Comments (4)
zliendo
commented on July 22, 2024
oh, one more piece of information, if sequence_length <=256, then I do get 35 ms/token for llama2 and codellama (unfortunately for code generation it usually needs sequence_length > 256)
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jeffhataws
commented on July 22, 2024
Thanks @zliendo . We will take a look.
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jeffhataws
commented on July 22, 2024
Thanks @zliendo I have checked the results in the blog and they are the results as of 11/7/23 when the blog was published. We are continuing to improve the performance of LLaMA on Neuron so will hope that you will try an upcoming release. Please keep an eye out for what's new and the performance page for updated performance data.
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zliendo
commented on July 22, 2024
Thank you so much! I will for sure try upcoming releases.
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Related Issues (20)
- Avoid splitting Hugging Face Hub checkpoint files on disk HOT 7
- Can't save/serialize any models except GPT2 HOT 4
- Compilation error on llama 7 B with batch size 8 HOT 4
- from_pretrained is broken after transformers made safetensor serialization default HOT 1
- LLaMA fails when the input token length is over 1790 tokens HOT 6
- Llama2 inference overhead time way too long HOT 6
- Mixtral Model support HOT 2
- Vicuna13B model support HOT 1
- Inf2 Modified Llama 2 Loading Issue HOT 11
- Skipping generation for useless tokens, and modiying cacheids HOT 3
- How to use generate() with inputs_embeds HOT 2
- Mixtral config issue -- not handling null well HOT 8
- Generate Llama 2 from Embeddings HOT 5
- Infering logits from `model.forward` for the entire batch instead of the last forward's output. HOT 6
- Support for MPT model HOT 1
- `stopping_criteria_list(input_ids, probs)` does not check for the correct sequence. HOT 4
- User feedback when compiling and reloading a large model HOT 1
- Issue while compiling Mistral 7B 0.2 Instruct HOT 5
- Backward compatibility with saved llama 2 compiled artifacts HOT 1
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