import{S as xr,i as Wr,s as Vr,e as s,k as u,w as d,t as n,M as Ar,c as l,d as t,m as c,a as o,x as f,h as r,b as h,f as As,N as Lr,G as a,g as p,y as m,q as g,o as y,B as b,v as Rr}from"../chunks/vendor-hf-doc-builder.js";import{T as Xr}from"../chunks/Tip-hf-doc-builder.js";import{I}from"../chunks/IconCopyLink-hf-doc-builder.js";import{C as je}from"../chunks/CodeBlock-hf-doc-builder.js";import{D as zr}from"../chunks/DocNotebookDropdown-hf-doc-builder.js";function Cr(ya){let v,D,w,k,V,U,$,A,Je;return{c(){v=s("p"),D=n("If you\u2019re interested in basic LLM usage, our high-level "),w=s("a"),k=s("code"),V=n("Pipeline"),U=n(" interface is a great starting point. However, LLMs often require advanced features like quantization and fine control of the token selection step, which is best done through "),$=s("a"),A=n("generate()"),Je=n(". Autoregressive generation with LLMs is also resource-intensive and should be executed on a GPU for adequate throughput."),this.h()},l(G){v=l(G,"P",{});var M=o(v);D=r(M,"If you\u2019re interested in basic LLM usage, our high-level "),w=l(M,"A",{href:!0});var Ee=o(w);k=l(Ee,"CODE",{});var R=o(k);V=r(R,"Pipeline"),R.forEach(t),Ee.forEach(t),U=r(M," interface is a great starting point. However, LLMs often require advanced features like quantization and fine control of the token selection step, which is best done through "),$=l(M,"A",{href:!0});var Ke=o($);A=r(Ke,"generate()"),Ke.forEach(t),Je=r(M,". Autoregressive generation with LLMs is also resource-intensive and should be executed on a GPU for adequate throughput."),M.forEach(t),this.h()},h(){h(w,"href","pipeline_tutorial"),h($,"href","/docs/transformers/main/en/main_classes/text_generation#transformers.GenerationMixin.generate")},m(G,M){p(G,v,M),a(v,D),a(v,w),a(w,k),a(k,V),a(v,U),a(v,$),a($,A),a(v,Je)},d(G){G&&t(v)}}}function Br(ya){let v,D,w,k,V,U,$,A,Je,G,M,Ee,R,Ke,ba,O,Rs,et,Xs,zs,va,tt,Cs,wa,L,Lt,Bs,Ns,xt,Fs,qs,Wt,Ss,Ma,at,Qs,_a,Te,ka,X,K,Vt,Ue,Hs,At,Ys,ja,ee,Ps,st,Ds,Os,Ja,z,J,an,Ks,Rt,el,Ea,lt,tl,Ta,C,E,sn,al,Xt,sl,Ua,te,ll,zt,ol,nl,Za,ae,rl,ot,il,pl,Ia,nt,hl,$a,se,Ga,rt,ul,La,Ze,xa,le,cl,Ct,dl,fl,Wa,oe,it,Bt,ml,gl,yl,ne,Nt,bl,vl,pt,wl,Ml,Va,ht,_l,Aa,re,kl,ut,jl,Jl,Ra,Ie,Xa,x,El,Ft,Tl,Ul,ct,Zl,Il,za,ie,$l,dt,Gl,Ll,Ca,$e,Ba,ft,xl,Na,B,pe,qt,Ge,Wl,St,Vl,Fa,he,Al,mt,Rl,Xl,qa,Le,Sa,N,ue,Qt,xe,zl,Ht,Cl,Qa,_,Bl,gt,Nl,Fl,Yt,ql,Sl,Pt,Ql,Hl,Dt,Yl,Pl,We,Dl,Ol,Ha,Ve,Ya,F,ce,Ot,Ae,Kl,Kt,eo,Pa,j,to,yt,ao,so,ea,lo,oo,ta,no,ro,Re,io,po,Da,Xe,Oa,q,de,aa,ze,ho,sa,uo,Ka,fe,co,Ce,fo,mo,es,Be,ts,S,me,la,Ne,go,oa,yo,as,bt,bo,ss,Q,ge,na,Fe,vo,ra,wo,ls,ye,vt,wt,Mo,_o,ko,Z,jo,Mt,Jo,Eo,_t,To,Uo,kt,Zo,Io,os,H,be,ia,qe,$o,pa,Go,ns,ve,jt,Se,Lo,xo,Wo,Jt,Qe,Vo,Ao,rs,Y,we,ha,He,Ro,ua,Xo,is,Et,Tt,Ut,zo,Co,ps,P,Me,ca,Ye,Bo,da,No,hs,_e,Zt,Pe,fa,Fo,qo,So,It,De,ma,Qo,Ho,us;return U=new I({}),M=new zr({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/en/llm_tutorial.ipynb"},{label:"PyTorch",value:"https://colab.research.google.com/github/huggingface/notebooks/blob/main/transformers_doc/en/pytorch/llm_tutorial.ipynb"},{label:"TensorFlow",value:"https://colab.research.google.com/github/huggingface/notebooks/blob/main/transformers_doc/en/tensorflow/llm_tutorial.ipynb"},{label:"Mixed",value:"https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/transformers_doc/en/llm_tutorial.ipynb"},{label:"PyTorch",value:"https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/transformers_doc/en/pytorch/llm_tutorial.ipynb"},{label:"TensorFlow",value:"https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/transformers_doc/en/tensorflow/llm_tutorial.ipynb"}]}}),Te=new je({props:{code:"cGlwJTIwaW5zdGFsbCUyMHRyYW5zZm9ybWVycyUyMGJpdHNhbmRieXRlcyUzRSUzRDAuMzkuMCUyMC1x",highlighted:"pip install transformers bitsandbytes&gt;=0.39.0 -q"}}),Ue=new I({}),se=new Xr({props:{$$slots:{default:[Cr]},$$scope:{ctx:ya}}}),Ze=new je({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Nb2RlbEZvckNhdXNhbExNJTBBJTBBbW9kZWwlMjAlM0QlMjBBdXRvTW9kZWxGb3JDYXVzYWxMTS5mcm9tX3ByZXRyYWluZWQoJTBBJTIwJTIwJTIwJTIwJTIyb3BlbmxtLXJlc2VhcmNoJTJGb3Blbl9sbGFtYV83YiUyMiUyQyUyMGRldmljZV9tYXAlM0QlMjJhdXRvJTIyJTJDJTIwbG9hZF9pbl80Yml0JTNEVHJ1ZSUwQSk=",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoModelForCausalLM

<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForCausalLM.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;openlm-research/open_llama_7b&quot;</span>, device_map=<span class="hljs-string">&quot;auto&quot;</span>, load_in_4bit=<span class="hljs-literal">True</span>
<span class="hljs-meta">... </span>)`}}),Ie=new je({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Ub2tlbml6ZXIlMEElMEF0b2tlbml6ZXIlMjAlM0QlMjBBdXRvVG9rZW5pemVyLmZyb21fcHJldHJhaW5lZCglMjJvcGVubG0tcmVzZWFyY2glMkZvcGVuX2xsYW1hXzdiJTIyKSUwQW1vZGVsX2lucHV0cyUyMCUzRCUyMHRva2VuaXplciglNUIlMjJBJTIwbGlzdCUyMG9mJTIwY29sb3JzJTNBJTIwcmVkJTJDJTIwYmx1ZSUyMiU1RCUyQyUyMHJldHVybl90ZW5zb3JzJTNEJTIycHQlMjIpLnRvKCUyMmN1ZGElMjIp",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoTokenizer

<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">&quot;openlm-research/open_llama_7b&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model_inputs = tokenizer([<span class="hljs-string">&quot;A list of colors: red, blue&quot;</span>], return_tensors=<span class="hljs-string">&quot;pt&quot;</span>).to(<span class="hljs-string">&quot;cuda&quot;</span>)`}}),$e=new je({props:{code:"Z2VuZXJhdGVkX2lkcyUyMCUzRCUyMG1vZGVsLmdlbmVyYXRlKCoqbW9kZWxfaW5wdXRzKSUwQXRva2VuaXplci5iYXRjaF9kZWNvZGUoZ2VuZXJhdGVkX2lkcyUyQyUyMHNraXBfc3BlY2lhbF90b2tlbnMlM0RUcnVlKSU1QjAlNUQ=",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span>generated_ids = model.generate(**model_inputs)
<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer.batch_decode(generated_ids, skip_special_tokens=<span class="hljs-literal">True</span>)[<span class="hljs-number">0</span>]
<span class="hljs-string">&#x27;A list of colors: red, blue, green, yellow, black, white, and brown&#x27;</span>`}}),Ge=new I({}),Le=new je({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoModelForCausalLM, AutoTokenizer

<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">&quot;openlm-research/open_llama_7b&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer.pad_token = tokenizer.eos_token  <span class="hljs-comment"># Llama has no pad token by default</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForCausalLM.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;openlm-research/open_llama_7b&quot;</span>, device_map=<span class="hljs-string">&quot;auto&quot;</span>, load_in_4bit=<span class="hljs-literal">True</span>
<span class="hljs-meta">... </span>)`}}),xe=new I({}),Ve=new je({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span>model_inputs = tokenizer([<span class="hljs-string">&quot;A sequence of numbers: 1, 2&quot;</span>], return_tensors=<span class="hljs-string">&quot;pt&quot;</span>).to(<span class="hljs-string">&quot;cuda&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># By default, the output will contain up to 20 tokens</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>generated_ids = model.generate(**model_inputs)
<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer.batch_decode(generated_ids, skip_special_tokens=<span class="hljs-literal">True</span>)[<span class="hljs-number">0</span>]
<span class="hljs-string">&#x27;A sequence of numbers: 1, 2, 3, 4, 5&#x27;</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Setting \`max_new_tokens\` allows you to control the maximum length</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>generated_ids = model.generate(**model_inputs, max_new_tokens=<span class="hljs-number">50</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer.batch_decode(generated_ids, skip_special_tokens=<span class="hljs-literal">True</span>)[<span class="hljs-number">0</span>]
<span class="hljs-string">&#x27;A sequence of numbers: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,&#x27;</span>`}}),Ae=new I({}),Xe=new je({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Set seed or reproducibility -- you don&#x27;t need this unless you want full reproducibility</span>
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> set_seed
<span class="hljs-meta">&gt;&gt;&gt; </span>set_seed(<span class="hljs-number">0</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>model_inputs = tokenizer([<span class="hljs-string">&quot;I am a cat.&quot;</span>], return_tensors=<span class="hljs-string">&quot;pt&quot;</span>).to(<span class="hljs-string">&quot;cuda&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># LLM + greedy decoding = repetitive, boring output</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>generated_ids = model.generate(**model_inputs)
<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer.batch_decode(generated_ids, skip_special_tokens=<span class="hljs-literal">True</span>)[<span class="hljs-number">0</span>]
<span class="hljs-string">&#x27;I am a cat. I am a cat. I am a cat. I am a cat&#x27;</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># With sampling, the output becomes more creative!</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>generated_ids = model.generate(**model_inputs, do_sample=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer.batch_decode(generated_ids, skip_special_tokens=<span class="hljs-literal">True</span>)[<span class="hljs-number">0</span>]
<span class="hljs-string">&#x27;I am a cat.\\nI just need to be. I am always.\\nEvery time&#x27;</span>`}}),ze=new I({}),Be=new je({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># The tokenizer initialized above has right-padding active by default: the 1st sequence,</span>
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># which is shorter, has padding on the right side. Generation fails.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model_inputs = tokenizer(
<span class="hljs-meta">... </span>    [<span class="hljs-string">&quot;1, 2, 3&quot;</span>, <span class="hljs-string">&quot;A, B, C, D, E&quot;</span>], padding=<span class="hljs-literal">True</span>, return_tensors=<span class="hljs-string">&quot;pt&quot;</span>
<span class="hljs-meta">... </span>).to(<span class="hljs-string">&quot;cuda&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>generated_ids = model.generate(**model_inputs)
<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer.batch_decode(generated_ids[<span class="hljs-number">0</span>], skip_special_tokens=<span class="hljs-literal">True</span>)[<span class="hljs-number">0</span>]
<span class="hljs-string">&#x27;&#x27;</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># With left-padding, it works as expected!</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">&quot;openlm-research/open_llama_7b&quot;</span>, padding_side=<span class="hljs-string">&quot;left&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer.pad_token = tokenizer.eos_token  <span class="hljs-comment"># Llama has no pad token by default</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model_inputs = tokenizer(
<span class="hljs-meta">... </span>    [<span class="hljs-string">&quot;1, 2, 3&quot;</span>, <span class="hljs-string">&quot;A, B, C, D, E&quot;</span>], padding=<span class="hljs-literal">True</span>, return_tensors=<span class="hljs-string">&quot;pt&quot;</span>
<span class="hljs-meta">... </span>).to(<span class="hljs-string">&quot;cuda&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>generated_ids = model.generate(**model_inputs)
<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer.batch_decode(generated_ids, skip_special_tokens=<span class="hljs-literal">True</span>)[<span class="hljs-number">0</span>]
<span class="hljs-string">&#x27;1, 2, 3, 4, 5, 6,&#x27;</span>`}}),Ne=new I({}),Fe=new I({}),qe=new I({}),He=new I({}),Ye=new I({}),{c(){v=s("meta"),D=u(),w=s("h1"),k=s("a"),V=s("span"),d(U.$$.fragment),$=u(),A=s("span"),Je=n("Generation with LLMs"),G=u(),d(M.$$.fragment),Ee=u(),R=s("p"),Ke=n("LLMs, or Large Language Models, are the key component behind text generation. In a nutshell, they consist of large pretrained transformer models trained to predict the next word (or, more precisely, token) given some input text. Since they predict one token at a time, you need to do something more elaborate to generate new sentences other than just calling the model \u2014 you need to do autoregressive generation."),ba=u(),O=s("p"),Rs=n("Autoregressive generation is the inference-time procedure of iteratively calling a model with its own generated outputs, given a few initial inputs. 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