2. 在主函数中,根据解析到的参数调用`write_model`函数。该函数将从输入目录中加载LLaMA模型的权重,并进行转换,最后保存到输出目录中。 3. 在`write_model`函数中,根据模型的大小读取对应的参数,这些参数包括层数,头数,维度等。然后根据模型是否分片进行不同的处理。对于未分片的模型,直接加载模型权重并转换成适应...
PAI-EAS provides a convenient way to deploy and run AI applications, including Llama 2 models. There are two methods to run Llama 2 on PAI-EAS: WebUI and API. The WebUI method allows you to interact with the model through a web interface, while the API method enables programmatic access...
In Step1, setting low_in_low_bit='nf4' within the AutoModelForCausalLM module of the bigdl.llm.transformers converts all Linear layers (excluding the 'lm_head') to 4-bit NormalFloat. Then, by executing model.to("xpu"), the converted Llama 2 7B model is transferred from ...
Llama 2 采用了 Llama 1 的大部分预训练设置和模型架构。他们使用标准的Transformer架构，应用RMSNorm进行...
python convert_llama_weights_to_hf.py \ --input_dir /xxxx/llama-2-7b/ \ --model_size 7B ...
下面,我们来尝试用LLaMA 2 7b模型来进行文本补全生成,命令如下: torchrun --nproc_per_node 1 example_text_completion.py --ckpt_dir llama-2-7b/ --tokenizer_path tokenizer.model --max_seq_len 128 --max_batch_size 4 这条命令使用torchrun启动了一个名为example_text_completion.py的PyTorch训练脚本...
, from hyper-specialization (Scialom et al., 2020b), it is important before a new Llama 2-Chat tuning iteration to gather new preference data using the latest Llama 2-Chat iterations. This step helps keep the reward model on-distribution and maintain an accurate reward for the latest model...
VideoLLaMA2.1-7B-AV Chat Fine-tuned BEATs_iter3+(AS2M)(cpt2) VideoLLaMA2.1-7B-16F 🤗 Demo It is highly recommended to try our online demo first. To run a video-based LLM (Large Language Model) web demonstration on your device, you will first need to ensure that you have the necess...
That is why we believe pretraining a 1.1B model for 3T tokens is a reasonable thing to do. Even if the loss curve does not go down eventually, we can still study the phenomenon of saturation and learn something from it.2. What does "saturation" mean?The figure from the Pythia paper ...
MODEL_PATH="llama-2-70b" elif [[ $m == "70B-chat" ]]; then SHARD=7 MODEL_PATH="llama-2-70b-chat" fi 最后下载这些文件并校验: for m in ${MODEL_SIZE//,/ } do ... # Set up MODEL_PATH and SHARD based on the model size ...