SFT（Supervised Fine-Tuning，监督微调）是一种微调的类型。如果按照是否有监督，还有无监督微调（Unsupervised Fine-Tuning，在没有明确标签的情况下，对预训练模型进行微调）、自监督微调（Self-Supervised Fine-Tuning，模型通过从输入数据中生成伪标签（如通过数据的部分遮掩、上下文预测等方式），然后利用这些伪标签进...
3、https://ai.meta.com/blog/meta-llama-3-1/-Llama3.1 论文: https://ai.meta.com/blog/meta-llama-3-1/4、https://learn.deeplearning.ai/courses/finetuning-large-language-models/lesson/1/introduction-deeplearn fine-tuning 视频:https://learn.deeplearning.ai/courses/finetuning-large-la...
openai.com/v1/completions \ -H "Content-Type: application/json" \ -H "Authorization: Bearer $OPENAI_API_KEY" \ -d '{ "prompt": "https://t.co/f93xEd2 Excited to share my latest blog post! ->", "max_tokens": 1, "model": "YOUR_FINE_TUNED_MODEL_NAME" }' 这将会返回: { "i...
including the Intel® Data Center GPU Flex 170 and Intel® Arc™ series graphics. Specifically, using the Intel® Data Center GPU Flex 170 hardware as an example, you can complete the fine-tuning of the Llama 2 7B model in approximately 2 hours on a single server equipped...
model = AutoModelForCausalLM.from_pretrained(model_name) 1. 2. 3. 2.4 设置基础评估函数 def is_exact_match(a, b): return a.strip() == b.strip() model.eval() 1. 2. 3. 4. 输出如下: GPTNeoXForCausalLM( (gpt_neox): GPTNeoXModel( ...
SageMaker Training. We demonstrate this through a step-by-step implementation of model fine-tuning, inference, quantization, and evaluation. We perform the steps on aMeta Llama 3.18B model utilizing the LoRA fine-tuning strategy on a single p4d.24xlarge worker node (providing ...
Prompt Tuning [13] Lora / QLora [14] 根据实际经验,这里推荐采用 Lora 或 QLora。简单介绍一下 QLoRA,重点改进是将模型采用 4bit 量化后加载,训练时把数值反量化到 bf16 后进行训练,利用 LoRA 可以锁定原模型参数不参与训练,只训练少量 LoRA 参数的特性使得训练所需的显存大大减少。例如 33B 的 LLaMA 模...
LLaMA 全称 Large Language Model Meta AI,是一个开源的优秀预训练模型,接近 GPT-3 的水准,同样只有 Transformer 的解码器。Vicuna 是基于 LLaMA 微调后的对话模型。 多模态融合还是得摆上这张图: 例如ViLBERT 属于 (c),ViLT 属于 (d) 等。 1.3 Related Work ...
tuning, the Llama 3 8b model should be able to generate insightful financial summaries for its application users. But fine-tuning an LLM just once isn’t enough. You need to regularly tune the LLM to keep it up to date with the most rec...
from transformers import AutoModelForCausalLM from transformers import TrainingArguments from transformers import AutoModelForCausalLM from llama import BasicModelRunner logger = logging.getLogger(__name__) global_config = None 1. 2. 3. 4.