![]() Our V0.1 chat model is finetuned using this script. We include a simple full-parameter finetuning & inference script in sft. Please refer to PRETRAIN.md for instructions on how to pretrain TinyLlama. Below are some throughputs that we measure: Framework The fact that TinyLlama is a relatively small model with grouped query attention means it is also fast during inference. The MPT number comes from here, in which they say MPT-1.3B " was trained on 440 A100-40GBs for about half a day" on 200B tokens. The Pythia number comes from their paper. You can also pretrain TinyLlama on 3090/4090 GPUs with a smaller per-gpu batch size.īelow is a comparison of the training speed of our codebase with that of Pythia and MPT. Those optimizations also greatly reduce the memory footprint, allowing us to stuff our 1.1B model into 40GB GPU RAM and train with a per-gpu batch size of 16k tokens. It means you can train a chinchilla-optimal TinyLlama (1.1B param, 22B tokens) in 32 hours with 8 A100. Thanks to those optimizations, we achieve a throughput of 24k tokens per second per A100-40G GPU, which translates to 56% model flops utilization without activation checkpointing (We expect the MFU to be even higher on A100-80G). Rotary positional embedding are from the FlashAttention repo. multi-gpu and multi-node distributed training with FSDP.Ĭredit: flash attention 2, fused layernorm, fused cross entropy loss, and fused.Our codebase supports the following features: See Issue 27 for a minor bugĮxcluded GitHub subset of Slimpajama Sampled all code from Starcoderdataģ trillion (slightly more than 3 epochs/1430k steps) Layers: 22, Heads: 32, Query Groups: 4, Embedding Size: 2048, Intermediate Size (Swiglu): 5632Ĭosine with 2000 warmup steps. Training Detailsīelow are some details of our training setup: Setting Moreover, our code can be a reference for enthusiasts keen on pretraining language models under 5 billion parameters without diving too early into Megatron-LM. Enabling real-time dialogue generation in video games.Deployment on edge devices with restricted memory and computational capacities, for functionalities like real-time machine translation without an internet connection (the 4bit-quantized TinyLlama-1.1B's weight only takes up 550MB RAM).Assisting speculative decoding of larger models.Tiny but strong language models are useful for many applications. Meanwhile, you can track the live cross entropy loss here. Note that the learning rate of the base model has not cooled down yet so we recommend you to also use the finetuned chat model. We are crafting a note offering possible explaination on why there is a significant improvement from 2T to 2.5T checkpoint (It is related to bos_id issue) We will postpone the release of TinyLlama 1.5T checkpoint ( click here for more information) TinyLlama-1.1B-intermediate-step-50k-105b We will be rolling out intermediate checkpoints following the below schedule. You can find the evaluation results of TinyLlama in EVAL.md. More eval benchmarks are added and documented in EVAL.md. We released a chat model finetuned on OpenAssisant and simple finetuning scripts is added. We released the intermediate checkpoint trained on 503B tokens. We added a chat demo so that you can play with TinyLlama-Chat-V0.1 right away. We document all intermediate checkpoints here. Do check out the speculative_decoding/README.md. : Add examples in speculative decoding with llama.cpp.This compactness allows it to cater to a multitude of applications demanding a restricted computation and memory footprint. Besides, TinyLlama is compact with only 1.1B parameters. This means TinyLlama can be plugged and played in many open-source projects built upon Llama. We adopted exactly the same architecture and tokenizer as Llama 2.
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