Comments (1)
Definitely! Closing this issue in favor of #176
from litgpt.
Related Issues (20)
- API has confusing/wrong description HOT 1
- Set kv cache size based on resource needs
- Pretrain then finetune on multiple GPUs error
- adding Aya model (checkpoints) to the supported checkpoints list that litgpt support
- did not convert the model to litgpt format, why? HOT 3
- Support downloading and using quantized weights (GGUF)
- Autodownload + conversion now fails on the first try
- Skip safetensors->bin file conversion
- Mistral Large 2 Checkpoints HOT 1
- Support sequential generation in litgpt serve HOT 1
- Provide minimal installation
- Implement prompt caching to speed up inference HOT 1
- access hidden layer(s) from a model
- TPU Pod Training
- MPS test gave out gibberish result HOT 1
- Use FlexAttention
- Tensor parallelism generates non-sensical outputs HOT 1
- Gemma 2B weights seem to have changed HOT 1
- Disable KV cache option
- attention mask is incorrect when generate with softcapping HOT 8
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from litgpt.