"I try models on LMS first with my test questions before loading them in ooba. 90% of the models fail my tests in LMS but then pass in ooba. LMS has more restrictions than the models themselves."

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"LMStudio is closed source and after reading the license I won't touch anything this company ever makes.

Quoting https://lmstudio.ai/terms

'Updates. You understand that Company Properties are evolving. As a result, Company may require you to accept updates to Company Properties that you have installed on your computer or mobile device. You acknowledge and agree that Company may update Company Properties with or WITHOUT notifying you. You may need to update third-party software from time to time in order to use Company Properties.

Company MAY, but is not obligated to, monitor or review Company Properties at any time. Although Company does not generally monitor user activity occurring in connection with Company Properties, if Company becomes aware of any possible violations by you of any provision of the Agreement, Company reserves the right to investigate such violations, and Company may, at its sole discretion, immediately terminate your license to use Company Properties, without prior notice to you.'

If you claim your software is private, i won't accept you saying that anytime you want you may embed backdoor via hidden update. I don't think this will happen though.

I think it will just be a rug pull - one day you will receive a notice that this app is now paid and requires a license, and your copy has a time bomb after which it will stop working.

They are hiring yet their product is free. What does it mean? They either have investors (doubt it, it's just gui built over llama.cpp), you are the product, or they think you will give them money in the future. I wish llama.cpp would have been released under AGPL."

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We will continue to look for the latest and best alternatives.

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Still strange Meta (Facebook) is creating this local open source Llama LLM. When it is widely known Meta Facebook has been actively participating in censorship and violating freedom of speech and basic human rights worldwide.?

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Advanced Usage of Llama 3 Models
ollama offers a range of advanced options and configurations to enhance your experience, including:

Fine-tuning: Fine-tune the Llama models on your own data to customize their behavior and performance for specific tasks or domains.
Quantization: Reduce the memory footprint and improve inference speed by quantizing the models.
Multi-GPU Support: Leverage multiple GPUs to accelerate inference and fine-tuning processes.
Containerization: Export containerized versions of your fine-tuned or quantized models for easy sharing and deployment across different systems.
Fine-tuning Llama 3 Models
Fine-tuning is the process of adapting a pre-trained language model like Llama 3 to a specific task or domain by further training it on a relevant dataset. This can significantly improve the model's performance and accuracy for the target use case.

The Fine-tuning Process
Prepare Dataset: Gather a high-quality dataset relevant to your target task or domain. The dataset should be formatted correctly, typically as a collection of input-output pairs or prompts and expected responses.
Load Pre-trained Model: Load the pre-trained Llama 3 model (8B or 70B) that you want to fine-tune.
Set Hyperparameters: Determine the appropriate hyperparameters for the fine-tuning process, such as learning rate, batch size, and number of epochs.
Fine-tune: Run the fine-tuning process, which involves updating the model's parameters using your dataset and the specified hyperparameters.
Evaluate: Evaluate the fine-tuned model's performance on a held-out test set or relevant benchmarks.
Deploy: Deploy the fine-tuned model for your target application or use case.

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