model = AutoModelForCausalLM.from_pretrained(model_id, quantization_config=bnb_config, use_cache = False, device_map=device_map) model.config.pretraining_tp = 1 # Load the tokenizer tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True) tokenizer.pad_token = tokenizer.eos...
side = "right"下面是参数定义,# Activate 4-bit precision base model loadinguse_4bit = True# Compute 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 qu...
model_name, quantization_config=bnb_config, #pass to AutoModelForCausalLM device_map=device_map ) TrainingArguments非常简单。它用于存储SFTTrainer的所有训练参数。SFFTrainer接受不同类型的参数,TrainingArguments帮助我们将所有相关的训练参数组织到一个数据类中保持代码的整洁和有组织。 还有一些很好的工具类可以...
I am trying to use the Mistral 7B parameter model from Hugging face, specifically trying to save it locally and then reload it. I have it under 4 bit quantization and the model size is only 3.5GB. However, upon reloading the model, my WSL RAM usage consumes all the 30GB+ of devoted ...
on testing GaintModels such as GPT3, StableFusion. We offer TensorRT && Int8 quantization on ...
“quantization_bit”: 0: 表示量化的位数。 “rmsnorm”: true: 表示是否使用RMS归一化。 “seq_length”: 32768: 序列长度。 “tie_word_embeddings”: false: 是否绑定输入和输出的词嵌入。 “torch_dtype”: “float16”: 使用的数据类型,这里是半精度浮点数。
Hugging Face开发的PEFT库，可以利用LoRA技术。from peft import LoraConfig, TaskTypelora_config = LoraConfig( r=16, lora_alpha=16, target_modules=["query_key_value"] lora_dropout=0.1, bias="none", task_type=TaskType.CAUSAL_LM, )还可以针对transformer架构中的所有密集层:#...
Hugging Face has made LoRA and quantization accessible across a broad range of transformer models through the PEFT library and its integration with the bitsandbytes library. The create_peft_config() function in the prepared scriptrun_clm.pyillustrates their usage ...
利用Model compression技术(如LoRA、QLoRA和16-bit quantization)来减小内存占用,这些见解来自于Lightning AI和社区实验。 采用硬件加速策略,如利用tinygrad的驱动补丁在NVIDIA 4090 GPUs上开启P2P支持,取得了显著的性能提升。 在框架如LLM.c和torchao中探索高效的张量布局、填充和矩阵运算的 swizzling, 对Kernel 优化进行...
(like HuggingFace) to one that other GGML tools can deal with. I was actually the who added the ability for that tool to output q8_0 — what I was thinking is that for someone who just wants to do stuff like test different quantizations, etc being able to keep a nearly original ...