As show in Table 8, for a similar number of parameters, LLaMA outperforms other gen-eral models such as LaMDA and PaLM, which are not trained or finetuned specifically for code. LLaMA with 13B parameters and more outper-forms LaMDA 137B on both HumanEval and MBPP. LLaMA 65B also outper...
number of parameters on (tensor, pipeline) model parallel rank (1, 0): 2007764992 number of parameters on (tensor, pipeline) model parallel rank (2, 0): 2007764992 number of parameters on (tensor, pipeline) model parallel rank (3, 0): 2007764992 ...
optimizer = AdamW(filter(lambda p: p.requires_grad, model.parameters())) 第二阶段预训练 第二阶段预训练使用LoRA技术,为模型添加LoRA权重(adapter),训练embedding的同时也更新LoRA参数。 首先,修改运行脚本run_pt.sh,需要修改的部分参数如下: --model_name_or_path: 原版HF格式的LLaMA模型所在目录 --tokeni...
In order to make the model focus more on waste image features while keeping the amount of parameters computationally small, we introduce the SimAM attention mechanism. Additionally, knowledge distillation is used to further compress the number of parameters in the model. By training and testing on ...
Benchmark the performance of the inference for various parameters. Run default benchmark llama-bench -m model.gguf#Output:#| model | size | params | backend | threads | test | t/s |#| --- | ---: | ---: | --- | ---: | ---: | ---: |#| qwen2 1.5B Q4_0 | 885.97 ...
Mistral models has been very well received by the open source community thanks to the usage of Grouped-query attention (GQA) for faster inference, making it highly efficient and performing comparably to model with twice or three times the number of parameters. Today, we a...
We read every piece of feedback, and take your input very seriously. Include my email address so I can be contacted Cancel Submit feedback Saved searches Use saved searches to filter your results more quickly Cancel Create saved search Sign in Sign up Reseting focus {...
# script parameters model_id: "meta-llama/Meta-Llama-3-70b" # Hugging Face model id dataset_path: "." # path to dataset max_seq_len: 3072 # 2048 # max sequence length for model and packing of the dataset # training parameters output_dir: "./llama-3-70b-hf-no-robot" # Temporary ...
# Number of test set batches, -1 uses the entire test set. test_batches: 100 # Maximum sequence length. max_seq_length: 8192 # Use gradient checkpointing to reduce memory use. grad_checkpoint: true # LoRA parameters can only be specified in a config file ...
number of tokens in prompt = 151 -> '' 8893 -> 'Build'292 -> 'ing'263 -> ' a'4700 -> ' website'508 -> ' can'367 -> ' be'2309 -> ' done'297 -> ' in'29871 -> ' '29896 -> '1'29900 -> '0'2560 -> ' simple'6576 -> ' steps'29901 -> ':'sampling parameters:...