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Meta-Llama-3-70B-Instruct-GGUF

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Model creator: meta-llama
Original model: Meta-Llama-3-70B-Instruct
GGUF quantization: provided by bartowski based on llama.cpp PR 6745

Llama 3 represents a huge update to the Llama family of models. This model is the 70B parameter instruction tuned model, with performance reaching and usually exceeding GPT-3.5.
This is a massive milestone, as an open model reaches the performance of a closed model over double its size.
This model is very happy to follow the given system prompt, so use this to your advantage to get the behavior you desire.
Llama 3 excels at all the general usage situations, including multi turn conversations, general world knowledge, and coding.

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 cases and examples to come.

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).

Only IQ1_M and IQ2_XS use importance matrix (iMatrix), the rest are made with the standard quant algorithms.

Check out their blog post for more information here

🙏 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 the IQ1_M and IQ2_XS quants, which makes them usable even at their tiny size!

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