weight (nn.Parameter): Learnable scaling parameter. """ super().__init__() self.eps = eps self.weight = nn.Parameter(torch.ones(dim)) def _norm(self, x): """ Apply the RMSNorm normalization to the input tensor. Args: x (torch.Tensor): The input tensor. Returns: torch.Tenso...
Llama 2 is an auto-regressive language model that uses an optimized transformer architecture. Llama 2 is intended for commercial and research use in English. It comes in a range of parameter sizes—7 billion, 13 billion, and 70 billion—as well as pre-trained an...
We train for one epoch over the training data. In earlier experiments, we found that training longer can lead to over-fitting. We use the sameoptimizerparameters as for the base model. The maximum learning rate is 5 × 10−6 for the 70BparameterLlama 2-Chat and 1 × 10−5 for the...
值得注意的是，Mistral 和 Llama 2 是 70 亿参数的大模型。相形之下，RoBERTa-large (355M 参数) 只是一个小模型，我们用它作为比较的基线。本文，我们使用 PEFT (Parameter-Efficient Fine-Tuning，参数高效微调) 技术: LoRA (Low-Rank Adaptation，低秩适配) 来微调带序列分类任务头的预训练模型。LoRA 旨在显...
One 4thGen Xeon socket delivers latencies under 100ms with 7 billon parameter and 13 billon parameter size of models. Users can run 2 parallel instances, one on each socket, for higher throughput and to serve clients independently. Alternatively, users can leverageIntel Extension for PyTorch* and...
这个temperature parameter也在探索中扮演了一个重要的角色,温度越高能使我们样本到更多样化的输出。 如图8(左图是Llama 2-Chat的SFT右图是Llama 2-Chat RLHF)展示了不同temperatures下在N个样本间的最大奖励曲线(N ∈[1,…,100]∈[1,…,100]∈[1,…,100])。我们有观察到在模型迭代更新过程中最佳的...
LLama2是MetaAI公司在2023年推出的一款半开源LLM(所谓半开源即为只有Inference没有Train过程),它是Llama的下一代版本,训练数据集2万亿token,上下文长度由llama的2048扩展到4096,可以理解和生成更长的文本,包括7B、13B、70B三个模型,展现出了卓越的性能,使其迅速在基准测试中崭露头角,标志着生成式人工智能领域的一次...
dtype for 4-bit base modelsbnb_4bit_compute_dtype = "float16"# Quantization type (fp4 or nf4)bnb_4bit_quant_type = "nf4"# Activate nested quantization for 4-bit base models (double quantization)use_double_nested_quant = False# LoRA attention dimensionlora_r = 64# Alpha parameter for ...
Llama-2-Chat models outperform open-source chat models on most benchmarks we tested, and in our human evaluations for helpfulness and safety, are on par with some popular closed-source models like ChatGPT and PaLM. Model Developers Meta Variations Llama 2 comes in a range of parameter sizes...
Parameter Efficient Fine-Tuning(PEFT)方法是一组使llm适应下游任务的方法,例如在内存受限的设备(如T4GPU 提供16GB VRAM)上进行摘要或问答。通过Peft对LLM的部分进行微调,仍然可以获得与完全微调相比的结果。如LoRA和Prefix Tuning是相当成功的。peft库是一个HuggingFace库,它提供了这些微调方法,这是一个可以追溯到2023...