> number of parameters on (tensor, pipeline) model parallel rank (0, 0): 354871296 loading checkpoint from /workspace/model/megatron-models/345m-init-mp-out at iteration 5000 checkpoint version 3.0 successfully loaded checkpoint from /workspace/model/megatron-models/345m-init-mp-out at iteration...
n_params=sum(p.numel()forpinself.transformer.parameters())print("number of parameters:%.2fM"%(n_params/1e6,))def_init_weights(self,module):ifisinstance(module,nn.Linear):torch.nn.init.normal_(module.weight,mean=0.0,std=0.02)ifmodule.biasisnotNone:torch.nn.init.zeros_(module.bias)elifi...
# note: this is a total of 1001 data shards. If you only want to test things # out and don't want to do an actual run, feel free to append the number of # training shards to download (e.g. for just 10 shards: ./edu_fineweb.sh 10) # the full dataset is ~200GB, we can ...
GPT-4 has over 1 trillion parameters, according to reporting from U.S. news outletSemafor. While OpenAI has not officially confirmed the number of parameters, early rumors that GPT-4 would have over 100 trillion parameters have beenstrongly denied by OpenAI CEO Sam Altman. A parameter is a v...
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In early 2019, OpenAI proposed GPT-2, a scaled-up version of the GPT-1 model that increased the number of parameters and the size of the training dataset tenfold. The number of parameters of this new version was 1.5 billion, trained on 40 GB of text. In November 2019, OpenAI released...
Transformer-based models are a stack of either transformer encoder or decoder blocks. Encoder (decoder) blocks have the same architecture and number of parameters. T5 consists of stacks of transformer encoders and decoders, while GPT-2 is composed of only transformer decoder blocks (Figure 1). ...
2022), demonstrate efficient reasoning skills in some contexts. It is worth noting that the most powerful LLMs, those with over 100 billion parameters (Wei et al., 2022a), appear to be the ones with the most impressive reasoning abilities, capable of solving a vast array of problems accurat...
# init a GPT and the optimizertorch.manual_seed (1337)gpt = GPT (config)optimizer = torch.optim.AdamW (gpt.parameters (), lr=1e-3, weight_decay=1e-1)# train the GPT for some number of iterationsfor i in range (50): logits = gpt (X) loss = F.cross_entropy (logits, Y...
In the early days, OpenAI reported on the number of features in its GPT models as a proxy metric for capabilities. For example, GPT had 117 million parameters, GPT-2 had up to 1.5 billion parameters and GPT-3 had up to 175 billion parameters. However, bigger isn't always better. In ...