trl/pr_5607/en/_app/immutable/nodes/58.bcb41f28.js

import{s as Nt,n as At,o as Dt}from"../chunks/scheduler.7b731bd4.js";import{S as Pt,i as Ot,e as i,s as a,c as o,h as Kt,a as r,d as l,b as s,f as Xt,g as p,j as y,k as Yt,l as te,m as n,n as m,t as u,o as T,p as J}from"../chunks/index.cc268345.js";import{C as ee,H as w,E as le}from"../chunks/MermaidChart.svelte_svelte_type_style_lang.f0d99f98.js";import{C as N}from"../chunks/CodeBlock.169a125f.js";function ne(kt){let M,P,A,O,d,K,f,tt,g,Ft='Unsloth is an open‑source framework for fine‑tuning and reinforcement learning that trains LLMs (like Llama, OpenAI gpt-oss, Mistral, Gemma, DeepSeek, and more) up to 2× faster with up to 80% less VRAM. Unsloth allows <a href="https://huggingface.co/docs/trl/en/unsloth_integration#Training" rel="nofollow">training</a>, evaluation, running and <a href="https://huggingface.co/docs/trl/en/unsloth_integration#Saving-the-model" rel="nofollow">deployment</a> with other inference engines like llama.cpp, Ollama and vLLM.',et,c,Rt='The library provides a streamlined, Hugging Face compatible workflow for training, evaluation, inference and deployment and is fully compatible with <a href="/docs/trl/pr_5607/en/sft_trainer#trl.SFTTrainer">SFTTrainer</a>.',lt,h,nt,U,Wt="<li>Training support for all transformer compatible models: Text-to-speech (TTS), multimodal, BERT, RL and more</li> <li>Supports full fine-tuning, pretraining, LoRA, QLoRA, 8-bit training &amp; more</li> <li>Works on Linux, Windows, Colab, Kaggle; NVIDIA GPUs, soon AMD &amp; Intel setups</li> <li>Supports most features TRL supports, including RLHF (GSPO, GRPO, DPO etc.)</li> <li>Hand-written Triton kernels and a manual backprop engine ensure no accuracy degradation (0% approximation error)</li>",at,I,st,b,it,$,qt="Local Installation (Linux recommended):",ot,j,rt,C,Bt='You can also install <code>unsloth</code> according to the <a href="https://docs.unsloth.ai/get-started/installing-+-updating" rel="nofollow">official documentation</a>. Once installed, you can incorporate unsloth into your workflow in a very simple manner; instead of loading <a href="https://huggingface.co/docs/transformers/main/en/model_doc/auto#transformers.AutoModelForCausalLM" rel="nofollow">AutoModelForCausalLM</a>, you just need to load a <code>FastLanguageModel</code> as follows:',pt,_,mt,Z,Gt='The saved model is fully compatible with Hugging Face’s transformers library. Learn more about unsloth in their <a href="https://github.com/unslothai/unsloth" rel="nofollow">official repository</a>.',ut,v,Tt,k,Jt,F,Vt="Access Jupyter Lab at <code>http://localhost:8888</code> and start fine-tuning!",yt,R,Mt,W,xt="These are some core settings you can toggle before training:",wt,q,Qt="<li><code>max_seq_length = 2048</code> – Controls context length. While Llama-3 supports 8192, we recommend 2048 for testing. Unsloth enables 4× longer context fine-tuning.</li> <li><code>dtype = &quot;auto&quot;</code> – For auto-detection; use torch.float16 or torch.bfloat16 for newer GPUs.</li> <li><code>load_in_4bit = True</code> – Enables 4-bit quantization, reducing memory use 4× for fine-tuning. Disabling it allows for LoRA 16-bit fine-tuning to be enabled.</li> <li>To enable full fine-tuning (FFT), set <code>full_finetuning = True</code>. For 8-bit fine-tuning, set <code>load_in_8bit = True</code>. Note: Only one training method can be set to True at a time.</li>",dt,B,Lt='For more information on configuring Unsloth’s hyperparameters and features, read their <a href="https://docs.unsloth.ai/get-started/fine-tuning-llms-guide" rel="nofollow">documentation guide here</a>.',ft,G,gt,V,Ht='Unsloth allows you to directly save the finetuned model as a small file called a LoRA adapter. You can instead push to the Hugging Face hub as well if you want to upload your model! Remember to get a <a href="https://huggingface.co/settings/tokens" rel="nofollow">Hugging Face token</a> and add your token!',ct,x,ht,Q,zt="To save to GGUF, Unsloth uses llama.cpp. To save locally:",Ut,L,It,H,Et="To push to the hub:",bt,z,$t,E,jt,S,St="To save to 16-bit for vLLM, use:",Ct,X,_t,Y,Zt,D,vt;return d=new ee({props:{containerStyle:"float: right; margin-left: 10px; display: inline-flex; position: relative; z-index: 10;"}}),f=new w({props:{title:"Unsloth Integration",local:"unsloth-integration",headingTag:"h1"}}),h=new w({props:{title:"Key Features",local:"key-features",headingTag:"h2"}}),I=new w({props:{title:"Installation",local:"installation",headingTag:"h2"}}),b=new w({props:{title:"pip install",local:"pip-install",headingTag:"h3"}}),j=new N({props:{code:"cGlwJTIwaW5zdGFsbCUyMHVuc2xvdGg=",highlighted:"pip install unsloth",wrap:!1}}),_=new N({props:{code:"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",highlighted:`<span class="hljs-keyword">import</span> torch
<span class="hljs-keyword">from</span> trl <span class="hljs-keyword">import</span> SFTConfig, SFTTrainer
<span class="hljs-keyword">from</span> unsloth <span class="hljs-keyword">import</span> FastLanguageModel

max_length = <span class="hljs-number">2048</span> <span class="hljs-comment"># Supports automatic RoPE Scaling, so choose any number</span>

<span class="hljs-comment"># Load model</span>
model, tokenizer = FastLanguageModel.from_pretrained(
    model_name=<span class="hljs-string">&quot;unsloth/mistral-7b&quot;</span>,
    max_seq_length=max_length,
    dtype=<span class="hljs-string">&quot;auto&quot;</span>,  <span class="hljs-comment"># For auto-detection. Float16 for Tesla T4, V100, Bfloat16 for Ampere+</span>
    load_in_4bit=<span class="hljs-literal">True</span>,  <span class="hljs-comment"># Use 4bit quantization to reduce memory usage. Can be False</span>
)

<span class="hljs-comment"># Do model patching and add fast LoRA weights</span>
model = FastLanguageModel.get_peft_model(
    model,
    r=<span class="hljs-number">16</span>,
    target_modules=[
        <span class="hljs-string">&quot;q_proj&quot;</span>,
        <span class="hljs-string">&quot;k_proj&quot;</span>,
        <span class="hljs-string">&quot;v_proj&quot;</span>,
        <span class="hljs-string">&quot;o_proj&quot;</span>,
        <span class="hljs-string">&quot;gate_proj&quot;</span>,
        <span class="hljs-string">&quot;up_proj&quot;</span>,
        <span class="hljs-string">&quot;down_proj&quot;</span>,
    ],
    lora_alpha=<span class="hljs-number">16</span>,
    lora_dropout=<span class="hljs-number">0</span>,  <span class="hljs-comment"># Dropout = 0 is currently optimized</span>
    bias=<span class="hljs-string">&quot;none&quot;</span>,  <span class="hljs-comment"># Bias = &quot;none&quot; is currently optimized</span>
    use_gradient_checkpointing=<span class="hljs-literal">True</span>,
    random_state=<span class="hljs-number">3407</span>,
)

training_args = SFTConfig(output_dir=<span class="hljs-string">&quot;./output&quot;</span>, max_length=max_length)

trainer = SFTTrainer(
    model=model,
    args=training_args,
    train_dataset=dataset,
)
trainer.train()`,wrap:!1}}),v=new w({props:{title:"Docker Install",local:"docker-install",headingTag:"h3"}}),k=new N({props:{code:"ZG9ja2VyJTIwcnVuJTIwLWQlMjAtZSUyMEpVUFlURVJfUEFTU1dPUkQlM0QlMjJteXBhc3N3b3JkJTIyJTIwJTVDJTBBJTIwJTIwLXAlMjA4ODg4JTNBODg4OCUyMC1wJTIwMjIyMiUzQTIyJTIwJTVDJTBBJTIwJTIwLXYlMjAlMjQocHdkKSUyRndvcmslM0ElMkZ3b3Jrc3BhY2UlMkZ3b3JrJTIwJTVDJTBBJTIwJTIwLS1ncHVzJTIwYWxsJTIwJTVDJTBBJTIwJTIwdW5zbG90aCUyRnVuc2xvdGg=",highlighted:`docker run -d -e JUPYTER_PASSWORD=<span class="hljs-string">&quot;mypassword&quot;</span> \\
  -p 8888:8888 -p 2222:22 \\
  -v $(<span class="hljs-built_in">pwd</span>)/work:/workspace/work \\
  --gpus all \\
  unsloth/unsloth`,wrap:!1}}),R=new w({props:{title:"Training",local:"training",headingTag:"h2"}}),G=new w({props:{title:"Saving the model",local:"saving-the-model",headingTag:"h2"}}),x=new w({props:{title:"Saving to GGUF",local:"saving-to-gguf",headingTag:"h3"}}),L=new N({props:{code:"bW9kZWwuc2F2ZV9wcmV0cmFpbmVkX2dndWYoJTIyZGlyZWN0b3J5JTIyJTJDJTIwdG9rZW5pemVyJTJDJTIwcXVhbnRpemF0aW9uX21ldGhvZCUyMCUzRCUyMCUyMnE0X2tfbSUyMiklMEFtb2RlbC5zYXZlX3ByZXRyYWluZWRfZ2d1ZiglMjJkaXJlY3RvcnklMjIlMkMlMjB0b2tlbml6ZXIlMkMlMjBxdWFudGl6YXRpb25fbWV0aG9kJTIwJTNEJTIwJTIycThfMCUyMiklMEFtb2RlbC5zYXZlX3ByZXRyYWluZWRfZ2d1ZiglMjJkaXJlY3RvcnklMjIlMkMlMjB0b2tlbml6ZXIlMkMlMjBxdWFudGl6YXRpb25fbWV0aG9kJTIwJTNEJTIwJTIyZjE2JTIyKQ==",highlighted:`model.save_pretrained_gguf(<span class="hljs-string">&quot;directory&quot;</span>, tokenizer, quantization_method = <span class="hljs-string">&quot;q4_k_m&quot;</span>)
model.save_pretrained_gguf(<span class="hljs-string">&quot;directory&quot;</span>, tokenizer, quantization_method = <span class="hljs-string">&quot;q8_0&quot;</span>)
model.save_pretrained_gguf(<span class="hljs-string">&quot;directory&quot;</span>, tokenizer, quantization_method = <span class="hljs-string">&quot;f16&quot;</span>)`,wrap:!1}}),z=new N({props:{code:"bW9kZWwucHVzaF90b19odWJfZ2d1ZiglMjJoZl91c2VybmFtZSUyRmRpcmVjdG9yeSUyMiUyQyUyMHRva2VuaXplciUyQyUyMHF1YW50aXphdGlvbl9tZXRob2QlMjAlM0QlMjAlMjJxNF9rX20lMjIpJTBBbW9kZWwucHVzaF90b19odWJfZ2d1ZiglMjJoZl91c2VybmFtZSUyRmRpcmVjdG9yeSUyMiUyQyUyMHRva2VuaXplciUyQyUyMHF1YW50aXphdGlvbl9tZXRob2QlMjAlM0QlMjAlMjJxOF8wJTIyKQ==",highlighted:`model.push_to_hub_gguf(<span class="hljs-string">&quot;hf_username/directory&quot;</span>, tokenizer, quantization_method = <span class="hljs-string">&quot;q4_k_m&quot;</span>)
model.push_to_hub_gguf(<span class="hljs-string">&quot;hf_username/directory&quot;</span>, tokenizer, quantization_method = <span class="hljs-string">&quot;q8_0&quot;</span>)`,wrap:!1}}),E=new w({props:{title:"Saving to vLLM",local:"saving-to-vllm",headingTag:"h3"}}),X=new N({props:{code:"bW9kZWwuc2F2ZV9wcmV0cmFpbmVkX21lcmdlZCglMjJtb2RlbCUyMiUyQyUyMHRva2VuaXplciUyQyUyMHNhdmVfbWV0aG9kJTIwJTNEJTIwJTIybWVyZ2VkXzE2Yml0JTIyJTJDKSUwQW1vZGVsLnB1c2hfdG9faHViX21lcmdlZCglMjJoZiUyRm1vZGVsJTIyJTJDJTIwdG9rZW5pemVyJTJDJTIwc2F2ZV9tZXRob2QlMjAlM0QlMjAlMjJtZXJnZWRfMTZiaXQlMjIlMkMlMjB0b2tlbiUyMCUzRCUyMCUyMiUyMik=",highlighted:`model.save_pretrained_merged(<span class="hljs-string">&quot;model&quot;</span>, tokenizer, save_method = <span class="hljs-string">&quot;merged_16bit&quot;</span>,)
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