模型显存占用分成两个部分,一部分是静态显存基本由模型参数量级决定,另一部分是动态显存在向前传播的过程中每个样本的每个神经元都会计算激活值并存储,用于向后传播时的梯度计算,这部分和batchsize以及参数量级相关。以下8bit量化优化的是静态显存,而梯度检查优化的是动态显存。 1. 8bit Quantization http
AlpacaEval:LLM-based automatic evaluation 开源模型王者vicuna,openchat, wizardlm Huggingface Open LLM Leaderboard MMLU只评估开源模型,Falcon夺冠,在Eleuther AI4个评估集上评估的LLM模型榜单,vicuna夺冠 https://opencompass.org.cn/ 上海人工智能实验室推出的开源榜单 Berkley出品大模型排位赛榜有准中文榜单 Elo评分...
{ "model_name_or_path": "THUDM/chatglm2-6b", "dataset_name_or_path": "/home/aistudio/mydata", "output_dir": "./checkpoints/chatglm2_lora_ckpts", "per_device_train_batch_size": 4, "gradient_accumulation_steps": 4, "per_device_eval_batch_size": 8, "eval_accumulation_steps"...
WizardLM 微软新发布13B,登顶AlpacaEval开源模型Top3,使用ChatGPT对指令进行复杂度进化微调LLama2 Falcon Falcon由阿联酋技术研究所在超高质量1万亿Token上训练得到1B,7B,40B开源,免费商用!土豪们表示钱什么的格局小了 Vicuna Alpaca前成员等开源以LLama13B为基础使用ShareGPT指令微调的模型,提出了用GPT4来评测模型效果 ...
For those using benefit subscriptions (such as Visual Studio Enterprise Subscription) or those looking to quickly test the fine-tuning and deployment process, this tutorial also provides guidance for fine-tuning with a minimal dataset using a CPU. However, it ...
(input,return_tensors='pt').cuda()outputs=model.generate(input_ids,max_length=384,do_sample=True,temperature=1.0,top_k=50,top_p=0.95,repetition_penalty=1.2,num_return_sequences=5)prompts=tokenizer.batch_decode(outputs[:,input_ids.size(1):],skip_special_tokens=True)prompts=[p.strip()for...
promptModel.eval()withtorch.no_grad():forbatchindata_loader:logits=promptModel(batch)preds=torch.argmax(logits,dim=-1)print(tokenizer.decode(batch['input_ids'][0],skip_special_tokens=True),classes[preds]) Copy Making predictions Below snippet shows the output for each of the input example. ...
模型显存占用分成两个部分,一部分是静态显存基本由模型参数量级决定,另一部分是动态显存在向前传播的过程中每个样本的每个神经元都会计算激活值并存储,用于向后传播时的梯度计算,这部分和batchsize以及参数量级相关。以下8bit量化优化的是静态显存,而梯度检查优化的是动态显存。
{ "model_name_or_path": "THUDM/chatglm2-6b", "dataset_name_or_path": "/home/aistudio/mydata", "output_dir": "./checkpoints/chatglm2_lora_ckpts", "per_device_train_batch_size": 4, "gradient_accumulation_steps": 4, "per_device_eval_batch_size": 8, "eval_accumulation_steps"...
For those using benefit subscriptions (such as Visual Studio Enterprise Subscription) or those looking to quickly test the fine-tuning and deployment process, this tutorial also provides guidance for fine-tuning with a minimal dataset using a CPU. However, it is important...