Navigator supports a variety of open source LLMs, and is adding more all the time. Below is the current list of supported models with links to their Hugging Face model pages where you can learn more about each model’s strengths and weaknesses. Change the model you are training here:
For most use cases that require training or inference on consumer hardware, we recommend using a 7B parameter model. This is the sweet spot between model performance and size for consumer devices. All LLM features in Navigator work well with 7B parameter models.
Full List of Supported Models
Click on a model below to learn more on Hugging Face:
TinyLlama/TinyLlama-1.1B-Chat-v1.0
Parameters: 1.1 billion
Description: Optimized for lightweight deployment with efficient performance.
gradientai/Llama-3-8B-Instruct-262k
Parameters: 8 billion
Description: Fine-tuned on 262k instruction-following examples, ideal for complex tasks.
mlx-community/Meta-Llama-3-8B-Instruct
Parameters: 8 billion
Description: Available in 4-bit and 8-bit quantized versions, balancing size and performance.
mlx-community/gemma-1.1-2b-it-4bit
Parameters: 2 billion
Description: Designed for Italian language tasks with 4-bit precision, efficient for specialized usage.
mlx-community/Mixtral-8x22B-Instruct-v0.1-4bit
Parameters: 22 billion
Description: Multi-modal model supporting diverse instructional tasks, with 4-bit quantization for better efficiency.
mlx-community/WizardLM2-8x22B-4bit-mlx
Parameters: 22 billion
Description: Advanced language model suitable for extensive instructional prompts, available in 4-bit format for reduced resource consumption.
mlx-community/Meta-Llama-3-70B-Instruct-4bit
Parameters: 70 billion
Description: One of the largest models in the Llama series, finely tuned for complex instructional tasks, with 4-bit optimization.
mlx-community/Phi-3-mini-4k-instruct
Parameters: Mini model optimized for 4k input sequences
Description: Available in 4-bit and 8-bit versions for versatile use in low-resource environments.
mlx-community/Phi-3-mini-128k-instruct
Parameters: Mini model fine-tuned on 128k instructions
Description: Supporting both 4-bit and 8-bit precision for flexible deployment.
mlx-community/OpenELM-270M-Instruct
Parameters: 270 million
Description: Ultra-lightweight model optimized for instructional tasks in resource-constrained environments.
mlx-community/OpenELM-450M-Instruct
Parameters: 450 million
Description: Slightly larger version for handling more complex tasks while maintaining efficiency.
mlx-community/OpenELM-1_1B-Instruct
Parameters: 1.1 billion
Description: Balances performance with efficiency, available in 4-bit and 8-bit quantizations for different use cases.
mlx-community/Qwen1.5-1.8B-Chat-4bit
Parameters: 1.8 billion
Description: Chat-oriented model optimized for conversational AI, available in 4-bit for resource efficiency.
mlx-community/Qwen1.5-0.5B-Chat-4bit
Parameters: 500 million
Description: Lightweight chat model designed for fast inference, available in 4-bit format.
mlx-community/Qwen1.5-7B-Chat-4bit
Parameters: 7 billion
Description: Mid-sized chat model providing a balance between performance and computational cost, in 4-bit format.
mlx-community/Qwen1.5-72B-Chat-4bit
Parameters: 72 billion
Description: High-end chat model for sophisticated conversational AI tasks, optimized in 4-bit for enhanced performance.
Maykeye/TinyLLama-v0
Parameters: Experimental
Description: Experimental version of TinyLLama, designed for ultra-low resource environments, focusing on efficiency over size.