I'm looking for a free LLM Model run on my home equipment? I can run up to 32B @ Q4, Still very usable speed-wise 8K–16K context comfortable. I've had recommendations of MythoMax, Mixtral, Falcon LLM. Model Requirements (Compressed) Type: Inst

Based on your requirements for a free LLM model that can run on your home equipment, here are a few recommendations: 1. **Hugging Face Models**: - Various models are available on Hugging Face that might fit your needs. Look for instruction-tuned models like **GPT-2** and its variants. Some are available in sizes up to 1.5B, but there are larger models that you can find. - Additionally, models like **Flan-T5** offer a good mix of instruction-following and chat capabilities. 2. **Alpaca**: - Created by Stanford, Alpaca is based on LLaMA and has various sizes. An instruct-tuned version could be very suitable for your needs, and it's lightweight enough to run on consumer hardware when quantized appropriately. 3. **LLaMA 2**: - Meta's LLaMA 2 models are available in various sizes (including 7B, 13B, and 70B). They have been fine-tuned for instruction-following tasks and can be quantized to fit your hardware constraints. 4. **Mistral**: - Mistral offers models like Mistral 7B and Mistral 7B Mix. While it may not meet the larger size requirement, it is optimized for chat and instruction-following and can provide a good experience in a quantized format. 5. **Falcon**: - The Falcon models, particularly Falcon 7B and Falcon 40B, offer excellent conversational capabilities and are designed with instruction-following in mind. 6. **Cerberus**: - There are models being developed, with specific focuses on long-context capabilities and instruction following. Check GitHub repositories and model-sharing platforms like Hugging Face and others for community-developed versions. **Best Practices for Running Models**: - Ensure you have the necessary libraries (like Hugging Face’s transformers) and dependencies set up to load and serve the models. - Use quantization tools such as **GPTQ** or **PEFT** that can help manage and optimize the performance of your models based on your RAM constraints and provide suitable formats for loading. **Note on Quantization**: - Implementing **Q4 or Q5** quantization can significantly improve your ability to run larger models by reducing their memory footprint while still maintaining a good level of performance. Make sure to check the specific licensing for each model to confirm that it fits your use case, especially if you plan to use them in commercial applications.