Mistral 7B is a large language model (LLM) developed by Mistral AI, featuring 7.3 billion parameters. This model has been open-sourced under the Apache 2.0 license, offering the community free and unrestricted access to its capabilities.
The model is recognized for its exceptional performance, surpassing other models of similar size in benchmarks. Notably, it achieves higher scores than Meta's Llama 2 13B model and is competitive with the much larger Llama 70B model, despite having nearly half the number of parameters of the Llama 2 13B.
Mistral 7B's accessibility and performance make it a valuable resource for researchers and developers in AI, particularly for those interested in training and running LLMs with constrained resources. A common use case of the model is to fine-tune it using Parameter-Efficient Fine-Tuning (PEFT) techniques like LORA.
Access the model on Hugging Face: https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2
Mistral 7B employs an advanced transformer architecture, with enhancements in attention mechanisms and improved memory optimization. These architectural enhancements enable Mistral 7B to achieve faster processing speeds and lower latency at inference time, handling up to approximately 131,000 tokens in its attention span by the final layer.
Here are some of the components:
Mistral 7B is most commonly compared to LLaMA, a competing open source LLM created by Meta. Mistral 7B demonstrates significant advantages over various models in the LLaMA family, especially when it comes to performance metrics.
Source: Mistral
Here is a summary of benchmark results reported by Mistral, comparing the Mistral 7B model to its closest competitor, LLaMA 2:
Related content: Read our guide to Mistral 7B vs ChatGPT (coming soon)
There are several options for accessing Mistral 7B.
For those with experience in handling AI models, Mistral 7B can be accessed by downloading the model and its Docker images from the GitHub registry. Ensure that your setup includes a cloud virtual machine with at least 24GB of vRAM to run the model efficiently, though some configurations may only require 16GB with certain inference stacks.
Here is the magnet link to download the model weights:
magnet:?xt=urn:btih:208b101a0f51514ecf285885a8b0f6fb1a1e4d7d&dn=mistral-7B-v0.1
Ollama provides an effortless method to run large language models like Mistral 7B on macOS or Linux systems. You can get Ollama here.
After downloading Ollama, you can initiate the model using a simple command:
ollama run mistral
ollama run mistral:text
This requires a minimum of 8GB of RAM.
Hugging Face offers a straightforward way to deploy Mistral 7B on dedicated infrastructure. The Inference Endpoints service allows for 1-click deployment from the Model catalog, and the model runs on a single NVIDIA A10G GPU at approximately $1.30 per hour, with a latency of 33ms per token.
Learn more on the Hugging Face model page.
For those interested in using Mistral 7B in a conversational AI format, Perplexity AI offers an integration that allows Mistral 7B to answer questions via its search engine. Select
mistral-7b-instruct
To begin working with Mistral 7B, follow these steps to set up and run the model. This guide covers installation, downloading the model, and running it for both demonstration and interactive purposes.
First, ensure you have the necessary dependencies installed. Use the following command to install them:
pip install -r requirements.txt
Next, download the Mistral 7B model weights using
wget
wget https://models.mistralcdn.com/mistral-7b-v0-1/mistral-7B-v0.1.tar -O mistral-7B-v0.1.tar tar -xf mistral-7B-v0.1.tar
To run the model in a demonstration mode, use the following command. Replace /path/to/mistral-7B-v0.1/ with the path where the model is extracted:
python -m main demo /path/to/mistral-7B-v0.1/
For an interactive session where you can provide your own prompts, run:
python -m main interactive /path/to/mistral-7B-v0.1/
You can adjust the model's behavior by changing parameters such as max_tokens and temperature. For example:
python -m main interactive /path/to/mistral-7B-v0.1/ --max_tokens 256 --temperature 1.0
If you prefer a self-contained implementation, you can use
one_file_ref.py
python -m one_file_ref /path/to/mistral-7B-v0.1/
For models too large to fit into a single GPU's memory, use pipeline parallelism (PP) with
torchrun
torchrun --nproc-per-node 2 -m main demo /path/to/mixtral-7B-8x-v0.1/ --num_pipeline_ranks=2
By following these steps, you can set up and start using Mistral 7B for your AI projects.
Related content: Read our guide to how to use Mistral 7B (coming soon)
Fine tuning MIstral 7B is a complex process. We’ll provide only the general steps and link to other resources that provide more information:
For an in-depth review of fine-tuning with Mistral 7B, refer to the blog post by Maxime Labonne.
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Randall Babaoye is a full stack software engineer with experience in application development and DevOps.