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Please provide me with the weight and destination of the package.'}, {'role': 'user', 'content': 'The weight of the package is 10 pounds and the destination is New York.'} ], "params": { "temperature": 0.1, "max_new_tokens": 512, "do_sample": True, "return_full_text": ...
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--resolution 生成图像的分辨率,这里设置为512x512。 --train_batch_size 训练时每个批次的样本数量。 --gradient_accumulation_steps 梯度累积的步数,用于模拟更大批次的训练。 --learning_rate 训练时的学习率。 --report_to 报告训练进度和结果的平台,这里设置为"wandb",即Weights & Biases。 --lr_scheduler ...
constraints. """ body = json.dumps({ "prompt": prompt, "max_tokens": 512, ...
Common Elements: Multi-Domain Agent Framework:To achievea multi-domain agent solution, we have designed two patterns(one per modality)to orchestrate multiple individual agents—one for the hotel domain and another for the flight domain—so they can work seamlessly together. From ...
id: mac host: localhost port: 8080 log: booster.log deadline: 180 pods: gpu: model: hermes prompt: chat sampling: janus threads: 1 gpus: [ 100 ] batch: 512 models: hermes: name: Hermes2 Pro 8B path:~/models/Hermes-2-Pro-Llama-3-8B-Q4_K_M.gguf context: 8K predict: 1K prompts...
-b, --batch-size Mini-batch size (default: 256) --batch-size 512 --compress Set compression and learning rate schedule --compress schedule.yaml --lr, --learning-rate Set initial learning rate --lr 0.001 --deterministic Seed random number generators with fixed values --resume-from Resume...
{"description":"The date of outbound flight","type":"str",},"return_date":{"description":"The date of return flight","type":"str",}}},"booking_hotel":{"name":"booking_hotel","description":"booking hotel","parameters":{"query":{"description":"The nam...