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.github/workflows/check_failed_tests.yml

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# For gated repositories, we still need to agree to share information on the Hub repo. page in order to get access.
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# This token is created under the bot `hf-transformers-bot`.
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HF_HUB_READ_TOKEN: ${{ secrets.HF_HUB_READ_TOKEN }}
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SIGOPT_API_TOKEN: ${{ secrets.SIGOPT_API_TOKEN }}
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TF_FORCE_GPU_ALLOW_GROWTH: true
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CUDA_VISIBLE_DEVICES: 0,1
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.github/workflows/doctest_job.yml

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RUN_SLOW: yes
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OMP_NUM_THREADS: 16
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MKL_NUM_THREADS: 16
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SIGOPT_API_TOKEN: ${{ secrets.SIGOPT_API_TOKEN }}
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TF_FORCE_GPU_ALLOW_GROWTH: true
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jobs:

.github/workflows/model_jobs.yml

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# For gated repositories, we still need to agree to share information on the Hub repo. page in order to get access.
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# This token is created under the bot `hf-transformers-bot`.
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HF_HUB_READ_TOKEN: ${{ secrets.HF_HUB_READ_TOKEN }}
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SIGOPT_API_TOKEN: ${{ secrets.SIGOPT_API_TOKEN }}
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TF_FORCE_GPU_ALLOW_GROWTH: true
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CUDA_VISIBLE_DEVICES: 0,1
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.github/workflows/model_jobs_intel_gaudi.yml

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TRANSFORMERS_IS_CI: yes
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PT_ENABLE_INT64_SUPPORT: 1
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HF_HUB_READ_TOKEN: ${{ secrets.HF_HUB_READ_TOKEN }}
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SIGOPT_API_TOKEN: ${{ secrets.SIGOPT_API_TOKEN }}
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HF_HOME: /mnt/cache/.cache/huggingface
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jobs:

.github/workflows/self-comment-ci.yml

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# For gated repositories, we still need to agree to share information on the Hub repo. page in order to get access.
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# This token is created under the bot `hf-transformers-bot`.
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HF_HUB_READ_TOKEN: ${{ secrets.HF_HUB_READ_TOKEN }}
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SIGOPT_API_TOKEN: ${{ secrets.SIGOPT_API_TOKEN }}
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TF_FORCE_GPU_ALLOW_GROWTH: true
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CUDA_VISIBLE_DEVICES: 0,1
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.github/workflows/self-scheduled-intel-gaudi.yml

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TRANSFORMERS_IS_CI: yes
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PT_ENABLE_INT64_SUPPORT: 1
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HF_HUB_READ_TOKEN: ${{ secrets.HF_HUB_READ_TOKEN }}
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SIGOPT_API_TOKEN: ${{ secrets.SIGOPT_API_TOKEN }}
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HF_HOME: /mnt/cache/.cache/huggingface
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jobs:

.github/workflows/self-scheduled.yml

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# For gated repositories, we still need to agree to share information on the Hub repo. page in order to get access.
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# This token is created under the bot `hf-transformers-bot`.
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HF_HUB_READ_TOKEN: ${{ secrets.HF_HUB_READ_TOKEN }}
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SIGOPT_API_TOKEN: ${{ secrets.SIGOPT_API_TOKEN }}
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TF_FORCE_GPU_ALLOW_GROWTH: true
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CUDA_VISIBLE_DEVICES: 0,1
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NUM_SLICES: 2

.github/workflows/ssh-runner.yml

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OMP_NUM_THREADS: 8
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MKL_NUM_THREADS: 8
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RUN_SLOW: yes # For gated repositories, we still need to agree to share information on the Hub repo. page in order to get access. # This token is created under the bot `hf-transformers-bot`.
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SIGOPT_API_TOKEN: ${{ secrets.SIGOPT_API_TOKEN }}
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TF_FORCE_GPU_ALLOW_GROWTH: true
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CUDA_VISIBLE_DEVICES: 0,1
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ISSUES.md

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cd examples/seq2seq
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torchrun --nproc_per_node=2 ./finetune_trainer.py \
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--model_name_or_path sshleifer/distill-mbart-en-ro-12-4 --data_dir wmt_en_ro \
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--output_dir output_dir --overwrite_output_dir \
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--output_dir output_dir \
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--do_train --n_train 500 --num_train_epochs 1 \
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--per_device_train_batch_size 1 --freeze_embeds \
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--src_lang en_XX --tgt_lang ro_RO --task translation \

docs/source/ar/llm_tutorial.md

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أولاً، تحتاج إلى تحميل النموذج.
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```py
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>>> from transformers import AutoModelForCausalLM
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>>> from transformers import AutoModelForCausalLM, BitsAndBytesConfig
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>>> model = AutoModelForCausalLM.from_pretrained(
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... "mistralai/Mistral-7B-v0.1", device_map="auto", load_in_4bit=True
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... "mistralai/Mistral-7B-v0.1", device_map="auto", quantization_config=BitsAndBytesConfig(load_in_4bit=True)
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... )
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```
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هناك العديد من [استراتيجيات التوليد](generation_strategies)، وفي بعض الأحيان قد لا تكون القيم الافتراضية مناسبة لحالتك الاستخدام. إذا لم تكن الإخراج الخاصة بك متوافقة مع ما تتوقعه، فقد قمنا بإنشاء قائمة بأكثر الأخطاء الشائعة وكيفية تجنبها.
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```py
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>>> from transformers import AutoModelForCausalLM, AutoTokenizer
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>>> from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
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>>> tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-v0.1")
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>>> tokenizer.pad_token = tokenizer.eos_token # Most LLMs don't have a pad token by default
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>>> model = AutoModelForCausalLM.from_pretrained(
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... "mistralai/Mistral-7B-v0.1", device_map="auto", load_in_4bit=True
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... "mistralai/Mistral-7B-v0.1", device_map="auto", quantization_config=BitsAndBytesConfig(load_in_4bit=True)
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... )
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```
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```python
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>>> tokenizer = AutoTokenizer.from_pretrained("HuggingFaceH4/zephyr-7b-alpha")
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>>> model = AutoModelForCausalLM.from_pretrained(
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... "HuggingFaceH4/zephyr-7b-alpha", device_map="auto", load_in_4bit=True
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... "HuggingFaceH4/zephyr-7b-alpha", device_map="auto", quantization_config=BitsAndBytesConfig(load_in_4bit=True)
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... )
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>>> set_seed(0)
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>>> prompt = """How many helicopters can a human eat in one sitting? Reply as a thug."""

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