# Finetune LLM with DeepSpeed
**URL:**
**Author:**  [@shon711](/static-proxy?url=https%3A%2F%2Fdiscuss.huggingface.co%2Fu%2Fshon711)%5C
**Post date:** [February 13, 2023, 8:07am UTC](/static-proxy?url=https%3A%2F%2Fdiscuss.huggingface.co%2Ft%2Ffinetune-llm-with-deepspeed%2F31589%2F1 "2023-02-13T08:07:52Z")
I want to finetune large LM such as opt-13b, opt-30b using Huggingface trainer and its DeepSpeed integration.
I always get out of memory when starting to train.
I have 2 `tesla v100-sxm2-32gb` GPUs.
I ran DeepSpeed memory estimation:
```auto
Estimated memory needed for params, optim states and gradients for a:
HW: Setup with 1 node, 1 GPU per node.
SW: Model with 13000M total params.
per CPU | per GPU | Options
290.57GB | 24.21GB | offload_optimizer=cpu
72.64GB | 242.14GB | offload_optimizer=none
```
My question is:
- Consider the table above - it seems like the per GPU is below 32 so we are good. but the per CPU is very high and I have less than that. It is even possible in that case? it is even possible to finetune 13b model with my resources? There is a solution where also the RAM is limited?
I saw this post in the forum but the guy there have 8 GPUs of 80G each:
> [@Fine-tune OPT 13B: CUDA out of memory error (720gb vram, batch size 1, fp16)!](/static-proxy?url=https%3A%2F%2Fdiscuss.huggingface.co%2Ft%2Ffine-tune-opt-13b-cuda-out-of-memory-error-720gb-vram-batch-size-1-fp16%2F19523%2F6)%3A
>
> Worked like a charm! Thank you @sgugger you rock!!!
this is my training script:
```auto
training_args = TrainingArguments(
output_dir=f'ft_rte_{opt_size}',
logging_dir=f'ft_rte_{opt_size}',
overwrite_output_dir=True,
max_steps=len(few_shot_dataset),
per_device_train_batch_size=1,
per_device_eval_batch_size=1,
evaluation_strategy='steps',
logging_strategy='steps',
eval_steps=1,
logging_steps=1,
logging_first_step=True,
save_strategy='no',
remove_unused_columns=True,
seed=0,
fp16=True,
deepspeed='ds_config.json'
)
model = AutoModelForCausalLM.from_pretrained(opt_size, cache_dir=cache_dir)
data_collator = DataCollatorForLanguageModeling(tokenizer, mlm=False)
trainer = Trainer(
model=model,
tokenizer=tokenizer,
args=training_args,
train_dataset=few_shot_dataset,
eval_dataset=dev_dataset,
data_collator=data_collator,
compute_metrics=eval_rte
)
trainer.train()
```
and this is my deepspeed config:
```auto
{
"fp16": {
"enabled": "auto",
"loss_scale": 0,
"loss_scale_window": 1000,
"initial_scale_power": 16,
"hysteresis": 2,
"min_loss_scale": 1
},
"optimizer": {
"type": "AdamW",
"params": {
"lr": "auto",
"betas": "auto",
"eps": "auto",
"weight_decay": "auto"
}
},
"scheduler": {
"type": "WarmupLR",
"params": {
"warmup_min_lr": "auto",
"warmup_max_lr": "auto",
"warmup_num_steps": "auto"
}
},
"zero_optimization": {
"stage": 2,
"offload_optimizer": {
"device": "cpu",
"pin_memory": true
},
"allgather_partitions": true,
"allgather_bucket_size": 2e8,
"overlap_comm": true,
"reduce_scatter": true,
"reduce_bucket_size": 2e8,
"contiguous_gradients": true
},
"gradient_accumulation_steps": "auto",
"gradient_clipping": "auto",
"train_batch_size": "auto",
"train_micro_batch_size_per_gpu": "auto"
}
```
Thanks in advance,
Shon
---
**Author:**  [@xianf](/static-proxy?url=https%3A%2F%2Fdiscuss.huggingface.co%2Fu%2Fxianf)%5C
**Post date:** [May 15, 2023, 3:13am UTC](/static-proxy?url=https%3A%2F%2Fdiscuss.huggingface.co%2Ft%2Ffinetune-llm-with-deepspeed%2F31589%2F2 "2023-05-15T03:13:40Z")
Hi @shon711 . Did you solve this? I met the same issue. Could you share the ds\_config and how you fix this?
---
**Author:**  [@Cameron-oos](/static-proxy?url=https%3A%2F%2Fdiscuss.huggingface.co%2Fu%2FCameron-oos)%5C
**Post date:** [February 22, 2024, 11:54am UTC](/static-proxy?url=https%3A%2F%2Fdiscuss.huggingface.co%2Ft%2Ffinetune-llm-with-deepspeed%2F31589%2F3 "2024-02-22T11:54:21Z")
Hello! I don’t have a solution for you, but Im running the exact same setup since I don’t have access to A100’s yet. Could you please read this and see if I am on the right track, because if I run into the error you are having I can assist you.
My post: [DeepSpeed integration for HuggingFace Seq2SeqTrainingArguments](/static-proxy?url=https%3A%2F%2Fdiscuss.huggingface.co%2Ft%2Fdeepspeed-integration-for-huggingface-seq2seqtrainingarguments%2F74323)