The latest release of the Mistral 7B model, version 3, brings significant changes and enhancements. Unlike previous versions, this model has been directly released on Hugging Face, making it more accessible. While the performance is expected to be similar to the Mistral 7B model, there are some prominent updates.

The most notable change is that this is a completely uncensored model, with an extended vocabulary of a few hundred tokens. This expansion is related to its ability to natively support function calling, a new feature introduced in this version. Additionally, the tokenizer has been updated to accommodate these changes.

The model maintains the same context window of 32,000 tokens, and the Mistral Inference Python package has been updated to enable seamless inference on this model. This package provides a straightforward way to install, download, and run the model, as demonstrated in the provided Python notebook.

The model's uncensored nature allows it to generate responses on a wide range of topics, including potentially sensitive or controversial subjects. However, it includes appropriate disclaimers to ensure the information is not misused for any illegal activities.

The model's performance on various tasks, such as answering logic-based questions, showcases its impressive capabilities. It also demonstrates strong programming abilities, including the ability to generate HTML code and integrate external tools through its function calling feature.

Overall, the Mistral v3 model represents a significant step forward, offering users access to a powerful, uncensored language model with enhanced functionality. Its potential applications span a wide range of domains, and further exploration of its capabilities is highly encouraged.

To get started with the latest Mistral 7B v3 model, we'll walk through the installation and setup process step-by-step:

1. Install the Mistral Inference Package: The recommended way to run inference on the Mistral 7B v3 model is by using the Mistral Inference Python package. You can install it using pip:

   pip install mistral-inference
   

2. Download the Model: We'll define the path where we want to download the model and check if the directory exists. If not, we'll create it. Then, we'll use the snapshot_download function from the Hugging Face Hub to download the model files:

   model_path = 'path/to/mistral-7b-v3'
   if not os.path.exists(model_path):
       os.makedirs(model_path)
   
   model_repo_id = 'mosaicml/mistral-7b-v3'
   model = snapshot_download(model_repo_id, cache_dir=model_path)
   

   This will download the model files to the specified directory, which may take a few minutes depending on your internet connection speed.

3. Run Inference in the CLI: You can use the mral chat command to run the model in the command-line interface (CLI). This will allow you to provide a prompt and generate a response:

   mral chat path/to/mistral-7b-v3 --instruct --max_new_tokens 256
   

   When prompted, enter a message, and the model will generate a response.

4. Use the Model in Python: In your Python code, you can use the Mistral Inference package to load the model and generate responses programmatically:

   from mistral_inference import Transformer, ChatCompletionRequest
   
   model = Transformer.from_pretrained(model_path)
   tokenizer = Transformer.from_pretrained(model_path, subfolder='tokenizer')
   
   def generate_response(model, tokenizer, user_query):
       chat_request = ChatCompletionRequest(user_query)
       output_tokens = model.generate(chat_request.input_ids, max_new_tokens=1024, eos_token_id=tokenizer.eos_token_id)
       output_text = tokenizer.decode(output_tokens[0], skip_special_tokens=True)
       return output_text
   
   user_query = "Hello, how are you?"
   response = generate_response(model, tokenizer, user_query)
   print(response)
   

This covers the essential steps to get started with the Mistral 7B v3 model. You can now explore the model's capabilities, test it with various prompts, and even fine-tune it on your own data in subsequent steps.

The latest release of the Mistral 7B model, version 3, brings several notable changes. Unlike previous versions, this model has been directly released on Hugging Face, rather than just providing magnet links. While the performance is expected to be similar to the Mistral 7B model, there are a few prominent updates.

The model is now completely uncensored, with an extended vocabulary of a few hundred additional tokens. This expansion is related to its improved ability to perform function calling, which is now natively supported. The tokenizer has also been updated to accommodate these changes.

To get started with the new Mistral 7B v3 model, we'll walk through the installation of the Mistral Inference Python package, downloading the model, and running initial queries. We'll explore the model's capabilities, including its ability to generate responses to various prompts, handle sensitive topics, and demonstrate its reasoning skills.

One of the most impressive features of this model is its newly added function calling capability. We'll dive into an example of how the model can utilize a custom "get current weather" tool to provide weather information for a given location, showcasing its versatility in integrating external functionalities.

Overall, the Mistral 7B v3 model presents an exciting evolution in the world of large language models, with its expanded capabilities and the potential for further fine-tuning and integration with various applications.

The latest release of the Mistral 7B model, version 3, introduces a significant advancement - the ability to natively support function calling. This feature allows the model to leverage external tools and APIs to enhance its capabilities, going beyond the traditional language model constraints.

One of the key highlights of Mistral v3 is its extended vocabulary, which now includes several hundred additional tokens. This expansion is directly tied to the model's function calling functionality, enabling it to seamlessly integrate and utilize external resources.

To demonstrate this capability, we'll walk through an example where the model is tasked with retrieving the current weather for a specific location. The model is provided with a list of available tools, including a "get_current_weather" function that takes the location and temperature format as input parameters.

When prompted with a query like "What is the weather like today in Paris?", the model recognizes the need to utilize the external tool and generates the appropriate function call. It correctly identifies Paris as the location and determines that Celsius is the appropriate temperature format based on the context.

Similarly, when the query is changed to "What is the weather like today in San Francisco?", the model adapts and generates the function call with the correct location and temperature format.

This function calling mechanism extends beyond simple weather queries. The model can also handle more complex tasks, such as performing mathematical calculations or accessing other types of data and services.

The integration of function calling represents a significant step forward in the capabilities of large language models. By breaking free from the constraints of a closed-off knowledge base, Mistral v3 can dynamically leverage external resources to provide more comprehensive and tailored responses to user queries.

As we explore the full potential of Mistral v3's advanced function calling, we can expect to see even more innovative applications and use cases emerge, pushing the boundaries of what is possible with state-of-the-art language models.

The release of the Mistral 7B V3 model by the MRO team is a significant development in the world of large language models. This uncensored model boasts several notable changes, including an expanded vocabulary, native support for function calling, and an updated tokenizer.

One of the key highlights of this model is its ability to engage in function calling, which allows it to leverage external tools and resources to enhance its capabilities. The example showcased in the transcript demonstrates how the model can utilize a "get current weather" function to provide accurate weather information for a given location.

While the model's performance on various tasks appears to be on par with the previous Mistral 7B model, the introduction of the function calling feature sets it apart and opens up new possibilities for its application. The transcript also highlights the model's ability to handle tasks that require multi-step reasoning, such as the glass door problem, which it solved effectively.

However, the model's responses on certain sensitive topics, such as breaking into a car, highlight the need for careful consideration of the ethical implications of such models. The transcript's inclusion of a disclaimer regarding the use of this information for illegal activities is a commendable approach.

Overall, the Mistral 7B V3 model represents a significant step forward in the development of large language models, with its function calling capabilities being a particularly noteworthy feature. As the author suggests, further exploration of this model, including fine-tuning and integration with the Local GPT project, will be an exciting area of focus for the future.