-by-step tutorial that will guide you through the process of creating a powerful document search engine using LlamaIndex. Let's get started!

Step 1: Import the Required Modules

 from llama_index import VectorStoreIndex, SimpleDirectoryReader, download_loader # Download the PDFReader loader PDFReader = download_loader("PDFReader") # Create a SimpleDirectoryReader object loader = PDFReader() 

Step 2: Load and Index Your Documents

 # Load the PDF documents documents = loader.load_data(file=Path('amdpt.pdf')) # Create a VectorStoreIndex object index = VectorStoreIndex.from_documents(documents) 

Step 3: Set Up the Query Engine

 # Set the OpenAI API key os.environ["OPENAI_API_KEY"] = "" # Create a query engine object query_engine = index.as_query_engine() 

Step 4: Search and Retrieve Information

 # Query the index with your question question = "?" response = query_engine.query(question) # Print the response print(response) 

Congratulations! You have …
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