lmstudio-community/Meta-Llama-3-70B-Instruct-BPE-fix-GGUF

💫 Community Model> Llama 3 70B Instruct by Meta

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Model creator: meta-llama
Original model: Meta-Llama-3-70B-Instruct
GGUF quantization: provided by bartowski based on llama.cpp release b2777

Model Summary:

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.

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 cases and examples to come.

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).

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

🙏 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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