💫 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 PR 6745
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.
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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