Meta-Llama-3-8B-Instruct-GGUF

💫 Community Model> Llama 3 8B Instruct by Meta

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Model creator: meta-llama<br> Original model: Meta-Llama-3-8B-Instruct<br> GGUF quantization: provided by bartowski based on llama.cpp PR 6745<br>

Model Summary:

Llama 3 represents a huge update to the Llama family of models. This model is the 8B parameter instruction tuned model, meaning it's small, fast, and tuned for following instructions.<br> This model is very happy to follow the given system prompt, so use this to your advantage to get the behavior you desire.<br> Llama 3 excels at all the general usage situations, including multi turn conversations, general world knowledge, and coding.<br> This 8B model exceeds the performance of Llama 2's 70B model, showing that the performance is far greater than the previous iteration.

Prompt Template:

Choose the 'Llama 3' preset in your LM Studio.

Under the hood, the model will see a prompt that's formatted like so:

<|begin_of_text|><|start_header_id|>system<|end_header_id|>

{system_prompt}<|eot_id|><|start_header_id|>user<|end_header_id|>

{prompt}<|eot_id|><|start_header_id|>assistant<|end_header_id|>

Use case and examples

Llama 3 should be great for anything you throw at it. Try it with conversations, coding, and just all around general inquiries.

Creative conversations

Using a system prompt of You are a pirate chatbot who always responds in pirate speak!

General knowledge

Coding

Technical Details

Llama 3 was trained on over 15T tokens from a massively diverse range of subjects and languages, and includes 4 times more code than Llama 2.

This model also features Grouped Attention Query (GQA) so that memory usage scales nicely over large contexts.

Instruction fine tuning was performed with a combination of supervised fine-tuning (SFT), rejection sampling, proximal policy optimization (PPO), and direct policy optimization (DPO).

Check out their blog post for more information here

Special thanks

🙏 Special thanks to Georgi Gerganov and the whole team working on llama.cpp for making all of this possible.

🙏 Special thanks to Kalomaze for his dataset (linked here) that was used for calculating the imatrix for these quants, which improves the overall quality!

Disclaimers

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