import{s as Zt,o as vt,n as U}from"../chunks/scheduler.d627b047.js";import{S as Lt,i as Ct,g as b,s as p,r as d,A as St,h as y,f as o,c,j as jt,u as g,x as I,k as Ut,y as Wt,a as r,v as w,d as h,t as T,w as J}from"../chunks/index.73c51727.js";import{T as Rt}from"../chunks/Tip.a82942ec.js";import{C as B}from"../chunks/CodeBlock.5f78c87f.js";import{H as Z,E as xt}from"../chunks/index.8df1db42.js";import{H as nt,a as v}from"../chunks/HfOption.d6c7dc54.js";function Gt(_){let l,f;return l=new B({props:{code:"dHJsJTIwc2Z0JTIwJTVDJTBBJTIwJTIwLS1tb2RlbF9uYW1lX29yX3BhdGglMjBRd2VuJTJGUXdlbjIuNS0wLjVCJTIwJTVDJTBBJTIwJTIwLS1kYXRhc2V0X25hbWUlMjBzdGFuZm9yZG5scCUyRmltZGI=",highlighted:`trl sft \\
  --model_name_or_path Qwen/Qwen2.5-0.5B \\
  --dataset_name stanfordnlp/imdb`,wrap:!1}}),{c(){d(l.$$.fragment)},l(t){g(l.$$.fragment,t)},m(t,u){w(l,t,u),f=!0},p:U,i(t){f||(h(l.$$.fragment,t),f=!0)},o(t){T(l.$$.fragment,t),f=!1},d(t){J(l,t)}}}function Yt(_){let l,f;return l=new B({props:{code:"dHJsJTIwZHBvJTIwJTVDJTBBJTIwJTIwLS1tb2RlbF9uYW1lX29yX3BhdGglMjBRd2VuJTJGUXdlbjIuNS0wLjVCJTIwJTVDJTBBJTIwJTIwLS1kYXRhc2V0X25hbWUlMjBhbnRocm9waWMlMkZoaC1ybGhm",highlighted:`trl dpo \\
  --model_name_or_path Qwen/Qwen2.5-0.5B \\
  --dataset_name anthropic/hh-rlhf`,wrap:!1}}),{c(){d(l.$$.fragment)},l(t){g(l.$$.fragment,t)},m(t,u){w(l,t,u),f=!0},p:U,i(t){f||(h(l.$$.fragment,t),f=!0)},o(t){T(l.$$.fragment,t),f=!1},d(t){J(l,t)}}}function Vt(_){let l,f,t,u;return l=new v({props:{id:"command_line",option:"SFT",$$slots:{default:[Gt]},$$scope:{ctx:_}}}),t=new v({props:{id:"command_line",option:"DPO",$$slots:{default:[Yt]},$$scope:{ctx:_}}}),{c(){d(l.$$.fragment),f=p(),d(t.$$.fragment)},l(i){g(l.$$.fragment,i),f=c(i),g(t.$$.fragment,i)},m(i,m){w(l,i,m),r(i,f,m),w(t,i,m),u=!0},p(i,m){const $={};m&2&&($.$$scope={dirty:m,ctx:i}),l.$set($);const n={};m&2&&(n.$$scope={dirty:m,ctx:i}),t.$set(n)},i(i){u||(h(l.$$.fragment,i),h(t.$$.fragment,i),u=!0)},o(i){T(l.$$.fragment,i),T(t.$$.fragment,i),u=!1},d(i){i&&o(f),J(l,i),J(t,i)}}}function Et(_){let l,f,t,u="Launch with:",i,m,$;return l=new B({props:{code:"JTIzJTIwc2Z0X2NvbmZpZy55YW1sJTBBbW9kZWxfbmFtZV9vcl9wYXRoJTNBJTIwUXdlbiUyRlF3ZW4yLjUtMC41QiUwQWRhdGFzZXRfbmFtZSUzQSUyMHN0YW5mb3JkbmxwJTJGaW1kYg==",highlighted:`<span class="hljs-comment"># sft_config.yaml</span>
<span class="hljs-attr">model_name_or_path:</span> <span class="hljs-string">Qwen/Qwen2.5-0.5B</span>
<span class="hljs-attr">dataset_name:</span> <span class="hljs-string">stanfordnlp/imdb</span>`,wrap:!1}}),m=new B({props:{code:"dHJsJTIwc2Z0JTIwLS1jb25maWclMjBzZnRfY29uZmlnLnlhbWw=",highlighted:"trl sft --config sft_config.yaml",wrap:!1}}),{c(){d(l.$$.fragment),f=p(),t=b("p"),t.textContent=u,i=p(),d(m.$$.fragment)},l(n){g(l.$$.fragment,n),f=c(n),t=y(n,"P",{"data-svelte-h":!0}),I(t)!=="svelte-ge9qnz"&&(t.textContent=u),i=c(n),g(m.$$.fragment,n)},m(n,a){w(l,n,a),r(n,f,a),r(n,t,a),r(n,i,a),w(m,n,a),$=!0},p:U,i(n){$||(h(l.$$.fragment,n),h(m.$$.fragment,n),$=!0)},o(n){T(l.$$.fragment,n),T(m.$$.fragment,n),$=!1},d(n){n&&(o(f),o(t),o(i)),J(l,n),J(m,n)}}}function Xt(_){let l,f,t,u="Launch with:",i,m,$;return l=new B({props:{code:"JTIzJTIwZHBvX2NvbmZpZy55YW1sJTBBbW9kZWxfbmFtZV9vcl9wYXRoJTNBJTIwUXdlbiUyRlF3ZW4yLjUtMC41QiUwQWRhdGFzZXRfbmFtZSUzQSUyMGFudGhyb3BpYyUyRmhoLXJsaGY=",highlighted:`<span class="hljs-comment"># dpo_config.yaml</span>
<span class="hljs-attr">model_name_or_path:</span> <span class="hljs-string">Qwen/Qwen2.5-0.5B</span>
<span class="hljs-attr">dataset_name:</span> <span class="hljs-string">anthropic/hh-rlhf</span>`,wrap:!1}}),m=new B({props:{code:"dHJsJTIwZHBvJTIwLS1jb25maWclMjBkcG9fY29uZmlnLnlhbWw=",highlighted:"trl dpo --config dpo_config.yaml",wrap:!1}}),{c(){d(l.$$.fragment),f=p(),t=b("p"),t.textContent=u,i=p(),d(m.$$.fragment)},l(n){g(l.$$.fragment,n),f=c(n),t=y(n,"P",{"data-svelte-h":!0}),I(t)!=="svelte-ge9qnz"&&(t.textContent=u),i=c(n),g(m.$$.fragment,n)},m(n,a){w(l,n,a),r(n,f,a),r(n,t,a),r(n,i,a),w(m,n,a),$=!0},p:U,i(n){$||(h(l.$$.fragment,n),h(m.$$.fragment,n),$=!0)},o(n){T(l.$$.fragment,n),T(m.$$.fragment,n),$=!1},d(n){n&&(o(f),o(t),o(i)),J(l,n),J(m,n)}}}function Ft(_){let l,f,t,u;return l=new v({props:{id:"config_file",option:"SFT",$$slots:{default:[Et]},$$scope:{ctx:_}}}),t=new v({props:{id:"config_file",option:"DPO",$$slots:{default:[Xt]},$$scope:{ctx:_}}}),{c(){d(l.$$.fragment),f=p(),d(t.$$.fragment)},l(i){g(l.$$.fragment,i),f=c(i),g(t.$$.fragment,i)},m(i,m){w(l,i,m),r(i,f,m),w(t,i,m),u=!0},p(i,m){const $={};m&2&&($.$$scope={dirty:m,ctx:i}),l.$set($);const n={};m&2&&(n.$$scope={dirty:m,ctx:i}),t.$set(n)},i(i){u||(h(l.$$.fragment,i),h(t.$$.fragment,i),u=!0)},o(i){T(l.$$.fragment,i),T(t.$$.fragment,i),u=!1},d(i){i&&o(f),J(l,i),J(t,i)}}}function Nt(_){let l,f;return l=new B({props:{code:"dHJsJTIwc2Z0JTIwJTVDJTBBJTIwJTIwLS1tb2RlbF9uYW1lX29yX3BhdGglMjBRd2VuJTJGUXdlbjIuNS0wLjVCJTIwJTVDJTBBJTIwJTIwLS1kYXRhc2V0X25hbWUlMjBzdGFuZm9yZG5scCUyRmltZGIlMjAlNUMlMEElMjAlMjAtLW51bV9wcm9jZXNzZXMlMjA0",highlighted:`trl sft \\
  --model_name_or_path Qwen/Qwen2.5-0.5B \\
  --dataset_name stanfordnlp/imdb \\
  --num_processes 4`,wrap:!1}}),{c(){d(l.$$.fragment)},l(t){g(l.$$.fragment,t)},m(t,u){w(l,t,u),f=!0},p:U,i(t){f||(h(l.$$.fragment,t),f=!0)},o(t){T(l.$$.fragment,t),f=!1},d(t){J(l,t)}}}function zt(_){let l,f,t,u="Launch with:",i,m,$;return l=new B({props:{code:"JTIzJTIwc2Z0X2NvbmZpZy55YW1sJTBBbW9kZWxfbmFtZV9vcl9wYXRoJTNBJTIwUXdlbiUyRlF3ZW4yLjUtMC41QiUwQWRhdGFzZXRfbmFtZSUzQSUyMHN0YW5mb3JkbmxwJTJGaW1kYiUwQW51bV9wcm9jZXNzZXMlM0ElMjA0",highlighted:`<span class="hljs-comment"># sft_config.yaml</span>
<span class="hljs-attr">model_name_or_path:</span> <span class="hljs-string">Qwen/Qwen2.5-0.5B</span>
<span class="hljs-attr">dataset_name:</span> <span class="hljs-string">stanfordnlp/imdb</span>
<span class="hljs-attr">num_processes:</span> <span class="hljs-number">4</span>`,wrap:!1}}),m=new B({props:{code:"dHJsJTIwc2Z0JTIwLS1jb25maWclMjBzZnRfY29uZmlnLnlhbWw=",highlighted:"trl sft --config sft_config.yaml",wrap:!1}}),{c(){d(l.$$.fragment),f=p(),t=b("p"),t.textContent=u,i=p(),d(m.$$.fragment)},l(n){g(l.$$.fragment,n),f=c(n),t=y(n,"P",{"data-svelte-h":!0}),I(t)!=="svelte-ge9qnz"&&(t.textContent=u),i=c(n),g(m.$$.fragment,n)},m(n,a){w(l,n,a),r(n,f,a),r(n,t,a),r(n,i,a),w(m,n,a),$=!0},p:U,i(n){$||(h(l.$$.fragment,n),h(m.$$.fragment,n),$=!0)},o(n){T(l.$$.fragment,n),T(m.$$.fragment,n),$=!1},d(n){n&&(o(f),o(t),o(i)),J(l,n),J(m,n)}}}function Ht(_){let l,f;return l=new B({props:{code:"dHJsJTIwZHBvJTIwJTVDJTBBJTIwJTIwLS1tb2RlbF9uYW1lX29yX3BhdGglMjBRd2VuJTJGUXdlbjIuNS0wLjVCJTIwJTVDJTBBJTIwJTIwLS1kYXRhc2V0X25hbWUlMjBhbnRocm9waWMlMkZoaC1ybGhmJTIwJTVDJTBBJTIwJTIwLS1udW1fcHJvY2Vzc2VzJTIwNA==",highlighted:`trl dpo \\
  --model_name_or_path Qwen/Qwen2.5-0.5B \\
  --dataset_name anthropic/hh-rlhf \\
  --num_processes 4`,wrap:!1}}),{c(){d(l.$$.fragment)},l(t){g(l.$$.fragment,t)},m(t,u){w(l,t,u),f=!0},p:U,i(t){f||(h(l.$$.fragment,t),f=!0)},o(t){T(l.$$.fragment,t),f=!1},d(t){J(l,t)}}}function Qt(_){let l,f,t,u="Launch with:",i,m,$;return l=new B({props:{code:"JTIzJTIwZHBvX2NvbmZpZy55YW1sJTBBbW9kZWxfbmFtZV9vcl9wYXRoJTNBJTIwUXdlbiUyRlF3ZW4yLjUtMC41QiUwQWRhdGFzZXRfbmFtZSUzQSUyMGFudGhyb3BpYyUyRmhoLXJsaGYlMEFudW1fcHJvY2Vzc2VzJTNBJTIwNA==",highlighted:`<span class="hljs-comment"># dpo_config.yaml</span>
<span class="hljs-attr">model_name_or_path:</span> <span class="hljs-string">Qwen/Qwen2.5-0.5B</span>
<span class="hljs-attr">dataset_name:</span> <span class="hljs-string">anthropic/hh-rlhf</span>
<span class="hljs-attr">num_processes:</span> <span class="hljs-number">4</span>`,wrap:!1}}),m=new B({props:{code:"dHJsJTIwZHBvJTIwLS1jb25maWclMjBkcG9fY29uZmlnLnlhbWw=",highlighted:"trl dpo --config dpo_config.yaml",wrap:!1}}),{c(){d(l.$$.fragment),f=p(),t=b("p"),t.textContent=u,i=p(),d(m.$$.fragment)},l(n){g(l.$$.fragment,n),f=c(n),t=y(n,"P",{"data-svelte-h":!0}),I(t)!=="svelte-ge9qnz"&&(t.textContent=u),i=c(n),g(m.$$.fragment,n)},m(n,a){w(l,n,a),r(n,f,a),r(n,t,a),r(n,i,a),w(m,n,a),$=!0},p:U,i(n){$||(h(l.$$.fragment,n),h(m.$$.fragment,n),$=!0)},o(n){T(l.$$.fragment,n),T(m.$$.fragment,n),$=!1},d(n){n&&(o(f),o(t),o(i)),J(l,n),J(m,n)}}}function kt(_){let l,f,t,u,i,m,$,n;return l=new v({props:{id:"launch_args",option:"SFT inline",$$slots:{default:[Nt]},$$scope:{ctx:_}}}),t=new v({props:{id:"launch_args",option:"SFT w/ config file",$$slots:{default:[zt]},$$scope:{ctx:_}}}),i=new v({props:{id:"launch_args",option:"DPO inline",$$slots:{default:[Ht]},$$scope:{ctx:_}}}),$=new v({props:{id:"launch_args",option:"DPO w/ config file",$$slots:{default:[Qt]},$$scope:{ctx:_}}}),{c(){d(l.$$.fragment),f=p(),d(t.$$.fragment),u=p(),d(i.$$.fragment),m=p(),d($.$$.fragment)},l(a){g(l.$$.fragment,a),f=c(a),g(t.$$.fragment,a),u=c(a),g(i.$$.fragment,a),m=c(a),g($.$$.fragment,a)},m(a,M){w(l,a,M),r(a,f,M),w(t,a,M),r(a,u,M),w(i,a,M),r(a,m,M),w($,a,M),n=!0},p(a,M){const S={};M&2&&(S.$$scope={dirty:M,ctx:a}),l.$set(S);const L={};M&2&&(L.$$scope={dirty:M,ctx:a}),t.$set(L);const j={};M&2&&(j.$$scope={dirty:M,ctx:a}),i.$set(j);const C={};M&2&&(C.$$scope={dirty:M,ctx:a}),$.$set(C)},i(a){n||(h(l.$$.fragment,a),h(t.$$.fragment,a),h(i.$$.fragment,a),h($.$$.fragment,a),n=!0)},o(a){T(l.$$.fragment,a),T(t.$$.fragment,a),T(i.$$.fragment,a),T($.$$.fragment,a),n=!1},d(a){a&&(o(f),o(u),o(m)),J(l,a),J(t,a),J(i,a),J($,a)}}}function At(_){let l,f;return l=new B({props:{code:"dHJsJTIwc2Z0JTIwJTVDJTBBJTIwJTIwLS1tb2RlbF9uYW1lX29yX3BhdGglMjBRd2VuJTJGUXdlbjIuNS0wLjVCJTIwJTVDJTBBJTIwJTIwLS1kYXRhc2V0X25hbWUlMjBzdGFuZm9yZG5scCUyRmltZGIlMjAlNUMlMEElMjAlMjAtLWFjY2VsZXJhdGVfY29uZmlnJTIwemVybzIlMjAlMjAlMjMlMjBvciUyMHBhdGglMkZ0byUyRm15JTJGYWNjZWxlcmF0ZSUyRmNvbmZpZy55YW1s",highlighted:`trl sft \\
  --model_name_or_path Qwen/Qwen2.5-0.5B \\
  --dataset_name stanfordnlp/imdb \\
  --accelerate_config zero2  <span class="hljs-comment"># or path/to/my/accelerate/config.yaml</span>`,wrap:!1}}),{c(){d(l.$$.fragment)},l(t){g(l.$$.fragment,t)},m(t,u){w(l,t,u),f=!0},p:U,i(t){f||(h(l.$$.fragment,t),f=!0)},o(t){T(l.$$.fragment,t),f=!1},d(t){J(l,t)}}}function Pt(_){let l,f,t,u="Launch with:",i,m,$;return l=new B({props:{code:"JTIzJTIwc2Z0X2NvbmZpZy55YW1sJTBBbW9kZWxfbmFtZV9vcl9wYXRoJTNBJTIwUXdlbiUyRlF3ZW4yLjUtMC41QiUwQWRhdGFzZXRfbmFtZSUzQSUyMHN0YW5mb3JkbmxwJTJGaW1kYiUwQWFjY2VsZXJhdGVfY29uZmlnJTNBJTIwemVybzIlMjAlMjAlMjMlMjBvciUyMHBhdGglMkZ0byUyRm15JTJGYWNjZWxlcmF0ZSUyRmNvbmZpZy55YW1s",highlighted:`<span class="hljs-comment"># sft_config.yaml</span>
<span class="hljs-attr">model_name_or_path:</span> <span class="hljs-string">Qwen/Qwen2.5-0.5B</span>
<span class="hljs-attr">dataset_name:</span> <span class="hljs-string">stanfordnlp/imdb</span>
<span class="hljs-attr">accelerate_config:</span> <span class="hljs-string">zero2</span>  <span class="hljs-comment"># or path/to/my/accelerate/config.yaml</span>`,wrap:!1}}),m=new B({props:{code:"dHJsJTIwc2Z0JTIwLS1jb25maWclMjBzZnRfY29uZmlnLnlhbWw=",highlighted:"trl sft --config sft_config.yaml",wrap:!1}}),{c(){d(l.$$.fragment),f=p(),t=b("p"),t.textContent=u,i=p(),d(m.$$.fragment)},l(n){g(l.$$.fragment,n),f=c(n),t=y(n,"P",{"data-svelte-h":!0}),I(t)!=="svelte-ge9qnz"&&(t.textContent=u),i=c(n),g(m.$$.fragment,n)},m(n,a){w(l,n,a),r(n,f,a),r(n,t,a),r(n,i,a),w(m,n,a),$=!0},p:U,i(n){$||(h(l.$$.fragment,n),h(m.$$.fragment,n),$=!0)},o(n){T(l.$$.fragment,n),T(m.$$.fragment,n),$=!1},d(n){n&&(o(f),o(t),o(i)),J(l,n),J(m,n)}}}function Dt(_){let l,f;return l=new B({props:{code:"dHJsJTIwZHBvJTIwJTVDJTBBJTIwJTIwLS1tb2RlbF9uYW1lX29yX3BhdGglMjBRd2VuJTJGUXdlbjIuNS0wLjVCJTIwJTVDJTBBJTIwJTIwLS1kYXRhc2V0X25hbWUlMjBhbnRocm9waWMlMkZoaC1ybGhmJTIwJTVDJTBBJTIwJTIwLS1hY2NlbGVyYXRlX2NvbmZpZyUyMHplcm8yJTIwJTIwJTIzJTIwb3IlMjBwYXRoJTJGdG8lMkZteSUyRmFjY2VsZXJhdGUlMkZjb25maWcueWFtbA==",highlighted:`trl dpo \\
  --model_name_or_path Qwen/Qwen2.5-0.5B \\
  --dataset_name anthropic/hh-rlhf \\
  --accelerate_config zero2  <span class="hljs-comment"># or path/to/my/accelerate/config.yaml</span>`,wrap:!1}}),{c(){d(l.$$.fragment)},l(t){g(l.$$.fragment,t)},m(t,u){w(l,t,u),f=!0},p:U,i(t){f||(h(l.$$.fragment,t),f=!0)},o(t){T(l.$$.fragment,t),f=!1},d(t){J(l,t)}}}function Ot(_){let l,f,t,u="Launch with:",i,m,$;return l=new B({props:{code:"JTIzJTIwZHBvX2NvbmZpZy55YW1sJTBBbW9kZWxfbmFtZV9vcl9wYXRoJTNBJTIwUXdlbiUyRlF3ZW4yLjUtMC41QiUwQWRhdGFzZXRfbmFtZSUzQSUyMGFudGhyb3BpYyUyRmhoLXJsaGYlMEFhY2NlbGVyYXRlX2NvbmZpZyUzQSUyMHplcm8yJTIwJTIwJTIzJTIwb3IlMjBwYXRoJTJGdG8lMkZteSUyRmFjY2VsZXJhdGUlMkZjb25maWcueWFtbA==",highlighted:`<span class="hljs-comment"># dpo_config.yaml</span>
<span class="hljs-attr">model_name_or_path:</span> <span class="hljs-string">Qwen/Qwen2.5-0.5B</span>
<span class="hljs-attr">dataset_name:</span> <span class="hljs-string">anthropic/hh-rlhf</span>
<span class="hljs-attr">accelerate_config:</span> <span class="hljs-string">zero2</span>  <span class="hljs-comment"># or path/to/my/accelerate/config.yaml</span>`,wrap:!1}}),m=new B({props:{code:"dHJsJTIwZHBvJTIwLS1jb25maWclMjBkcG9fY29uZmlnLnlhbWw=",highlighted:"trl dpo --config dpo_config.yaml",wrap:!1}}),{c(){d(l.$$.fragment),f=p(),t=b("p"),t.textContent=u,i=p(),d(m.$$.fragment)},l(n){g(l.$$.fragment,n),f=c(n),t=y(n,"P",{"data-svelte-h":!0}),I(t)!=="svelte-ge9qnz"&&(t.textContent=u),i=c(n),g(m.$$.fragment,n)},m(n,a){w(l,n,a),r(n,f,a),r(n,t,a),r(n,i,a),w(m,n,a),$=!0},p:U,i(n){$||(h(l.$$.fragment,n),h(m.$$.fragment,n),$=!0)},o(n){T(l.$$.fragment,n),T(m.$$.fragment,n),$=!1},d(n){n&&(o(f),o(t),o(i)),J(l,n),J(m,n)}}}function qt(_){let l,f,t,u,i,m,$,n;return l=new v({props:{id:"accelerate_config",option:"SFT inline",$$slots:{default:[At]},$$scope:{ctx:_}}}),t=new v({props:{id:"accelerate_config",option:"SFT w/ config file",$$slots:{default:[Pt]},$$scope:{ctx:_}}}),i=new v({props:{id:"accelerate_config",option:"DPO inline",$$slots:{default:[Dt]},$$scope:{ctx:_}}}),$=new v({props:{id:"accelerate_config",option:"DPO w/ config file",$$slots:{default:[Ot]},$$scope:{ctx:_}}}),{c(){d(l.$$.fragment),f=p(),d(t.$$.fragment),u=p(),d(i.$$.fragment),m=p(),d($.$$.fragment)},l(a){g(l.$$.fragment,a),f=c(a),g(t.$$.fragment,a),u=c(a),g(i.$$.fragment,a),m=c(a),g($.$$.fragment,a)},m(a,M){w(l,a,M),r(a,f,M),w(t,a,M),r(a,u,M),w(i,a,M),r(a,m,M),w($,a,M),n=!0},p(a,M){const S={};M&2&&(S.$$scope={dirty:M,ctx:a}),l.$set(S);const L={};M&2&&(L.$$scope={dirty:M,ctx:a}),t.$set(L);const j={};M&2&&(j.$$scope={dirty:M,ctx:a}),i.$set(j);const C={};M&2&&(C.$$scope={dirty:M,ctx:a}),$.$set(C)},i(a){n||(h(l.$$.fragment,a),h(t.$$.fragment,a),h(i.$$.fragment,a),h($.$$.fragment,a),n=!0)},o(a){T(l.$$.fragment,a),T(t.$$.fragment,a),T(i.$$.fragment,a),T($.$$.fragment,a),n=!1},d(a){a&&(o(f),o(u),o(m)),J(l,a),J(t,a),J(i,a),J($,a)}}}function Kt(_){let l,f='The chat interface is deprecated and will be removed in TRL 0.19. Use <code>transformers-cli chat</code> instead. For more information, see the <a href="https://huggingface.co/docs/transformers/quicktour#chat-with-text-generation-models" rel="nofollow">Transformers documentation, chat with text generation models</a>.';return{c(){l=b("p"),l.innerHTML=f},l(t){l=y(t,"P",{"data-svelte-h":!0}),I(l)!=="svelte-19igv4f"&&(l.innerHTML=f)},m(t,u){r(t,l,u)},p:U,d(t){t&&o(l)}}}function en(_){let l,f,t,u,i,m,$,n="TRL provides a powerful command-line interface (CLI) to fine-tune large language models (LLMs) using methods like Supervised Fine-Tuning (SFT), Direct Preference Optimization (DPO), and more. The CLI abstracts away much of the boilerplate, letting you launch training jobs quickly and reproducibly.",a,M,S="Currently supported commands are:",L,j,C,V,lt="<li><code>trl dpo</code>: fine-tune a LLM with DPO</li> <li><code>trl grpo</code>: fine-tune a LLM with GRPO</li> <li><code>trl kto</code>: fine-tune a LLM with KTO</li> <li><code>trl sft</code>: fine-tune a LLM with SFT</li>",he,E,Te,X,at="<li><code>trl env</code>: get the system information</li> <li><code>trl vllm-serve</code>: serve a model with vLLM</li>",Je,F,Me,N,_e,z,st="You can launch training directly from the CLI by specifying required arguments like the model and dataset:",be,W,ye,H,Ie,Q,it="To keep your CLI commands clean and reproducible, you can define all training arguments in a YAML configuration file:",Be,R,je,k,Ue,A,ot='TRL CLI natively supports <a href="https://huggingface.co/docs/accelerate" rel="nofollow">🤗 Accelerate</a>, making it easy to scale training across multiple GPUs, machines, or use advanced setups like DeepSpeed — all from the same CLI.',Ze,P,rt='You can pass any <code>accelerate launch</code> arguments directly to <code>trl</code>, such as <code>--num_processes</code>. For more information see <a href="https://huggingface.co/docs/accelerate/en/basic_tutorials/launch#using-accelerate-launch" rel="nofollow">Using accelerate launch</a>.',ve,x,Le,D,Ce,O,ft='The <code>--accelerate_config</code> flag lets you easily configure distributed training with <a href="https://github.com/huggingface/accelerate" rel="nofollow">🤗 Accelerate</a>. This flag accepts either:',Se,q,mt="<li>the name of a predefined config profile (built into TRL), or</li> <li>a path to a custom Accelerate YAML config file.</li>",We,K,Re,ee,pt="TRL provides several ready-to-use Accelerate configs to simplify common training setups:",xe,te,ct="<thead><tr><th>Name</th> <th>Description</th></tr></thead> <tbody><tr><td><code>fsdp1</code></td> <td>Fully Sharded Data Parallel Stage 1</td></tr> <tr><td><code>fsdp2</code></td> <td>Fully Sharded Data Parallel Stage 2</td></tr> <tr><td><code>zero1</code></td> <td>DeepSpeed ZeRO Stage 1</td></tr> <tr><td><code>zero2</code></td> <td>DeepSpeed ZeRO Stage 2</td></tr> <tr><td><code>zero3</code></td> <td>DeepSpeed ZeRO Stage 3</td></tr> <tr><td><code>multi_gpu</code></td> <td>Multi-GPU training</td></tr> <tr><td><code>single_gpu</code></td> <td>Single-GPU training</td></tr></tbody>",Ge,ne,$t="To use one of these, just pass the name to <code>--accelerate_config</code>. TRL will automatically load the corresponding config file from <code>trl/accelerate_config/</code>.",Ye,le,Ve,G,Ee,ae,Xe,Y,Fe,se,ut="The chat CLI lets you quickly load the model and talk to it. Simply run the following:",Ne,ie,dt=`<code>$ trl chat --model_name_or_path Qwen/Qwen1.5-0.5B-Chat 
<strong><span style="color: red;">&lt;quentin_gallouedec&gt;:</span></strong>
What is the best programming language?

<strong><span style="color: blue;">&lt;Qwen/Qwen1.5-0.5B-Chat&gt;:</span></strong>
There isn&#39;t a &quot;best&quot; programming language, as everyone has different style preferences, needs, and preferences. However, some people commonly use   
languages like Python, Java, C++, and JavaScript, which are popular among developers for a variety of reasons, including readability, flexibility,  
and scalability. Ultimately, it depends on personal preference, needs, and goals.
</code>`,ze,oe,gt='Note that the chat interface relies on the tokenizer’s <a href="https://huggingface.co/docs/transformers/chat_templating" rel="nofollow">chat template</a> to format the inputs for the model. Make sure your tokenizer has a chat template defined.',He,re,wt="Besides talking to the model there are a few commands you can use:",Qe,fe,ht="<li><code>clear</code>: clears the current conversation and start a new one</li> <li><code>example {NAME}</code>: load example named <code>{NAME}</code> from the config and use it as the user input</li> <li><code>set {SETTING_NAME}={SETTING_VALUE};</code>: change the system prompt or generation settings (multiple settings are separated by a <code>;</code>).</li> <li><code>reset</code>: same as clear but also resets the generation configs to defaults if they have been changed by <code>set</code></li> <li><code>save</code> or <code>save {SAVE_NAME}</code>: save the current chat and settings to file by default to <code>./chat_history/{MODEL_NAME}/chat_{DATETIME}.yaml</code> or <code>{SAVE_NAME}</code> if provided</li> <li><code>exit</code>: closes the interface</li>",ke,me,Ae,pe,Tt="You can get the system information by running the following 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the following information when reporting an issue:

- Platform: Linux-5.15.0-1048-aws-x86_64-with-glibc2.31
- Python version: 3.11.9
- PyTorch version: 2.4.1
- CUDA device: NVIDIA H100 80GB HBM3
- Transformers version: 4.45.0.dev0
- Accelerate version: 0.34.2
- Accelerate config: 
  - compute_environment: LOCAL_MACHINE
  - distributed_type: DEEPSPEED
  - mixed_precision: no
  - use_cpu: False
  - debug: False
  - num_processes: 4
  - machine_rank: 0
  - num_machines: 1
  - rdzv_backend: static
  - same_network: True
  - main_training_function: main
  - enable_cpu_affinity: False
  - deepspeed_config: {&#x27;gradient_accumulation_steps&#x27;: 4, &#x27;offload_optimizer_device&#x27;: &#x27;none&#x27;, &#x27;offload_param_device&#x27;: &#x27;none&#x27;, &#x27;zero3_init_flag&#x27;: False, &#x27;zero_stage&#x27;: 2}
  - downcast_bf16: no
  - tpu_use_cluster: False
  - tpu_use_sudo: False
  - tpu_env: []
- Datasets version: 3.0.0
- HF Hub version: 0.24.7
- TRL version: 0.12.0.dev0+acb4d70
- bitsandbytes version: 0.41.1
- DeepSpeed version: 0.15.1
- Diffusers version: 0.30.3
- Liger-Kernel version: 0.3.0
- LLM-Blender version: 0.0.2
- OpenAI version: 1.46.0
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