mx.eval(token) prompt_processing = toc("Prompt processing", start) if len(tokens) >= args.max_tokens: break elif (len(tokens) % args.write_every) == 0: # It is perfectly ok to eval things we have already eval-ed. mx.eval(tokens) s = tokenizer.decode([t.item() for t in toke...
---+ | Processes: | | GPU GI CI PID Type Process name GPU Memory | | ID ID Usage | |===| | 0 N/A N/A 55017 C python 57429MiB | ... | 7 N/A N/A 55017 C python 949MiB | +---
lr_scheduler_type: "constant" # learning rate scheduler num_train_epochs: 3 # number of training epochs per_device_train_batch_size: 1 # batch size per device during training per_device_eval_batch_size: 1 # batch size for evaluation gradient_accumulation_steps: 2 # number of steps before ...
Finetune Llama 3.3, Mistral, Phi-4, Qwen 2.5 & Gemma 2x faster with 80% less memory! ✨ Finetune for Free All notebooks arebeginner friendly! Add your dataset, click "Run All", and you'll get a 2x faster finetuned model which can be exported to GGUF, Ollama, vLLM or uploaded...
profile_with_memory ... False profile_with_stack ... False query_in_block_prob ... 0.1 rampup_batch_size ... None rank ... 0 recompute_granularity ... None recompute_method ...
GPU 显存分为全局内存(Global memory)、本地内存(Local memory)、共享内存(Shared memory,SRAM)、寄存器内存(Register memory)、常量内存(Constant memory)、纹理内存(Texture memory)等六大类。图2.8给出了NVIDIA GPU 内存的整体结构。其中全局内存、本地内存、共享内存和寄存器内存具有读写能力。
#mvbench evaluationCUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 bash scripts/eval/eval_video_qa_mvbench.sh#activitynet-qa evaluation (need to set azure openai key/endpoint/deployname)CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 bash scripts/eval/eval_video_qa_mvbench.sh ...
eval_duration: time in nanoseconds spent generating the response context: an encoding of the conversation used in this response, this can be sent in the next request to keep a conversational memory response: empty if the response was streamed, if not streamed, this will contain the full respo...
# Number of training steps between validations. steps_per_eval: 200 # Load path to resume training with the given adapter weights. resume_adapter_file: null # Save/load path for the trained adapter weights. adapter_path: "adapters"
llama2_13btext_generationWikiText2-PPLeval6.14- llama2_13breading comprehensionSQuAD 1.1-EM/F1eval27.91/44.23- llama2_70b 待补充。 基于Atlas 900 A2 PoDc configtaskDatasetsSeqLengthmetricphasescoreperformance llama2_7btext_generationwiki4096-train-4100 tks/s/p ...