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A=k=>X(`${n}.${k}`,!!K[k]),G=()=>X(`${n}.${V}`,!!C),q=()=>a(13,j=!1);function E(k){Gn[k?"unshift":"push"](()=>{B=k,a(12,B)})}return o.$$set=k=>{"anchor"in k&&a(9,n=k.anchor),"name"in k&&a(10,s=k.name),"parameters"in k&&a(1,v=k.parameters),"parametersDescription"in k&&a(11,u=k.parametersDescription),"parameterGroups"in k&&a(2,i=k.parameterGroups),"returnDescription"in k&&a(3,C=k.returnDescription),"returnType"in k&&a(4,M=k.returnType),"isYield"in k&&a(20,_=k.isYield),"raiseDescription"in k&&a(5,r=k.raiseDescription),"raiseType"in k&&a(6,h=k.raiseType),"source"in k&&a(7,b=k.source),"hashlink"in k&&a(0,f=k.hashlink),"isGetSetDescriptor"in k&&a(8,T=k.isGetSetDescriptor)},[f,v,i,C,M,r,h,b,T,n,s,u,B,j,K,O,V,X,R,I,_,A,G,q,E]}class be extends Tl{constructor(t){super(),Cl(this,t,es,On,Ml,{anchor:9,name:10,parameters:1,parametersDescription:11,parameterGroups:2,returnDescription:3,returnType:4,isYield:20,raiseDescription:5,raiseType:6,source:7,hashlink:0,isGetSetDescriptor:8},null,[-1,-1])}}function ts(o){let t,a="يمكنك تحميل محول PEFT باستخدام فئة <code>AutoModelFor</code> أو فئة النموذج الأساسي مثل <code>OPTForCausalLM</code> أو <code>LlamaForCausalLM</code>.";return{c(){t=w("p"),t.innerHTML=a},l(n){t=y(n,"P",{"data-svelte-h":!0}),$(t)!=="svelte-pypmay"&&(t.innerHTML=a)},m(n,s){p(n,t,s)},p:Ga,d(n){n&&l(t)}}}function as(o){let t,a='إذا لم تكن معتادًا على ضبط نموذج دقيق باستخدام <a href="training"><code>Trainer</code>، فراجع البرنامج التعليمي</a> لضبط نموذج مُدرب مسبقًا.';return{c(){t=w("p"),t.innerHTML=a},l(n){t=y(n,"P",{"data-svelte-h":!0}),$(t)!=="svelte-1e6o6ig"&&(t.innerHTML=a)},m(n,s){p(n,t,s)},p:Ga,d(n){n&&l(t)}}}function ls(o){let t,a,n,s,v,u,i,C,M,_='تقنية “التدريب الدقيق ذو الكفاءة البارامتيرية” (PEFT)](<a href="https://huggingface.co/blog/peft" rel="nofollow">https://huggingface.co/blog/peft</a>) تقوم بتجميد معلمات النموذج المُدرب مسبقًا أثناء الضبط الدقيق وتضيف عدد صغير من المعلمات القابلة للتدريب (المحولات) فوقه. يتم تدريب المحوّلات لتعلم معلومات خاصة بالمهام. وقد ثبت أن هذا النهج فعال للغاية من حيث استخدام الذاكرة مع انخفاض استخدام الكمبيوتر أثناء إنتاج نتائج قمماثلة للنموذج مضبوط دقيقًا بالكامل.',r,h,b="عادة ما تكون المحولات المدربة باستخدام PEFT أصغر بمقدار كبير من حيث الحجم من النموذج الكامل، مما يجعل من السهل مشاركتها وتخزينها وتحميلها.",f,T,B='<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/peft/PEFT-hub-screenshot.png"/> <figcaption class="text-center">تبلغ أوزان المحول لطراز OPTForCausalLM المخزن على Hub حوالي 6 ميجابايت مقارنة بالحجم الكامل لأوزان النموذج، والتي يمكن أن تكون حوالي 700 ميجابايت.</figcaption>',N,j,K='إذا كنت مهتمًا بمعرفة المزيد عن مكتبة 🤗 PEFT، فراجع <a href="https://huggingface.co/docs/peft/index" rel="nofollow">الوثائق</a>.',O,V,X,R,I="ابدأ بتثبيت 🤗 PEFT:",A,G,q,E,k="إذا كنت تريد تجربة الميزات الجديدة تمامًا، فقد تكون مهتمًا بتثبيت المكتبة من المصدر:",z,D,S,ie,zt,ye,kl="يدعم 🤗 Transformers بشكلٍ أصلي بعض طرق PEFT، مما يعني أنه يمكنك تحميل أوزان المحول المخزنة محليًا أو على Hub وتشغيلها أو تدريبها ببضع سطور من التعليمات البرمجية. الطرق المدعومة هي:",St,Me,Ul='<li><a href="https://huggingface.co/docs/peft/conceptual_guides/lora" rel="nofollow">محولات الرتبة المنخفضة</a></li> <li><a href="https://huggingface.co/docs/peft/conceptual_guides/ia3" rel="nofollow">IA3</a></li> <li><a href="https://arxiv.org/abs/2303.10512" rel="nofollow">AdaLoRA</a></li>',Dt,Te,$l='إذا كنت تريد استخدام طرق PEFT الأخرى، مثل تعلم المحث أو ضبط المحث، أو حول مكتبة 🤗 PEFT بشكل عام، يرجى الرجوع إلى <a href="https://huggingface.co/docs/peft/index" rel="nofollow">الوثائق</a>.',Yt,Ce,Kt,Je,xl="لتحميل نموذج محول PEFT واستخدامه من 🤗 Transformers، تأكد من أن مستودع Hub أو الدليل المحلي يحتوي على ملف <code>adapter_config.json</code> وأوزان المحوّل، كما هو موضح في صورة المثال أعلاه. بعد ذلك، يمكنك تحميل نموذج محوّل PEFT باستخدام فئة <code>AutoModelFor</code>. على سبيل المثال، لتحميل نموذج محول PEFT للنمذجة اللغوية السببية:",Ot,ke,El="<li>حدد معرف النموذج لPEFT</li> <li>مرره إلى فئة <code>AutoModelForCausalLM</code></li>",ea,Ue,ta,me,aa,$e,Fl="يمكنك أيضًا تحميل محول PEFT عن طريق استدعاء طريقة <code>load_adapter</code>:",la,xe,na,Ee,Pl='راجع قسم <a href="#transformers.integrations.PeftAdapterMixin">وثائق API</a> أدناه لمزيد من التفاصيل.',sa,Fe,ra,Pe,Zl='راجع قسم <a href="#transformers.integrations.PeftAdapterMixin">وثائق API</a> أدناه لمزيد من التفاصيل.',oa,Ze,ia,Le,Ll='يدعم تكامل <code>bitsandbytes</code> أنواع بيانات الدقة 8 بت و4 بت، والتي تكون مفيدة لتحميل النماذج الكبيرة لأنها توفر مساحة في الذاكرة (راجع دليل تكامل <code>bitsandbytes</code> <a href="./quantization#bitsandbytes-integration">guide</a> لمعرفة المزيد). أضف المعلمات<code>load_in_8bit</code> أو <code>load_in_4bit</code> إلى <code>from_pretrained()</code> وقم بتعيين <code>device_map="auto"</code> لتوزيع النموذج بشكل فعال على الأجهزة لديك:',da,Qe,fa,Be,pa,He,Ql="يمكنك استخدام الدالة <code>~peft.PeftModel.add_adapter</code> لإضافة محوّل جديد إلى نموذج يحتوي بالفعل على محوّل آخر طالما أن المحول الجديد مطابقًا للنوع الحالي. على سبيل المثال، إذا كان لديك محول LoRA موجود مرتبط بنموذج:",ma,We,ca,je,Bl="لإضافة محول جديد:",ua,Ve,ha,Ae,Hl="الآن يمكنك استخدام <code>~peft.PeftModel.set_adapter</code> لتعيين المحول الذي سيتم استخدامه:",ga,Re,ba,Ie,_a,Ge,Wl="بمجرد إضافة محول إلى نموذج، يمكنك تمكين أو تعطيل وحدة المحول. لتمكين وحدة المحول:",va,Ne,wa,Xe,jl="لإيقاف تشغيل وحدة المحول:",ya,qe,Ma,ze,Ta,Se,Vl="يدعم محول PEFT فئة <code>Trainer</code> بحيث يمكنك تدريب محول لحالتك الاستخدام المحددة. فهو يتطلب فقط إضافة بضع سطور أخرى من التعليمات البرمجية. على سبيل المثال، لتدريب محول LoRA:",Ca,ce,Ja,De,Al="<li>حدد تكوين المحول باستخدام نوع المهمة والمعاملات الزائدة (راجع <code>~peft.LoraConfig</code> لمزيد من التفاصيل حول وظيفة هذه المعلمات).</li>",ka,Ye,Ua,ue,Rl="<li>أضف المحول إلى النموذج.</li>",$a,Ke,xa,he,Il="<li>الآن يمكنك تمرير النموذج إلى <code>Trainer</code>!</li>",Ea,Oe,Fa,et,Gl="لحفظ محول المدرب وتحميله مرة أخرى:",Pa,tt,Za,at,La,lt,Qa,nt,Ba,st,Nl="يمكنك أيضًا إجراء تدريب دقيق لمحوّلات قابلة للتدريب إضافية فوق نموذج يحتوي بالفعل على محوّلات عن طريق تمرير معلم <code>modules_to_save</code> في تكوين PEFT الخاص بك. على سبيل المثال، إذا كنت تريد أيضًا ضبط دقيق لرأس النموذج اللغوي<code>lm_head</code> فوق نموذج بمحوّل LoRA:",Ha,rt,Wa,ot,ja,H,it,za,vt,Xl=`A class containing all functions for loading and using adapters weights that are supported in PEFT library. For | |
| more details about adapters and injecting them on a transformer-based model, check out the documentation of PEFT | |
| library: <a href="https://huggingface.co/docs/peft/index" rel="nofollow">https://huggingface.co/docs/peft/index</a>`,Sa,wt,ql=`Currently supported PEFT methods are all non-prefix tuning methods. Below is the list of supported PEFT methods | |
| that anyone can load, train and run with this mixin class:`,Da,yt,zl='<li>Low Rank Adapters (LoRA): <a href="https://huggingface.co/docs/peft/conceptual_guides/lora" rel="nofollow">https://huggingface.co/docs/peft/conceptual_guides/lora</a></li> <li>IA3: <a href="https://huggingface.co/docs/peft/conceptual_guides/ia3" rel="nofollow">https://huggingface.co/docs/peft/conceptual_guides/ia3</a></li> <li>AdaLora: <a href="https://arxiv.org/abs/2303.10512" rel="nofollow">https://arxiv.org/abs/2303.10512</a></li>',Ya,Mt,Sl=`Other PEFT models such as prompt tuning, prompt learning are out of scope as these adapters are not “injectable” | |
| into a torch module. For using these methods, please refer to the usage guide of PEFT library.`,Ka,Tt,Dl="With this mixin, if the correct PEFT version is installed, it is possible to:",Oa,Ct,Yl="<li>Load an adapter stored on a local path or in a remote Hub repository, and inject it in the model</li> <li>Attach new adapters in the model and train them with Trainer or by your own.</li> <li>Attach multiple adapters and iteratively activate / deactivate them</li> <li>Activate / deactivate all adapters from the model.</li> <li>Get the <code>state_dict</code> of the active adapter.</li>",el,ae,dt,tl,Jt,Kl=`Load adapter weights from file or remote Hub folder. If you are not familiar with adapters and PEFT methods, we | |
| invite you to read more about them on PEFT official documentation: <a href="https://huggingface.co/docs/peft" rel="nofollow">https://huggingface.co/docs/peft</a>`,al,kt,Ol="Requires peft as a backend to load the adapter weights.",ll,le,ft,nl,Ut,en=`If you are not familiar with adapters and PEFT methods, we invite you to read more about them on the PEFT | |
| official documentation: <a href="https://huggingface.co/docs/peft" rel="nofollow">https://huggingface.co/docs/peft</a>`,sl,$t,tn=`Adds a fresh new adapter to the current model for training purpose. If no adapter name is passed, a default | |
| name is assigned to the adapter to follow the convention of PEFT library (in PEFT we use “default” as the | |
| default adapter name).`,rl,ne,pt,ol,xt,an=`If you are not familiar with adapters and PEFT methods, we invite you to read more about them on the PEFT | |
| official documentation: <a href="https://huggingface.co/docs/peft" rel="nofollow">https://huggingface.co/docs/peft</a>`,il,Et,ln="Sets a specific adapter by forcing the model to use a that adapter and disable the other adapters.",dl,se,mt,fl,Ft,nn=`If you are not familiar with adapters and PEFT methods, we invite you to read more about them on the PEFT | |
| official documentation: <a href="https://huggingface.co/docs/peft" rel="nofollow">https://huggingface.co/docs/peft</a>`,pl,Pt,sn="Disable all adapters that are attached to the model. This leads to inferring with the base model only.",ml,re,ct,cl,Zt,rn=`If you are not familiar with adapters and PEFT methods, we invite you to read more about them on the PEFT | |
| official documentation: <a href="https://huggingface.co/docs/peft" rel="nofollow">https://huggingface.co/docs/peft</a>`,ul,Lt,on="Enable adapters that are attached to the model. The model will use <code>self.active_adapter()</code>",hl,ee,ut,gl,Qt,dn=`If you are not familiar with adapters and PEFT methods, we invite you to read more about them on the PEFT | |
| official documentation: <a href="https://huggingface.co/docs/peft" rel="nofollow">https://huggingface.co/docs/peft</a>`,bl,Bt,fn=`Gets the current active adapters of the model. In case of multi-adapter inference (combining multiple adapters | |
| for inference) returns the list of all active adapters so that users can deal with them accordingly.`,_l,Ht,pn=`For previous PEFT versions (that does not support multi-adapter inference), <code>module.active_adapter</code> will return | |
| a single string.`,vl,oe,ht,wl,Wt,mn=`If you are not familiar with adapters and PEFT methods, we invite you to read more about them on the PEFT | |
| official documentation: <a href="https://huggingface.co/docs/peft" rel="nofollow">https://huggingface.co/docs/peft</a>`,yl,jt,cn=`Gets the adapter state dict that should only contain the weights tensors of the specified adapter_name adapter. | |
| If no adapter_name is passed, the active adapter is used.`,Va,gt,Aa,Nt,Ra;return v=new te({props:{title:"تحميل المحوّلات باستخدام 🤗 PEFT",local:"تحميل-المحولات-باستخدام--peft",headingTag:"h1"}}),i=new zn({props:{classNames:"absolute z-10 right-0 top-0",options:[{label:"Mixed",value:"https://colab.research.google.com/github/huggingface/notebooks/blob/main/transformers_doc/ar/peft.ipynb"},{label:"PyTorch",value:"https://colab.research.google.com/github/huggingface/notebooks/blob/main/transformers_doc/ar/pytorch/peft.ipynb"},{label:"TensorFlow",value:"https://colab.research.google.com/github/huggingface/notebooks/blob/main/transformers_doc/ar/tensorflow/peft.ipynb"},{label:"Mixed",value:"https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/transformers_doc/ar/peft.ipynb"},{label:"PyTorch",value:"https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/transformers_doc/ar/pytorch/peft.ipynb"},{label:"TensorFlow",value:"https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/transformers_doc/ar/tensorflow/peft.ipynb"}]}}),V=new te({props:{title:"الإعداد",local:"الإعداد",headingTag:"h2"}}),G=new Y({props:{code:"cGlwJTIwaW5zdGFsbCUyMHBlZnQ=",highlighted:"pip install peft",wrap:!1}}),D=new Y({props:{code:"cGlwJTIwaW5zdGFsbCUyMGdpdCUyQmh0dHBzJTNBJTJGJTJGZ2l0aHViLmNvbSUyRmh1Z2dpbmdmYWNlJTJGcGVmdC5naXQ=",highlighted:"pip install git+https://github.com/huggingface/peft.git",wrap:!1}}),ie=new te({props:{title:"نماذج PEFT المدعومة",local:"نماذج-peft-المدعومة",headingTag:"h2"}}),Ce=new te({props:{title:"تحميل محول PEFT",local:"تحميل-محول-peft",headingTag:"h2"}}),Ue=new Y({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Nb2RlbEZvckNhdXNhbExNJTJDJTIwQXV0b1Rva2VuaXplciUwQSUwQXBlZnRfbW9kZWxfaWQlMjAlM0QlMjAlMjJ5YmVsa2FkYSUyRm9wdC0zNTBtLWxvcmElMjIlMEFtb2RlbCUyMCUzRCUyMEF1dG9Nb2RlbEZvckNhdXNhbExNLmZyb21fcHJldHJhaW5lZChwZWZ0X21vZGVsX2lkKQ==",highlighted:`<span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoModelForCausalLM, AutoTokenizer | |
| peft_model_id = <span class="hljs-string">"ybelkada/opt-350m-lora"</span> | |
| model = AutoModelForCausalLM.from_pretrained(peft_model_id)`,wrap:!1}}),me=new un({props:{$$slots:{default:[ts]},$$scope:{ctx:o}}}),xe=new Y({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Nb2RlbEZvckNhdXNhbExNJTJDJTIwQXV0b1Rva2VuaXplciUwQSUwQW1vZGVsX2lkJTIwJTNEJTIwJTIyZmFjZWJvb2slMkZvcHQtMzUwbSUyMiUwQXBlZnRfbW9kZWxfaWQlMjAlM0QlMjAlMjJ5YmVsa2FkYSUyRm9wdC0zNTBtLWxvcmElMjIlMEElMEFtb2RlbCUyMCUzRCUyMEF1dG9Nb2RlbEZvckNhdXNhbExNLmZyb21fcHJldHJhaW5lZChtb2RlbF9pZCklMEFtb2RlbC5sb2FkX2FkYXB0ZXIocGVmdF9tb2RlbF9pZCk=",highlighted:`<span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoModelForCausalLM, AutoTokenizer | |
| model_id = <span class="hljs-string">"facebook/opt-350m"</span> | |
| peft_model_id = <span class="hljs-string">"ybelkada/opt-350m-lora"</span> | |
| model = AutoModelForCausalLM.from_pretrained(model_id) | |
| model.load_adapter(peft_model_id)`,wrap:!1}}),Fe=new te({props:{title:"التحميل في 8 بت أو 4 بت",local:"التحميل-في-8-بت-أو-4-بت",headingTag:"h2"}}),Ze=new te({props:{title:"التحميل في 8 بت أو 4 بت",local:"التحميل-في-8-بت-أو-4-بت",headingTag:"h2"}}),Qe=new Y({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Nb2RlbEZvckNhdXNhbExNJTJDJTIwQXV0b1Rva2VuaXplciUyQyUyMEJpdHNBbmRCeXRlc0NvbmZpZyUwQSUwQXBlZnRfbW9kZWxfaWQlMjAlM0QlMjAlMjJ5YmVsa2FkYSUyRm9wdC0zNTBtLWxvcmElMjIlMEFtb2RlbCUyMCUzRCUyMEF1dG9Nb2RlbEZvckNhdXNhbExNLmZyb21fcHJldHJhaW5lZChwZWZ0X21vZGVsX2lkJTJDJTIwcXVhbnRpemF0aW9uX2NvbmZpZyUzREJpdHNBbmRCeXRlc0NvbmZpZyhsb2FkX2luXzhiaXQlM0RUcnVlKSk=",highlighted:`<span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig | |
| peft_model_id = <span class="hljs-string">"ybelkada/opt-350m-lora"</span> | |
| model = AutoModelForCausalLM.from_pretrained(peft_model_id, quantization_config=BitsAndBytesConfig(load_in_8bit=<span class="hljs-literal">True</span>))`,wrap:!1}}),Be=new te({props:{title:"إضافة محول جديد",local:"إضافة-محول-جديد",headingTag:"h2"}}),We=new Y({props:{code:"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",highlighted:`<span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoModelForCausalLM, OPTForCausalLM, AutoTokenizer | |
| <span class="hljs-keyword">from</span> peft <span class="hljs-keyword">import</span> LoraConfig | |
| model_id = <span class="hljs-string">"facebook/opt-350m"</span> | |
| model = AutoModelForCausalLM.from_pretrained(model_id) | |
| lora_config = LoraConfig( | |
| target_modules=[<span class="hljs-string">"q_proj"</span>, <span class="hljs-string">"k_proj"</span>], | |
| init_lora_weights=<span class="hljs-literal">False</span> | |
| ) | |
| model.add_adapter(lora_config, adapter_name=<span class="hljs-string">"adapter_1"</span>)`,wrap:!1}}),Ve=new Y({props:{code:"JTIzJTIwJUQ5JTgyJUQ5JTg1JTIwJUQ4JUE4JUQ4JUFBJUQ4JUI5JUQ5JTg0JUQ5JThBJUQ5JTgyJTIwJUQ5JTg1JUQ4JUFEJUQ5JTg4JUQ5JTg0JTIwJUQ4JUFDJUQ4JUFGJUQ5JThBJUQ4JUFGJTIwJUQ4JUE4JUQ5JTg2JUQ5JTgxJUQ4JUIzJTIwJUQ4JUE3JUQ5JTg0JUQ4JUFBJUQ5JTgzJUQ5JTg4JUQ5JThBJUQ5JTg2JTBBbW9kZWwuYWRkX2FkYXB0ZXIobG9yYV9jb25maWclMkMlMjBhZGFwdGVyX25hbWUlM0QlMjJhZGFwdGVyXzIlMjIp",highlighted:`<span class="hljs-comment"># قم بتعليق محول جديد بنفس التكوين</span> | |
| model.add_adapter(lora_config, adapter_name=<span class="hljs-string">"adapter_2"</span>)`,wrap:!1}}),Re=new Y({props:{code:"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",highlighted:`<span class="hljs-comment"># استخدم adapter_1</span> | |
| model.set_adapter(<span class="hljs-string">"adapter_1"</span>) | |
| output = model.generate(**inputs) | |
| <span class="hljs-built_in">print</span>(tokenizer.decode(output_disabled[<span class="hljs-number">0</span>], skip_special_tokens=<span class="hljs-literal">True</span>)) | |
| <span class="hljs-comment"># استخدم adapter_2</span> | |
| model.set_adapter(<span class="hljs-string">"adapter_2"</span>) | |
| output_enabled = model.generate(**inputs) | |
| <span class="hljs-built_in">print</span>(tokenizer.decode(output_enabled[<span class="hljs-number">0</span>], skip_special_tokens=<span class="hljs-literal">True</span>))`,wrap:!1}}),Ie=new te({props:{title:"تمكين وتعطيل المحولات",local:"تمكين-وتعطيل-المحولات",headingTag:"h2"}}),Ne=new Y({props:{code:"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",highlighted:`<span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoModelForCausalLM, OPTForCausalLM, AutoTokenizer | |
| <span class="hljs-keyword">from</span> peft <span class="hljs-keyword">import</span> PeftConfig | |
| model_id = <span class="hljs-string">"facebook/opt-350m"</span> | |
| adapter_model_id = <span class="hljs-string">"ybelkada/opt-350m-lora"</span> | |
| tokenizer = AutoTokenizer.from_pretrained(model_id) | |
| text = <span class="hljs-string">"Hello"</span> | |
| inputs = tokenizer(text, return_tensors=<span class="hljs-string">"pt"</span>) | |
| model = AutoModelForCausalLM.from_pretrained(model_id) | |
| peft_config = PeftConfig.from_pretrained(adapter_model_id) | |
| <span class="hljs-comment"># لبدء تشغيله بأوزان عشوائية</span> | |
| peft_config.init_lora_weights = <span class="hljs-literal">False</span> | |
| model.add_adapter(peft_config) | |
| model.enable_adapters() | |
| output = model.generate(**inputs)`,wrap:!1}}),qe=new Y({props:{code:"bW9kZWwuZGlzYWJsZV9hZGFwdGVycygpJTBBb3V0cHV0JTIwJTNEJTIwbW9kZWwuZ2VuZXJhdGUoKippbnB1dHMp",highlighted:`model.disable_adapters() | |
| output = model.generate(**inputs)`,wrap:!1}}),ze=new te({props:{title:"تدريب محول PEFT",local:"تدريب-محول-peft",headingTag:"h2"}}),ce=new un({props:{$$slots:{default:[as]},$$scope:{ctx:o}}}),Ye=new Y({props:{code:"ZnJvbSUyMHBlZnQlMjBpbXBvcnQlMjBMb3JhQ29uZmlnJTBBJTBBcGVmdF9jb25maWclMjAlM0QlMjBMb3JhQ29uZmlnKCUwQSUyMCUyMCUyMCUyMGxvcmFfYWxwaGElM0QxNiUyQyUwQSUyMCUyMCUyMCUyMGxvcmFfZHJvcG91dCUzRDAuMSUyQyUwQSUyMCUyMCUyMCUyMHIlM0Q2NCUyQyUwQSUyMCUyMCUyMCUyMGJpYXMlM0QlMjJub25lJTIyJTJDJTBBJTIwJTIwJTIwJTIwdGFza190eXBlJTNEJTIyQ0FVU0FMX0xNJTIyJUQ4JThDJTBBKQ==",highlighted:`<span class="hljs-keyword">from</span> peft <span class="hljs-keyword">import</span> LoraConfig | |
| peft_config = LoraConfig( | |
| lora_alpha=<span class="hljs-number">16</span>, | |
| lora_dropout=<span class="hljs-number">0.1</span>, | |
| r=<span class="hljs-number">64</span>, | |
| bias=<span class="hljs-string">"none"</span>, | |
| task_type=<span class="hljs-string">"CAUSAL_LM"</span>، | |
| )`,wrap:!1}}),Ke=new Y({props:{code:"bW9kZWwuYWRkX2FkYXB0ZXIocGVmdF9jb25maWcp",highlighted:"model.add_adapter(peft_config)",wrap:!1}}),Oe=new Y({props:{code:"dHJhaW5lciUyMCUzRCUyMFRyYWluZXIobW9kZWwlM0Rtb2RlbCUyQyUyMC4uLiklMEF0cmFpbmVyLnRyYWluKCk=",highlighted:`trainer = Trainer(model=model, ...) | |
| trainer.train()`,wrap:!1}}),tt=new Y({props:{code:"bW9kZWwuc2F2ZV9wcmV0cmFpbmVkKHNhdmVfZGlyKSUwQW1vZGVsJTIwJTNEJTIwQXV0b01vZGVsRm9yQ2F1c2FsTE0uZnJvbV9wcmV0cmFpbmVkKHNhdmVfZGlyKQ==",highlighted:`model.save_pretrained(save_dir) | |
| model = AutoModelForCausalLM.from_pretrained(save_dir)`,wrap:!1}}),at=new te({props:{title:"إضافة طبقات قابلة للتدريب إضافية إلى محول PEFT",local:"إضافة-طبقات-قابلة-للتدريب-إضافية-إلى-محول-peft",headingTag:"h2"}}),lt=new Y({props:{code:"bW9kZWwuc2F2ZV9wcmV0cmFpbmVkKHNhdmVfZGlyKSUwQW1vZGVsJTIwJTNEJTIwQXV0b01vZGVsRm9yQ2F1c2FsTE0uZnJvbV9wcmV0cmFpbmVkKHNhdmVfZGlyKQ==",highlighted:`model.save_pretrained(save_dir) | |
| model = AutoModelForCausalLM.from_pretrained(save_dir)`,wrap:!1}}),nt=new te({props:{title:"إضافة طبقات قابلة للتدريب إضافية إلى محول PEFT",local:"إضافة-طبقات-قابلة-للتدريب-إضافية-إلى-محول-peft",headingTag:"h2"}}),rt=new Y({props:{code:"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",highlighted:`<span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoModelForCausalLM, OPTForCausalLM, AutoTokenizer | |
| <span class="hljs-keyword">from</span> peft <span class="hljs-keyword">import</span> LoraConfig | |
| model_id = <span class="hljs-string">"facebook/opt-350m"</span> | |
| model = AutoModelForCausalLM.from_pretrained(model_id) | |
| lora_config = LoraConfig( | |
| target_modules=[<span class="hljs-string">"q_proj"</span>, <span class="hljs-string">"k_proj"</span>], | |
| modules_to_save=[<span class="hljs-string">"lm_head"</span>]، | |
| ) | |
| model.add_adapter(lora_config)`,wrap:!1}}),ot=new te({props:{title:"وثائق API",local:"transformers.integrations.PeftAdapterMixin",headingTag:"h2"}}),it=new be({props:{name:"class transformers.integrations.PeftAdapterMixin",anchor:"transformers.integrations.PeftAdapterMixin",parameters:[],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/integrations/peft.py#L42"}}),dt=new be({props:{name:"load_adapter",anchor:"transformers.integrations.PeftAdapterMixin.load_adapter",parameters:[{name:"peft_model_id",val:": Optional = None"},{name:"adapter_name",val:": Optional = None"},{name:"revision",val:": Optional = None"},{name:"token",val:": Optional = None"},{name:"device_map",val:": Optional = 'auto'"},{name:"max_memory",val:": Optional = None"},{name:"offload_folder",val:": Optional = None"},{name:"offload_index",val:": Optional = None"},{name:"peft_config",val:": Dict = None"},{name:"adapter_state_dict",val:": Optional = None"},{name:"adapter_kwargs",val:": Optional = None"}],parametersDescription:[{anchor:"transformers.integrations.PeftAdapterMixin.load_adapter.peft_model_id",description:`<strong>peft_model_id</strong> (<code>str</code>, <em>optional</em>) — | |
| The identifier of the model to look for on the Hub, or a local path to the saved adapter config file | |
| and adapter weights.`,name:"peft_model_id"},{anchor:"transformers.integrations.PeftAdapterMixin.load_adapter.adapter_name",description:`<strong>adapter_name</strong> (<code>str</code>, <em>optional</em>) — | |
| The adapter name to use. If not set, will use the default adapter.`,name:"adapter_name"},{anchor:"transformers.integrations.PeftAdapterMixin.load_adapter.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>"main"</code>) — | |
| The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a | |
| git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any | |
| identifier allowed by git.</p> | |
| <div class="course-tip bg-gradient-to-br dark:bg-gradient-to-r before:border-green-500 dark:before:border-green-800 from-green-50 dark:from-gray-900 to-white dark:to-gray-950 border border-green-50 text-green-700 dark:text-gray-400"> | |
| <p>To test a pull request you made on the Hub, you can pass \`revision=“refs/pr/<pr_number>“.</pr_number></p> | |
| </div>`,name:"revision"},{anchor:"transformers.integrations.PeftAdapterMixin.load_adapter.token",description:`<strong>token</strong> (<code>str</code>, <code>optional</code>) — | |
| Whether to use authentication token to load the remote folder. Userful to load private repositories | |
| that are on HuggingFace Hub. You might need to call <code>huggingface-cli login</code> and paste your tokens to | |
| cache it.`,name:"token"},{anchor:"transformers.integrations.PeftAdapterMixin.load_adapter.device_map",description:`<strong>device_map</strong> (<code>str</code> or <code>Dict[str, Union[int, str, torch.device]]</code> or <code>int</code> or <code>torch.device</code>, <em>optional</em>) — | |
| A map that specifies where each submodule should go. It doesn’t need to be refined to each | |
| parameter/buffer name, once a given module name is inside, every submodule of it will be sent to the | |
| same device. If we only pass the device (<em>e.g.</em>, <code>"cpu"</code>, <code>"cuda:1"</code>, <code>"mps"</code>, or a GPU ordinal rank | |
| like <code>1</code>) on which the model will be allocated, the device map will map the entire model to this | |
| device. Passing <code>device_map = 0</code> means put the whole model on GPU 0.</p> | |
| <p>To have Accelerate compute the most optimized <code>device_map</code> automatically, set <code>device_map="auto"</code>. For | |
| more information about each option see <a href="https://hf.co/docs/accelerate/main/en/usage_guides/big_modeling#designing-a-device-map" rel="nofollow">designing a device | |
| map</a>.`,name:"device_map"},{anchor:"transformers.integrations.PeftAdapterMixin.load_adapter.max_memory",description:`<strong>max_memory</strong> (<code>Dict</code>, <em>optional</em>) — | |
| A dictionary device identifier to maximum memory. Will default to the maximum memory available for each | |
| GPU and the available CPU RAM if unset.`,name:"max_memory"},{anchor:"transformers.integrations.PeftAdapterMixin.load_adapter.offload_folder",description:`<strong>offload_folder</strong> (<code>str</code> or <code>os.PathLike</code>, <code>optional</code>) — | |
| If the <code>device_map</code> contains any value <code>"disk"</code>, the folder where we will offload weights.`,name:"offload_folder"},{anchor:"transformers.integrations.PeftAdapterMixin.load_adapter.offload_index",description:`<strong>offload_index</strong> (<code>int</code>, <code>optional</code>) — | |
| <code>offload_index</code> argument to be passed to <code>accelerate.dispatch_model</code> method.`,name:"offload_index"},{anchor:"transformers.integrations.PeftAdapterMixin.load_adapter.peft_config",description:`<strong>peft_config</strong> (<code>Dict[str, Any]</code>, <em>optional</em>) — | |
| The configuration of the adapter to add, supported adapters are non-prefix tuning and adaption prompts | |
| methods. This argument is used in case users directly pass PEFT state dicts`,name:"peft_config"},{anchor:"transformers.integrations.PeftAdapterMixin.load_adapter.adapter_state_dict",description:`<strong>adapter_state_dict</strong> (<code>Dict[str, torch.Tensor]</code>, <em>optional</em>) — | |
| The state dict of the adapter to load. This argument is used in case users directly pass PEFT state | |
| dicts`,name:"adapter_state_dict"},{anchor:"transformers.integrations.PeftAdapterMixin.load_adapter.adapter_kwargs",description:`<strong>adapter_kwargs</strong> (<code>Dict[str, Any]</code>, <em>optional</em>) — | |
| Additional keyword arguments passed along to the <code>from_pretrained</code> method of the adapter config and | |
| <code>find_adapter_config_file</code> method.`,name:"adapter_kwargs"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/integrations/peft.py#L68"}}),ft=new be({props:{name:"add_adapter",anchor:"transformers.integrations.PeftAdapterMixin.add_adapter",parameters:[{name:"adapter_config",val:""},{name:"adapter_name",val:": Optional = None"}],parametersDescription:[{anchor:"transformers.integrations.PeftAdapterMixin.add_adapter.adapter_config",description:`<strong>adapter_config</strong> (<code>~peft.PeftConfig</code>) — | |
| The configuration of the adapter to add, supported adapters are non-prefix tuning and adaption prompts | |
| methods`,name:"adapter_config"},{anchor:"transformers.integrations.PeftAdapterMixin.add_adapter.adapter_name",description:`<strong>adapter_name</strong> (<code>str</code>, <em>optional</em>, defaults to <code>"default"</code>) — | |
| The name of the adapter to add. If no name is passed, a default name is assigned to the adapter.`,name:"adapter_name"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/integrations/peft.py#L237"}}),pt=new be({props:{name:"set_adapter",anchor:"transformers.integrations.PeftAdapterMixin.set_adapter",parameters:[{name:"adapter_name",val:": Union"}],parametersDescription:[{anchor:"transformers.integrations.PeftAdapterMixin.set_adapter.adapter_name",description:`<strong>adapter_name</strong> (<code>Union[List[str], str]</code>) — | |
| The name of the adapter to set. Can be also a list of strings to set multiple adapters.`,name:"adapter_name"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/integrations/peft.py#L274"}}),mt=new be({props:{name:"disable_adapters",anchor:"transformers.integrations.PeftAdapterMixin.disable_adapters",parameters:[],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/integrations/peft.py#L319"}}),ct=new be({props:{name:"enable_adapters",anchor:"transformers.integrations.PeftAdapterMixin.enable_adapters",parameters:[],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/integrations/peft.py#L342"}}),ut=new be({props:{name:"active_adapters",anchor:"transformers.integrations.PeftAdapterMixin.active_adapters",parameters:[],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/integrations/peft.py#L364"}}),ht=new be({props:{name:"get_adapter_state_dict",anchor:"transformers.integrations.PeftAdapterMixin.get_adapter_state_dict",parameters:[{name:"adapter_name",val:": Optional = None"}],parametersDescription:[{anchor:"transformers.integrations.PeftAdapterMixin.get_adapter_state_dict.adapter_name",description:`<strong>adapter_name</strong> (<code>str</code>, <em>optional</em>) — | |
| The name of the adapter to get the state dict from. 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