Inference parameters control the text generation process at the endpoint. The maximum new tokens control refers to the size of the output generated by the model. Note that this is not the same as the number of words because the vocabulary of the model is not the...
Llama 2 and Llama 2-Chat, at scales up to 70B parameters. On the series of helpfulness and safety benchmarks we tested, Llama 2-Chat models generally perform better than existing open-source models. They also appear to be on par with some of the closed-source models, at least on...
args (ModelArgs): Model configuration parameters. Attributes: n_heads (int): Number of attention heads. dim (int): Dimension size of the model. head_dim (int): Dimension size of each attention head. attention (Attention): Attention module. feed_forward (FeedForward): FeedForward modu...
The inference performance of Llama 2 7 billion and Llama 2 13 billion parameters models are evaluated on a 600W OAM device which has two GPUs (tiles) on the package, while we only used one of the tiles to run the inference. Figure 4 shows that Intel Data Center GPU Max single tile ca...
LLama2是MetaAI公司在2023年推出的一款半开源LLM(所谓半开源即为只有Inference没有Train过程),它是Llama的下一代版本,训练数据集2万亿token,上下文长度由llama的2048扩展到4096,可以理解和生成更长的文本,包括7B、13B、70B三个模型,展现出了卓越的性能,使其迅速在基准测试中崭露头角,标志着生成式人工智能领域的一次...
# Set training parameters training_arguments = TrainingArguments( output_dir=output_dir, num_train_epochs=num_train_epochs, per_device_train_batch_size=per_device_train_batch_size, gradient_accumulation_steps=gradient_accumulation_steps, optim=optim, ...
# Set training parameters training_arguments = TrainingArguments( output_dir=output_dir, num_train_epochs=num_train_epochs, per_device_train_batch_size=per_device_train_batch_size, gradient_accumulation_steps=gradient_accumulation_steps, optim=optim, ...
保存总数限制--seed 42 \# seed:随机种子--disable_tqdmfalse\# disable_tqdm:禁用tqdm--ddp_find_unused_parametersfalse\# 注释:ddp查找未使用的参数--block_size 2048 \# block_size:块大小--report_to tensorboard \# report_to:报告给tensorboard--overwrite_output_dir \# overwrite_output_dir:覆盖输出...
# Set training parameters training_arguments = TrainingArguments( output_dir=output_dir, num_train_epochs=num_train_epochs, per_device_train_batch_size=per_device_train_batch_size, gradient_accumulation_steps=gradient_accumulation_steps, optim=optim, ...
The largest LLaMA 2 model has 70 billion parameters. The parameter count refers to the amount of weights, as in float32 variables, that are adjusted to correspond to the amount of text variables at play across the corpus. The corresponding parameter count therefore correlates directly to the cap...