如果你修改的modeling_llama.py在同一个环境中，再次加载模型时，应该能够正常使用你修改过的forward方法。
这个分档标注在 Llama 2 中被用来设定 Reward Model Training 损失函数中的 margin(详见 Model Training 5.3 节);在 Llama 3 中则被用来筛选数据,reward modeling 和 DPO 都只会使用 significantly better 或 better 两档的数据进行训练。 we also ask annotators to label the degree to which they prefer the...
立即登录 没有帐号,去注册 编辑仓库简介 简介内容 Codellama-13b-oasst-sft-v10可能是一个用于软件测试的模型,具有13亿个参数,并且可能在特定软件测试场景中进行了微调。 主页 取消 保存更改 1 https://gitee.com/hf-models/codellama-13b-oasst-sft-v10.git git@gitee.com:hf-models/codellama-13b-oa...
在视觉标记序列之上,我们的 Transformer 模型与自回归语言模型几乎相同,因此我们采用了 LLAMA[80] 的 Transformer 架构,这是一种流行的具有广泛可用实现的开源语言模型。我们使用 4096 个标记的上下文长度,可以在我们的 VQGAN 分词器下拟合 16 张图像。与语言模型类似,我们在每个视觉句子的开头添加一个 [BOS](句子...
export WEBLLAMA_PROJECT_DIR=/path/to/the/modeling/directory/ # For example, if you are in the modeling directory, you can run: export WEBLLAMA_PROJECT_DIR=$(pwd) Install Dependencies You need to install the dependencies by running the following command: pip install -e .[extra] pip install...
Codes for VPGTrans: Transfer Visual Prompt Generator across LLMs. VL-LLaMA, VL-Vicuna. llm vision-language-model large-scale-language-modeling vl-llm Updated May 29, 2023 Python Improve this page Add a description, image, and links to the large-scale-language-modeling topic page so that ...
经过上一步抽取到visual sequence之后,后续的步骤与基于AR(autoregressive)的LLM几乎相同,即使用sequence前面的token逐渐预测整个visual sequence。具体实践上,作者使用LLaMA作为基本框架,context length设定为4096能够组成16张图像。模型在UVD v1(420billions,大约16亿图像)数据集上训练1个epoch。
Large language models (LLMs), such as ChatGPT (OpenAI,2022), Gemini (DeepMind,2023), LLaMA (Touvron et al.,2023), Alpaca (Taori et al.,2023), and GLM (Zeng et al.,2023), are the latest paradigm of language models, which evolve from early statistical language models (Bellegarda,2004...
aflame monitor monitor de la llama [translate] aAll components of the equipment exposed to the weather shall be sea water resistance. Internal components shall be fully enclosed with heavy duty seals and sufficient heat dissipation mechanism (e.g. ventilation, conduction, etc.) to protect the ...
# Copied from transformers.model.llama.modeling_llama.repeat_kv def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor: """ This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch, num_key_value_heads, seq...