为便捷构建 LLM 应用,我们需要基于本地部署的 LLaMA3_1_LLM,自定义一个 LLM 类,将 LLaMA3.1 接入到 LangChain 框架中。完成自定义 LLM 类之后,可以以完全一致的方式调用 LangChain 的接口,而无需考虑底层模型调用的不一致。 为便捷构建 `LLM` 应用,我们需要基于本地部署的 `LLaMA3_1_LLM`,自定义一个 `...
per_device_eval_batch_size: 1 # batch size for evaluation gradient_accumulation_steps: 2 # number of steps before performing a backward/update pass optim: adamw_torch # use torch adamw optimizer logging_steps: 10 # log every 10 steps save_strategy: epoch # save checkpoint every epoch evaluat...
update：训练结果来看倒也确实是符合scaling law没错，因此我需要考虑措辞重新表达如下：llama3真不按optim...
per_device_eval_batch_size: 1 # batch size for evaluation gradient_accumulation_steps: 2 # number of steps before performing a backward/update pass optim: adamw_torch # use torch adamw optimizer logging_steps: 10 # log every 10 steps save_strategy: epoch # save checkpoint every epoch evaluat...
llama update the code to use the module's __call__ Mar 21, 2024 .gitignore Initial commit Feb 24, 2023 CODE_OF_CONDUCT.md Initial commit Feb 24, 2023 CONTRIBUTING.md Updating contributor guide Feb 29, 2024 LICENSE Update LICENSE Jul 21, 2023 ...
gradient_accumulation_steps: 2 # number of steps before performing a backward/update pass optim: adamw_torch # use torch adamw optimizer logging_steps: 10 # log every 10 steps save_strategy: epoch # save checkpoint every epoch evaluation_strategy: epoch # evaluate every epoch ...
Llama 2 请求地址：https://ai.meta.com/resources/models-and-libraries/llama-downloads/ 来源：https://ai.meta.com/blog/llama-2-update/?utm_source=twitter&utm_medium=organic_social&utm_campaign=llama2&utm_content=card 随着 Llama 2 的逐渐走红，大家对它的二次开发开始流行起来。前几天，OpenAI ...
PRETRAINED_MODEL_PATH="/root/notebook/common_data/Meta-Llama-3-8B"git pull # update example benchmark from branch feature/colossal-infercd ColossalAI/examples/inference/python benchmark_llama3.py -m llama3-8b -b 32 -s 128 -o 256 -p PRETRAINED_MODEL_PATH 单卡H100对LLaMA3-8B进行Benchmark...
January’s update brings several exciting new features to boost your productivity in AI development. Here's a closer look at what's included: Support for OpenAI’s new o1 Model: We've added access to GitHub hosted OpenAI’s latest o1 model. This new model replaces the o1-preview and ...
# 获取模型的微调训练算法 est = model.get_estimator() # 获取PAI提供的公共读数据和预训练模型 training_inputs = model.get_estimator_inputs() # 使用用户自定义数据 # training_inputs.update( # { # "train": "<训练数据集OSS或是本地路径>", # "validation": "<验证数据集的OSS或是本地路径>...