Meta-Llama-3-8B-Instruct-BPE-fix-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 release b2777<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.

This model is made with the BPE fixes from llama.cpp

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!

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