bitsandbytes/pr_1908/en/_app/immutable/nodes/12.28fdb61a.js

import{s as ee,n as le,o as ne}from"../chunks/scheduler.8a2cc2fa.js";import{S as se,i as ae,e as M,s,c as i,h as ie,a as u,d as l,b as a,f as Ot,g as m,j as f,k as te,l as me,m as n,n as p,t as r,o,p as b}from"../chunks/index.7079e750.js";import{C as pe,H as y,E as re}from"../chunks/MermaidChart.svelte_svelte_type_style_lang.2b7ce466.js";import{C as P}from"../chunks/CodeBlock.a326412a.js";function oe(zt){let d,tt,D,et,J,lt,T,nt,c,Xt="Welcome to bitsandbytes! This library enables accessible large language models via k-bit quantization for PyTorch, dramatically reducing memory consumption for inference and training.",st,w,at,h,it,$,Ft="<strong>Requirements:</strong> Python 3.10+, PyTorch 2.3+",mt,g,Rt='For detailed installation instructions, see the <a href="./installation">Installation Guide</a>.',pt,U,rt,C,xt="bitsandbytes provides three main features:",ot,j,Vt="<li><strong>LLM.int8()</strong>: 8-bit quantization for inference (50% memory reduction)</li> <li><strong>QLoRA</strong>: 4-bit quantization for training (75% memory reduction)</li> <li><strong>8-bit Optimizers</strong>: Memory-efficient optimizers for training</li>",bt,B,Mt,I,ut,k,Lt="Load and run a model using 8-bit quantization:",ft,G,yt,Z,Yt='<p><strong>Learn more:</strong> See the <a href="./integrations">Integrations guide</a> for more details on using bitsandbytes with Transformers.</p>',dt,Q,Jt,W,qt="For even greater memory savings:",Tt,v,ct,_,wt,E,Nt="Combine 4-bit quantization with LoRA for efficient training:",ht,z,$t,X,At='<p><strong>Learn more:</strong> See the <a href="./fsdp_qlora">FSDP-QLoRA guide</a> for advanced training techniques and the <a href="./integrations">Integrations guide</a> for using with PEFT.</p>',gt,F,Ut,R,St="Use 8-bit optimizers to reduce training memory by 75%:",Ct,x,jt,V,Ht='<p><strong>Learn more:</strong> See the <a href="./optimizers">8-bit Optimizers guide</a> for detailed usage and configuration options.</p>',Bt,L,It,Y,Kt="Use quantized linear layers directly in your models:",kt,q,Gt,N,Zt,A,Pt='<li><a href="./optimizers">8-bit Optimizers Guide</a> - Detailed optimizer usage</li> <li><a href="./fsdp_qlora">FSDP-QLoRA</a> - Train 70B+ models on consumer GPUs</li> <li><a href="./integrations">Integrations</a> - Use with Transformers, PEFT, Accelerate</li> <li><a href="./faqs">FAQs</a> - Common questions and troubleshooting</li>',Qt,S,Wt,H,Dt='<li>Check the <a href="./faqs">FAQs</a> and <a href="./errors">Common Errors</a></li> <li>Visit <a href="https://huggingface.co/docs/bitsandbytes" rel="nofollow">official documentation</a></li> <li>Open an issue on <a href="https://github.com/bitsandbytes-foundation/bitsandbytes/issues" rel="nofollow">GitHub</a></li>',vt,K,_t,O,Et;return J=new pe({props:{containerStyle:"float: right; margin-left: 10px; display: inline-flex; position: relative; z-index: 10;"}}),T=new y({props:{title:"Quickstart",local:"quickstart",headingTag:"h1"}}),w=new y({props:{title:"Installation",local:"installation",headingTag:"h2"}}),h=new P({props:{code:"cGlwJTIwaW5zdGFsbCUyMGJpdHNhbmRieXRlcw==",highlighted:"pip install bitsandbytes",wrap:!1}}),U=new y({props:{title:"What is bitsandbytes?",local:"what-is-bitsandbytes",headingTag:"h2"}}),B=new y({props:{title:"Quick Examples",local:"quick-examples",headingTag:"h2"}}),I=new y({props:{title:"8-bit Inference",local:"8-bit-inference",headingTag:"h3"}}),G=new P({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Nb2RlbEZvckNhdXNhbExNJTJDJTIwQXV0b1Rva2VuaXplciUyQyUyMEJpdHNBbmRCeXRlc0NvbmZpZyUwQSUwQW1vZGVsJTIwJTNEJTIwQXV0b01vZGVsRm9yQ2F1c2FsTE0uZnJvbV9wcmV0cmFpbmVkKCUwQSUyMCUyMCUyMCUyMCUyMm1ldGEtbGxhbWElMkZMbGFtYS0yLTdiLWhmJTIyJTJDJTBBJTIwJTIwJTIwJTIwZGV2aWNlX21hcCUzRCUyMmF1dG8lMjIlMkMlMEElMjAlMjAlMjAlMjBxdWFudGl6YXRpb25fY29uZmlnJTNEQml0c0FuZEJ5dGVzQ29uZmlnKGxvYWRfaW5fOGJpdCUzRFRydWUpJTJDJTBBKSUwQSUwQXRva2VuaXplciUyMCUzRCUyMEF1dG9Ub2tlbml6ZXIuZnJvbV9wcmV0cmFpbmVkKCUyMm1ldGEtbGxhbWElMkZMbGFtYS0yLTdiLWhmJTIyKSUwQWlucHV0cyUyMCUzRCUyMHRva2VuaXplciglMjJIZWxsbyUyQyUyMG15JTIwbmFtZSUyMGlzJTIyJTJDJTIwcmV0dXJuX3RlbnNvcnMlM0QlMjJwdCUyMikudG8oJTIyY3VkYSUyMiklMEFvdXRwdXRzJTIwJTNEJTIwbW9kZWwuZ2VuZXJhdGUoKippbnB1dHMlMkMlMjBtYXhfbmV3X3Rva2VucyUzRDIwKSUwQXByaW50KHRva2VuaXplci5kZWNvZGUob3V0cHV0cyU1QjAlNUQpKQ==",highlighted:`<span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig

model = AutoModelForCausalLM.from_pretrained(
    <span class="hljs-string">&quot;meta-llama/Llama-2-7b-hf&quot;</span>,
    device_map=<span class="hljs-string">&quot;auto&quot;</span>,
    quantization_config=BitsAndBytesConfig(load_in_8bit=<span class="hljs-literal">True</span>),
)

tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">&quot;meta-llama/Llama-2-7b-hf&quot;</span>)
inputs = tokenizer(<span class="hljs-string">&quot;Hello, my name is&quot;</span>, return_tensors=<span class="hljs-string">&quot;pt&quot;</span>).to(<span class="hljs-string">&quot;cuda&quot;</span>)
outputs = model.generate(**inputs, max_new_tokens=<span class="hljs-number">20</span>)
<span class="hljs-built_in">print</span>(tokenizer.decode(outputs[<span class="hljs-number">0</span>]))`,wrap:!1}}),Q=new y({props:{title:"4-bit Quantization",local:"4-bit-quantization",headingTag:"h3"}}),v=new P({props:{code:"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",highlighted:`<span class="hljs-keyword">import</span> torch
<span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoModelForCausalLM, BitsAndBytesConfig

bnb_config = BitsAndBytesConfig(
    load_in_4bit=<span class="hljs-literal">True</span>,
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_quant_type=<span class="hljs-string">&quot;nf4&quot;</span>,
)

model = AutoModelForCausalLM.from_pretrained(
    <span class="hljs-string">&quot;meta-llama/Llama-2-7b-hf&quot;</span>,
    quantization_config=bnb_config,
    device_map=<span class="hljs-string">&quot;auto&quot;</span>,
)`,wrap:!1}}),_=new y({props:{title:"QLoRA Fine-tuning",local:"qlora-fine-tuning",headingTag:"h3"}}),z=new P({props:{code:"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",highlighted:`<span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoModelForCausalLM, BitsAndBytesConfig
<span class="hljs-keyword">from</span> peft <span class="hljs-keyword">import</span> LoraConfig, get_peft_model, prepare_model_for_kbit_training

<span class="hljs-comment"># Load 4-bit model</span>
bnb_config = BitsAndBytesConfig(load_in_4bit=<span class="hljs-literal">True</span>)
model = AutoModelForCausalLM.from_pretrained(
    <span class="hljs-string">&quot;meta-llama/Llama-2-7b-hf&quot;</span>,
    quantization_config=bnb_config,
)

<span class="hljs-comment"># Prepare for training</span>
model = prepare_model_for_kbit_training(model)

<span class="hljs-comment"># Add LoRA adapters</span>
lora_config = LoraConfig(
    r=<span class="hljs-number">16</span>,
    lora_alpha=<span class="hljs-number">32</span>,
    target_modules=[<span class="hljs-string">&quot;q_proj&quot;</span>, <span class="hljs-string">&quot;v_proj&quot;</span>],
    task_type=<span class="hljs-string">&quot;CAUSAL_LM&quot;</span>,
)
model = get_peft_model(model, lora_config)

<span class="hljs-comment"># Now train with your preferred trainer</span>`,wrap:!1}}),F=new y({props:{title:"8-bit Optimizers",local:"8-bit-optimizers",headingTag:"h3"}}),x=new P({props:{code:"aW1wb3J0JTIwYml0c2FuZGJ5dGVzJTIwYXMlMjBibmIlMEElMEFtb2RlbCUyMCUzRCUyMFlvdXJNb2RlbCgpJTBBJTBBJTIzJTIwUmVwbGFjZSUyMHN0YW5kYXJkJTIwb3B0aW1pemVyJTIwd2l0aCUyMDgtYml0JTIwdmVyc2lvbiUwQW9wdGltaXplciUyMCUzRCUyMGJuYi5vcHRpbS5BZGFtOGJpdChtb2RlbC5wYXJhbWV0ZXJzKCklMkMlMjBsciUzRDFlLTMpJTBBJTBBJTIzJTIwVXNlJTIwaW4lMjB0cmFpbmluZyUyMGxvb3AlMjBhcyUyMG5vcm1hbCUwQWZvciUyMGJhdGNoJTIwaW4lMjBkYXRhbG9hZGVyJTNBJTBBJTIwJTIwJTIwJTIwbG9zcyUyMCUzRCUyMG1vZGVsKGJhdGNoKSUwQSUyMCUyMCUyMCUyMGxvc3MuYmFja3dhcmQoKSUwQSUyMCUyMCUyMCUyMG9wdGltaXplci5zdGVwKCklMEElMjAlMjAlMjAlMjBvcHRpbWl6ZXIuemVyb19ncmFkKCk=",highlighted:`<span class="hljs-keyword">import</span> bitsandbytes <span class="hljs-keyword">as</span> bnb

model = YourModel()

<span class="hljs-comment"># Replace standard optimizer with 8-bit version</span>
optimizer = bnb.optim.Adam8bit(model.parameters(), lr=<span class="hljs-number">1e-3</span>)

<span class="hljs-comment"># Use in training loop as normal</span>
<span class="hljs-keyword">for</span> batch <span class="hljs-keyword">in</span> dataloader:
    loss = model(batch)
    loss.backward()
    optimizer.step()
    optimizer.zero_grad()`,wrap:!1}}),L=new y({props:{title:"Custom Quantized Layers",local:"custom-quantized-layers",headingTag:"h3"}}),q=new P({props:{code:"aW1wb3J0JTIwdG9yY2glMEFpbXBvcnQlMjBiaXRzYW5kYnl0ZXMlMjBhcyUyMGJuYiUwQSUwQSUyMyUyMDgtYml0JTIwbGluZWFyJTIwbGF5ZXIlMEFsaW5lYXJfOGJpdCUyMCUzRCUyMGJuYi5ubi5MaW5lYXI4Yml0THQoMTAyNCUyQyUyMDEwMjQlMkMlMjBoYXNfZnAxNl93ZWlnaHRzJTNERmFsc2UpJTBBJTBBJTIzJTIwNC1iaXQlMjBsaW5lYXIlMjBsYXllciUwQWxpbmVhcl80Yml0JTIwJTNEJTIwYm5iLm5uLkxpbmVhcjRiaXQoMTAyNCUyQyUyMDEwMjQlMkMlMjBjb21wdXRlX2R0eXBlJTNEdG9yY2guYmZsb2F0MTYp",highlighted:`<span class="hljs-keyword">import</span> torch
<span class="hljs-keyword">import</span> bitsandbytes <span class="hljs-keyword">as</span> bnb

<span class="hljs-comment"># 8-bit linear layer</span>
linear_8bit = bnb.nn.Linear8bitLt(<span class="hljs-number">1024</span>, <span class="hljs-number">1024</span>, has_fp16_weights=<span class="hljs-literal">False</span>)

<span class="hljs-comment"># 4-bit linear layer</span>
linear_4bit = bnb.nn.Linear4bit(<span class="hljs-number">1024</span>, <span class="hljs-number">1024</span>, compute_dtype=torch.bfloat16)`,wrap:!1}}),N=new y({props:{title:"Next Steps",local:"next-steps",headingTag:"h2"}}),S=new y({props:{title:"Getting Help",local:"getting-help",headingTag:"h2"}}),K=new 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