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Remove all traces of low_cpu_mem_usage (#38792)

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* remove it from all py files

* remove it from the doc

* remove it from examples

* style

* remove traces of _fast_init

* Update test_peft_integration.py

* CIs
This commit is contained in:
Cyril Vallez
2025-06-12 16:39:33 +02:00
committed by GitHub
parent 3542e0b844
commit 4b8ec667e9
76 changed files with 100 additions and 598 deletions
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2
docs/source/ar/llm_tutorial_optimization.md
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@@ -231,7 +231,7 @@ flush()
دعنا نرى ما هو استهلاك ذاكرة GPU الذروة الذي يوفره تكميم 4 بت. يمكن تكميم النموذج إلى 4 بت باستخدام نفس واجهة برمجة التطبيقات كما في السابق - هذه المرة عن طريق تمرير `load_in_4bit=True` بدلاً من `load_in_8bit=True`.
```python
model = AutoModelForCausalLM.from_pretrained("bigcode/octocoder", load_in_4bit=True, low_cpu_mem_usage=True, pad_token_id=0)
model = AutoModelForCausalLM.from_pretrained("bigcode/octocoder", load_in_4bit=True, pad_token_id=0)
pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)

6
docs/source/ar/trainer.md
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@@ -459,7 +459,7 @@ args = TrainingArguments(
model_id = "google/gemma-2b"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id، low_cpu_mem_usage=True).to(0)
model = AutoModelForCausalLM.from_pretrained(model_id).to(0)
trainer = trl.SFTTrainer(
model=model،
@@ -503,7 +503,7 @@ args = TrainingArguments(
# تحميل النموذج والمجزىء اللغوي
model_id = "google/gemma-2b"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, low_cpu_mem_usage=True).to(0)
model = AutoModelForCausalLM.from_pretrained(model_id).to(0)
# تهيئة المدرب
trainer = Trainer(
@@ -547,7 +547,7 @@ args = TrainingArguments(
model_id = "google/gemma-2b"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, low_cpu_mem_usage=True).to(0)
model = AutoModelForCausalLM.from_pretrained(model_id).to(0)
trainer = trl.SFTTrainer(
model=model,