我在llama2的modeling_llama.py代码上对forward函数做了修改，但没有改变其结构，就是在forward里加了一...
LlamaRMSNorm is equivalent to T5LayerNorm """ super().__init__() self.weight = nn.Parameter(torch.ones(hidden_size)) self.variance_epsilon = eps def forward(self, hidden_states): input_dtype = hidden_states.dtype hidden_states = hidden_states.to(torch.float32) ...
🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX. - transformers/src/transformers/models/llama/modeling_llama.py at main · huggingface/transformers
HtmlRAG: HTML is Better Than Plain Text for Modeling Retrieval Results in RAG Systems (WWW 2025) - HtmlRAG/llm_modeling/Llama32/modeling_llama.py at main · plageon/HtmlRAG
确认导入路径的一个方法是检查transformers库的源代码或安装目录,看看llama文件夹和modeling_llama.py文件是否存在。 4. 尝试重新安装或更新transformers库 如果以上步骤都不能解决问题,尝试重新安装transformers库可能是一个有效的解决方案。您可以使用pip的--force-reinstall选项来强制重新安装库: bash pip install --forc...
p modeling.py test_model --model_name causal --model_path gpt2 p modeling.py test_model --model_name llama --model_path decapoda-research/llama-7b-hf p modeling.py test_model --model_name llama --model_path chavinlo/alpaca-native ...
PyLlama: a stable and versatile Python toolkit for the electromagnetic modeling of multilayered anisotropic mediaMélanie BaySilvia VignoliniKevin Vynck
Additionally, while the fine-tuned LLaMA-2 model supports the constructs of natural language in its prompts, the flexibility of its inputs suggests that CrystaLLM may be conditioned on other properties of the structure as well, including those not traditionally included in the CIF format. Finally...
In comparison to the fine-tuned LLaMA-2 model, the largest CrystaLLM model has 200 million parameters, whereas the smallest fine-tuned LLaMA-2 model has 7 billion parameters, a difference of more than an order of magnitude in the number of parameters. The smaller size of CrystaLLM makes it...
👑 Easy-to-use and powerful NLP and LLM library with 🤗 Awesome model zoo, supporting wide-range of NLP tasks from research to industrial applications, including 🗂Text Classification, 🔍 Neural Search, ❓ Question Answering, ℹ️ Informat