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<div id="3fe31229-8f6b-48bc-a86d-af8e5466d11c" class="cell" data-scrolled="true" data-execution_count="1">
<div class="sourceCode cell-code" id="cb1"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb1-1"><a href="#cb1-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Check if GPU is available I used 4x NVIDIA GeForce RTX 3090 (rented 2.1.2-cuda12.1-cudnn8-devel)</span></span>
<span id="cb1-2"><a href="#cb1-2" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> torch</span>
<span id="cb1-3"><a href="#cb1-3" aria-hidden="true" tabindex="-1"></a><span class="bu">print</span>(<span class="st">'GPU available?'</span>, torch.cuda.is_available())</span>
<span id="cb1-4"><a href="#cb1-4" aria-hidden="true" tabindex="-1"></a><span class="bu">print</span>(<span class="st">'BF16 is supported?'</span>, torch.cuda.is_bf16_supported())</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
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<pre><code>GPU available? True
BF16 is supported? True</code></pre>
</div>
</div>
<div id="1dee845b-f3cb-4b1e-bdd9-1a918eac140b" class="cell" data-execution_count="2">
<div class="sourceCode cell-code" id="cb3"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb3-1"><a href="#cb3-1" aria-hidden="true" tabindex="-1"></a><span class="op">!</span>pip install huggingface_hub</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
<div class="cell-output cell-output-stdout">
<pre><code>Collecting huggingface_hub
Downloading huggingface_hub-0.20.1-py3-none-any.whl.metadata (12 kB)
Requirement already satisfied: filelock in /opt/conda/lib/python3.10/site-packages (from huggingface_hub) (3.9.0)
Requirement already satisfied: fsspec&gt;=2023.5.0 in /opt/conda/lib/python3.10/site-packages (from huggingface_hub) (2023.10.0)
Requirement already satisfied: requests in /opt/conda/lib/python3.10/site-packages (from huggingface_hub) (2.31.0)
Requirement already satisfied: tqdm&gt;=4.42.1 in /opt/conda/lib/python3.10/site-packages (from huggingface_hub) (4.65.0)
Requirement already satisfied: pyyaml&gt;=5.1 in /opt/conda/lib/python3.10/site-packages (from huggingface_hub) (6.0.1)
Requirement already satisfied: typing-extensions&gt;=3.7.4.3 in /opt/conda/lib/python3.10/site-packages (from huggingface_hub) (4.7.1)
Requirement already satisfied: packaging&gt;=20.9 in /opt/conda/lib/python3.10/site-packages (from huggingface_hub) (23.1)
Requirement already satisfied: charset-normalizer&lt;4,&gt;=2 in /opt/conda/lib/python3.10/site-packages (from requests-&gt;huggingface_hub) (2.0.4)
Requirement already satisfied: idna&lt;4,&gt;=2.5 in /opt/conda/lib/python3.10/site-packages (from requests-&gt;huggingface_hub) (3.4)
Requirement already satisfied: urllib3&lt;3,&gt;=1.21.1 in /opt/conda/lib/python3.10/site-packages (from requests-&gt;huggingface_hub) (1.26.18)
Requirement already satisfied: certifi&gt;=2017.4.17 in /opt/conda/lib/python3.10/site-packages (from requests-&gt;huggingface_hub) (2023.7.22)
Downloading huggingface_hub-0.20.1-py3-none-any.whl (330 kB)
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 330.1/330.1 kB 8.8 MB/s eta 0:00:00:00:01
Installing collected packages: huggingface_hub
Successfully installed huggingface_hub-0.20.1
WARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv</code></pre>
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<div id="88731672-9050-4034-8266-11aaace2a44e" class="cell" data-execution_count="4">
<div class="sourceCode cell-code" id="cb5"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb5-1"><a href="#cb5-1" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> huggingface_hub <span class="im">import</span> notebook_login</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</div>
<div id="6b5aa7d7-3b18-4c14-afd4-043c2c545259" class="cell" data-execution_count="5">
<div class="sourceCode cell-code" id="cb6"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb6-1"><a href="#cb6-1" aria-hidden="true" tabindex="-1"></a><span class="co">#Login to huggingface so you can push the model to hub later</span></span>
<span id="cb6-2"><a href="#cb6-2" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> sys</span>
<span id="cb6-3"><a href="#cb6-3" aria-hidden="true" tabindex="-1"></a>stdout <span class="op">=</span> sys.stdout</span>
<span id="cb6-4"><a href="#cb6-4" aria-hidden="true" tabindex="-1"></a>notebook_login()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
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<script type="application/vnd.jupyter.widget-view+json">
{"model_id":"60df98d7b0294289aad8b6c8cd023c3b","version_major":2,"version_minor":0,"quarto_mimetype":"application/vnd.jupyter.widget-view+json"}
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<div id="b74d0635-5033-4494-b7bd-ff6822103d93" class="cell" data-execution_count="6">
<div class="sourceCode cell-code" id="cb7"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb7-1"><a href="#cb7-1" aria-hidden="true" tabindex="-1"></a><span class="co">#I noticed that when you use notebook_login() nothing gets printed after so we use sys </span></span>
<span id="cb7-2"><a href="#cb7-2" aria-hidden="true" tabindex="-1"></a>sys.stdout <span class="op">=</span> stdout</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
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<div id="e3c3b088-45e7-484b-ae39-66beabc48da8" class="cell" data-execution_count="7">
<div class="sourceCode cell-code" id="cb8"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb8-1"><a href="#cb8-1" aria-hidden="true" tabindex="-1"></a><span class="co">#axolotl</span></span>
<span id="cb8-2"><a href="#cb8-2" aria-hidden="true" tabindex="-1"></a><span class="op">!</span>git clone <span class="op">-</span>b main <span class="op">--</span>depth <span class="dv">1</span> https:<span class="op">//</span>github.com<span class="op">/</span>OpenAccess<span class="op">-</span>AI<span class="op">-</span>Collective<span class="op">/</span>axolotl</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
<div class="cell-output cell-output-stdout">
<pre><code>Cloning into 'axolotl'...
remote: Enumerating objects: 235, done.
remote: Counting objects: 100% (235/235), done.
remote: Compressing objects: 100% (207/207), done.
remote: Total 235 (delta 48), reused 123 (delta 13), pack-reused 0
Receiving objects: 100% (235/235), 1.46 MiB | 11.65 MiB/s, done.
Resolving deltas: 100% (48/48), done.</code></pre>
</div>
</div>
<div id="66927751-4fd6-4477-97fc-6ab08c9d9a74" class="cell" data-execution_count="8">
<div class="sourceCode cell-code" id="cb10"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb10-1"><a href="#cb10-1" aria-hidden="true" tabindex="-1"></a>cd axolotl</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
<div class="cell-output cell-output-stdout">
<pre><code>/axolotl</code></pre>
</div>
</div>
<div id="fcccf8da-353b-4d70-8f55-5cfe08c7e6b9" class="cell" data-execution_count="9">
<div class="sourceCode cell-code" id="cb12"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb12-1"><a href="#cb12-1" aria-hidden="true" tabindex="-1"></a><span class="co">#instaling what is needed inside axolotl file</span></span>
<span id="cb12-2"><a href="#cb12-2" aria-hidden="true" tabindex="-1"></a><span class="op">!</span>pip install packaging</span>
<span id="cb12-3"><a href="#cb12-3" aria-hidden="true" tabindex="-1"></a><span class="op">!</span>pip install <span class="op">-</span>e <span class="st">'.[flash-attn,deepspeed]'</span></span>
<span id="cb12-4"><a href="#cb12-4" aria-hidden="true" tabindex="-1"></a><span class="op">!</span>pip install <span class="op">-</span>U git<span class="op">+</span>https:<span class="op">//</span>github.com<span class="op">/</span>huggingface<span class="op">/</span>peft.git</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
<div class="cell-output cell-output-stdout">
<pre><code>Requirement already satisfied: packaging in /opt/conda/lib/python3.10/site-packages (23.1)
WARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv
Obtaining file:///axolotl
Preparing metadata (setup.py) ... done
Collecting auto-gptq==0.5.1
Downloading auto_gptq-0.5.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (20 kB)
Requirement already satisfied: packaging in /opt/conda/lib/python3.10/site-packages (23.1)
Collecting peft==0.6.0
Downloading peft-0.6.0-py3-none-any.whl.metadata (23 kB)
Collecting transformers==4.36.2
Downloading transformers-4.36.2-py3-none-any.whl.metadata (126 kB)
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 126.8/126.8 kB 9.1 MB/s eta 0:00:00
Collecting tokenizers==0.15.0
Downloading tokenizers-0.15.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (6.7 kB)
Collecting bitsandbytes&gt;=0.41.1
Downloading bitsandbytes-0.41.3.post2-py3-none-any.whl.metadata (9.8 kB)
Collecting accelerate==0.24.1
Downloading accelerate-0.24.1-py3-none-any.whl.metadata (18 kB)
Collecting addict
Downloading addict-2.4.0-py3-none-any.whl (3.8 kB)
Collecting fire
Downloading fire-0.5.0.tar.gz (88 kB)
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Preparing metadata (setup.py) ... done
Requirement already satisfied: PyYAML&gt;=6.0 in /opt/conda/lib/python3.10/site-packages (6.0.1)
Collecting datasets&gt;=2.15.0
Downloading datasets-2.16.0-py3-none-any.whl.metadata (20 kB)
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Downloading sentencepiece-0.1.99-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.3 MB)
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Building wheels for collected packages: flash-attn, optimum, rouge-score, deepspeed, fire, ffmpy, wavedrom
Building wheel for flash-attn (setup.py) ... done
Created wheel for flash-attn: filename=flash_attn-2.3.3-cp310-cp310-linux_x86_64.whl size=57042553 sha256=b1df92cb5bd7657d38b789dd48e907aa3e0bd2715c817eb85f3c4320bb11fb3f
Stored in directory: /root/.cache/pip/wheels/e5/e6/fa/941802ec61d1afd320d27160ab1db98e6dba65381f84b76d4a
Building wheel for optimum (pyproject.toml) ... done
Created wheel for optimum: filename=optimum-1.13.2-py3-none-any.whl size=395599 sha256=ff3a73120e1b6eeeda28f76e3fc8cd4cd826e5d66c869b7848ba150e7af79c62
Stored in directory: /root/.cache/pip/wheels/6e/b7/2c/79405d98f0943373d8546daeae25a3d377f7659ca0cbe48699
Building wheel for rouge-score (setup.py) ... done
Created wheel for rouge-score: filename=rouge_score-0.1.2-py3-none-any.whl size=24932 sha256=8118ecbbcd3529085e794c803f0ddb182fc6c6d3e8a494103b49a94abf1bec37
Stored in directory: /root/.cache/pip/wheels/5f/dd/89/461065a73be61a532ff8599a28e9beef17985c9e9c31e541b4
Building wheel for deepspeed (setup.py) ... done
Created wheel for deepspeed: filename=deepspeed-0.12.6-py3-none-any.whl size=1306729 sha256=35c46b6f0275b0d3063522e0af4f3cbd9ec1c310114d8917d87cbe2bf43346e2
Stored in directory: /root/.cache/pip/wheels/a3/dc/a2/f585faaed4dec84108916dcc8e8a7c129a216df8202ca32984
Building wheel for fire (setup.py) ... done
Created wheel for fire: filename=fire-0.5.0-py2.py3-none-any.whl size=116934 sha256=e76d5185f237f34ec69bb8aa657497bef07408978e4f7efdaef48663bb8cd4ef
Stored in directory: /root/.cache/pip/wheels/90/d4/f7/9404e5db0116bd4d43e5666eaa3e70ab53723e1e3ea40c9a95
Building wheel for ffmpy (setup.py) ... done
Created wheel for ffmpy: filename=ffmpy-0.3.1-py3-none-any.whl size=5579 sha256=da3b54dc0ac1a825a1a233315970ac80b8b4c53ebd9cb2a2cfdeab118f453a64
Stored in directory: /root/.cache/pip/wheels/01/a6/d1/1c0828c304a4283b2c1639a09ad86f83d7c487ef34c6b4a1bf
Building wheel for wavedrom (setup.py) ... done
Created wheel for wavedrom: filename=wavedrom-2.0.3.post3-py2.py3-none-any.whl size=30052 sha256=7f0cbd15d63ee9c120190bac122ab51bbbfc91ee374bc3c046fadb320816c17e
Stored in directory: /root/.cache/pip/wheels/9c/52/8c/38b454b42f712f325e26f633287484c7dc1ad469e1580c5954
Successfully built flash-attn optimum rouge-score deepspeed fire ffmpy wavedrom
Installing collected packages: sentencepiece, pydub, py-cpuinfo, ninja, nh3, hjson, ffmpy, bitsandbytes, appdirs, addict, xxhash, wrapt, werkzeug, websockets, tzdata, typing-extensions, threadpoolctl, termcolor, tensorboard-data-server, svgwrite, smmap, shortuuid, setproctitle, sentry-sdk, semantic-version, scipy, safetensors, rouge, regex, python-multipart, pyparsing, pynvml, pyasn1, pyarrow-hotfix, pyarrow, protobuf, orjson, oauthlib, multidict, mdurl, markdown2, markdown, llvmlite, kiwisolver, joblib, jmespath, importlib-resources, humanfriendly, hf_transfer, h11, grpcio, google-crc32c, gekko, frozenlist, fonttools, einops, docker-pycreds, dill, cycler, contourpy, colorama, cachetools, async-timeout, art, aioitertools, aiofiles, absl-py, yarl, wavedrom, uvicorn, tiktoken, scikit-learn, rsa, responses, requests-oauthlib, pydantic, pyasn1-modules, pandas, numba, nltk, multiprocess, matplotlib, markdown-it-py, httpcore, googleapis-common-protos, google-resumable-media, gitdb, fire, coloredlogs, botocore, aiosignal, xformers, tokenizers, starlette, rouge-score, rich, httpx, google-auth, GitPython, flash-attn, deepspeed, aiohttp, accelerate, wandb, transformers, gradio-client, google-auth-oauthlib, google-api-core, fastapi, altair, aiobotocore, tensorboard, s3fs, peft, gradio, google-cloud-core, fschat, datasets, bert-score, optimum, google-cloud-storage, evaluate, auto-gptq, gcsfs, axolotl
Attempting uninstall: typing-extensions
Found existing installation: typing_extensions 4.7.1
Uninstalling typing_extensions-4.7.1:
Successfully uninstalled typing_extensions-4.7.1
Running setup.py develop for axolotl
Successfully installed GitPython-3.1.40 absl-py-2.0.0 accelerate-0.24.1 addict-2.4.0 aiobotocore-2.7.0 aiofiles-23.2.1 aiohttp-3.9.1 aioitertools-0.11.0 aiosignal-1.3.1 altair-5.2.0 appdirs-1.4.4 art-6.1 async-timeout-4.0.3 auto-gptq-0.5.1 axolotl-0.3.0 bert-score-0.3.13 bitsandbytes-0.41.3.post2 botocore-1.31.64 cachetools-5.3.2 colorama-0.4.6 coloredlogs-15.0.1 contourpy-1.2.0 cycler-0.12.1 datasets-2.16.0 deepspeed-0.12.6 dill-0.3.7 docker-pycreds-0.4.0 einops-0.7.0 evaluate-0.4.0 fastapi-0.108.0 ffmpy-0.3.1 fire-0.5.0 flash-attn-2.3.3 fonttools-4.47.0 frozenlist-1.4.1 fschat-0.2.34 gcsfs-2023.10.0 gekko-1.0.6 gitdb-4.0.11 google-api-core-2.15.0 google-auth-2.25.2 google-auth-oauthlib-1.2.0 google-cloud-core-2.4.1 google-cloud-storage-2.14.0 google-crc32c-1.5.0 google-resumable-media-2.7.0 googleapis-common-protos-1.62.0 gradio-3.50.2 gradio-client-0.6.1 grpcio-1.60.0 h11-0.14.0 hf_transfer-0.1.4 hjson-3.1.0 httpcore-1.0.2 httpx-0.26.0 humanfriendly-10.0 importlib-resources-6.1.1 jmespath-1.0.1 joblib-1.3.2 kiwisolver-1.4.5 llvmlite-0.41.1 markdown-3.5.1 markdown-it-py-3.0.0 markdown2-2.4.12 matplotlib-3.8.2 mdurl-0.1.2 multidict-6.0.4 multiprocess-0.70.15 nh3-0.2.15 ninja-1.11.1.1 nltk-3.8.1 numba-0.58.1 oauthlib-3.2.2 optimum-1.13.2 orjson-3.9.10 pandas-2.1.4 peft-0.6.0 protobuf-4.23.4 py-cpuinfo-9.0.0 pyarrow-14.0.2 pyarrow-hotfix-0.6 pyasn1-0.5.1 pyasn1-modules-0.3.0 pydantic-1.10.13 pydub-0.25.1 pynvml-11.5.0 pyparsing-3.1.1 python-multipart-0.0.6 regex-2023.12.25 requests-oauthlib-1.3.1 responses-0.18.0 rich-13.7.0 rouge-1.0.1 rouge-score-0.1.2 rsa-4.9 s3fs-2023.10.0 safetensors-0.4.1 scikit-learn-1.2.2 scipy-1.11.4 semantic-version-2.10.0 sentencepiece-0.1.99 sentry-sdk-1.39.1 setproctitle-1.3.3 shortuuid-1.0.11 smmap-5.0.1 starlette-0.32.0.post1 svgwrite-1.4.3 tensorboard-2.15.1 tensorboard-data-server-0.7.2 termcolor-2.4.0 threadpoolctl-3.2.0 tiktoken-0.5.2 tokenizers-0.15.0 transformers-4.36.2 typing-extensions-4.8.0 tzdata-2023.3 uvicorn-0.25.0 wandb-0.16.1 wavedrom-2.0.3.post3 websockets-11.0.3 werkzeug-3.0.1 wrapt-1.16.0 xformers-0.0.23 xxhash-3.4.1 yarl-1.9.4
WARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv
Collecting git+https://github.com/huggingface/peft.git
Cloning https://github.com/huggingface/peft.git to /tmp/pip-req-build-hka8xgk2
Running command git clone --filter=blob:none --quiet https://github.com/huggingface/peft.git /tmp/pip-req-build-hka8xgk2
Resolved https://github.com/huggingface/peft.git to commit cf04d0353f0343cbf66627228c4495f51669af34
Installing build dependencies ... done
Getting requirements to build wheel ... done
Preparing metadata (pyproject.toml) ... done
Requirement already satisfied: numpy&gt;=1.17 in /opt/conda/lib/python3.10/site-packages (from peft==0.7.2.dev0) (1.26.0)
Requirement already satisfied: packaging&gt;=20.0 in /opt/conda/lib/python3.10/site-packages (from peft==0.7.2.dev0) (23.1)
Requirement already satisfied: psutil in /opt/conda/lib/python3.10/site-packages (from peft==0.7.2.dev0) (5.9.0)
Requirement already satisfied: pyyaml in /opt/conda/lib/python3.10/site-packages (from peft==0.7.2.dev0) (6.0.1)
Requirement already satisfied: torch&gt;=1.13.0 in /opt/conda/lib/python3.10/site-packages (from peft==0.7.2.dev0) (2.1.1)
Requirement already satisfied: transformers in /opt/conda/lib/python3.10/site-packages (from peft==0.7.2.dev0) (4.36.2)
Requirement already satisfied: tqdm in /opt/conda/lib/python3.10/site-packages (from peft==0.7.2.dev0) (4.65.0)
Requirement already satisfied: accelerate&gt;=0.21.0 in /opt/conda/lib/python3.10/site-packages (from peft==0.7.2.dev0) (0.24.1)
Requirement already satisfied: safetensors in /opt/conda/lib/python3.10/site-packages (from peft==0.7.2.dev0) (0.4.1)
Requirement already satisfied: huggingface-hub&gt;=0.17.0 in /opt/conda/lib/python3.10/site-packages (from peft==0.7.2.dev0) (0.20.1)
Requirement already satisfied: filelock in /opt/conda/lib/python3.10/site-packages (from huggingface-hub&gt;=0.17.0-&gt;peft==0.7.2.dev0) (3.9.0)
Requirement already satisfied: fsspec&gt;=2023.5.0 in /opt/conda/lib/python3.10/site-packages (from huggingface-hub&gt;=0.17.0-&gt;peft==0.7.2.dev0) (2023.10.0)
Requirement already satisfied: requests in /opt/conda/lib/python3.10/site-packages (from huggingface-hub&gt;=0.17.0-&gt;peft==0.7.2.dev0) (2.31.0)
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Requirement already satisfied: sympy in /opt/conda/lib/python3.10/site-packages (from torch&gt;=1.13.0-&gt;peft==0.7.2.dev0) (1.11.1)
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Requirement already satisfied: MarkupSafe&gt;=2.0 in /opt/conda/lib/python3.10/site-packages (from jinja2-&gt;torch&gt;=1.13.0-&gt;peft==0.7.2.dev0) (2.1.1)
Requirement already satisfied: charset-normalizer&lt;4,&gt;=2 in /opt/conda/lib/python3.10/site-packages (from requests-&gt;huggingface-hub&gt;=0.17.0-&gt;peft==0.7.2.dev0) (2.0.4)
Requirement already satisfied: idna&lt;4,&gt;=2.5 in /opt/conda/lib/python3.10/site-packages (from requests-&gt;huggingface-hub&gt;=0.17.0-&gt;peft==0.7.2.dev0) (3.4)
Requirement already satisfied: urllib3&lt;3,&gt;=1.21.1 in /opt/conda/lib/python3.10/site-packages (from requests-&gt;huggingface-hub&gt;=0.17.0-&gt;peft==0.7.2.dev0) (1.26.18)
Requirement already satisfied: certifi&gt;=2017.4.17 in /opt/conda/lib/python3.10/site-packages (from requests-&gt;huggingface-hub&gt;=0.17.0-&gt;peft==0.7.2.dev0) (2023.7.22)
Requirement already satisfied: mpmath&gt;=0.19 in /opt/conda/lib/python3.10/site-packages (from sympy-&gt;torch&gt;=1.13.0-&gt;peft==0.7.2.dev0) (1.3.0)
Building wheels for collected packages: peft
Building wheel for peft (pyproject.toml) ... done
Created wheel for peft: filename=peft-0.7.2.dev0-py3-none-any.whl size=169456 sha256=4c70d23e759fa6abb3827fb2f3a8683be3b24d78777d0f403bbc2c0548e5dd4b
Stored in directory: /tmp/pip-ephem-wheel-cache-my5ncou6/wheels/d7/c7/de/1368fac8590e1b103ddc2ec2a28ad51d83aded1a3830e8a087
Successfully built peft
Installing collected packages: peft
Attempting uninstall: peft
Found existing installation: peft 0.6.0
Uninstalling peft-0.6.0:
Successfully uninstalled peft-0.6.0
ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.
axolotl 0.3.0 requires peft==0.6.0, but you have peft 0.7.2.dev0 which is incompatible.
Successfully installed peft-0.7.2.dev0
WARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv</code></pre>
</div>
</div>
<div id="82d1a380-1e87-48fe-89fe-25331326014d" class="cell" data-execution_count="16">
<div class="sourceCode cell-code" id="cb14"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb14-1"><a href="#cb14-1" aria-hidden="true" tabindex="-1"></a><span class="co">"""</span></span>
<span id="cb14-2"><a href="#cb14-2" aria-hidden="true" tabindex="-1"></a><span class="co">Training using the config.yml file and using deepspeed:zero3_bf16 the most aggressive optimization out of zero1,zero2,zero3 stages which partitions </span></span>
<span id="cb14-3"><a href="#cb14-3" aria-hidden="true" tabindex="-1"></a><span class="co">not only optimizer states but also gradients and parameters across GPUs. The bf16 indicate mixed precision training using bfloat16.</span></span>
<span id="cb14-4"><a href="#cb14-4" aria-hidden="true" tabindex="-1"></a><span class="co">For more information read axolotl's readme</span></span>
<span id="cb14-5"><a href="#cb14-5" aria-hidden="true" tabindex="-1"></a><span class="co">"""</span></span>
<span id="cb14-6"><a href="#cb14-6" aria-hidden="true" tabindex="-1"></a><span class="op">!</span>accelerate launch <span class="op">-</span>m axolotl.cli.train <span class="op">/</span>folder<span class="op">/</span>config.yml <span class="op">--</span>deepspeed deepspeed_configs<span class="op">/</span>zero3_bf16.json</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
<div class="cell-output cell-output-stdout">
<pre><code>The following values were not passed to `accelerate launch` and had defaults used instead:
`--num_processes` was set to a value of `3`
More than one GPU was found, enabling multi-GPU training.
If this was unintended please pass in `--num_processes=1`.
`--num_machines` was set to a value of `1`
`--mixed_precision` was set to a value of `'no'`
`--dynamo_backend` was set to a value of `'no'`
To avoid this warning pass in values for each of the problematic parameters or run `accelerate config`.
/opt/conda/lib/python3.10/site-packages/transformers/deepspeed.py:23: FutureWarning: transformers.deepspeed module is deprecated and will be removed in a future version. Please import deepspeed modules directly from transformers.integrations
warnings.warn(
[2023-12-28 15:44:09,979] [INFO] [datasets.&lt;module&gt;:58] [PID:2814] PyTorch version 2.1.1 available.
/opt/conda/lib/python3.10/site-packages/transformers/deepspeed.py:23: FutureWarning: transformers.deepspeed module is deprecated and will be removed in a future version. Please import deepspeed modules directly from transformers.integrations
warnings.warn(
/opt/conda/lib/python3.10/site-packages/transformers/deepspeed.py:23: FutureWarning: transformers.deepspeed module is deprecated and will be removed in a future version. Please import deepspeed modules directly from transformers.integrations
warnings.warn(
[2023-12-28 15:44:10,011] [INFO] [datasets.&lt;module&gt;:58] [PID:2812] PyTorch version 2.1.1 available.
[2023-12-28 15:44:10,013] [INFO] [datasets.&lt;module&gt;:58] [PID:2813] PyTorch version 2.1.1 available.
[2023-12-28 15:44:10,805] [INFO] [axolotl.normalize_config:150] [PID:2814] [RANK:2] GPU memory usage baseline: 0.000GB (+0.317GB misc)
[2023-12-28 15:44:10,830] [INFO] [real_accelerator.py:161:get_accelerator] Setting ds_accelerator to cuda (auto detect)
[2023-12-28 15:44:10,842] [INFO] [axolotl.normalize_config:150] [PID:2813] [RANK:1] GPU memory usage baseline: 0.000GB (+0.317GB misc)
[2023-12-28 15:44:10,865] [INFO] [real_accelerator.py:161:get_accelerator] Setting ds_accelerator to cuda (auto detect)
[2023-12-28 15:44:10,869] [INFO] [axolotl.normalize_config:150] [PID:2812] [RANK:0] GPU memory usage baseline: 0.000GB (+0.351GB misc)
[2023-12-28 15:44:10,887] [INFO] [real_accelerator.py:161:get_accelerator] Setting ds_accelerator to cuda (auto detect)
[2023-12-28 15:44:10,961] [INFO] [comm.py:637:init_distributed] cdb=None
[2023-12-28 15:44:10,994] [INFO] [comm.py:637:init_distributed] cdb=None
[2023-12-28 15:44:11,015] [INFO] [comm.py:637:init_distributed] cdb=None
[2023-12-28 15:44:11,015] [INFO] [comm.py:668:init_distributed] Initializing TorchBackend in DeepSpeed with backend nccl
dP dP dP
88 88 88
.d8888b. dP. .dP .d8888b. 88 .d8888b. d8888P 88
88' `88 `8bd8' 88' `88 88 88' `88 88 88
88. .88 .d88b. 88. .88 88 88. .88 88 88
`88888P8 dP' `dP `88888P' dP `88888P' dP dP
[2023-12-28 15:44:11,412] [DEBUG] [axolotl.load_tokenizer:184] [PID:2812] [RANK:0] EOS: 2 / &lt;/s&gt;
[2023-12-28 15:44:11,412] [DEBUG] [axolotl.load_tokenizer:185] [PID:2812] [RANK:0] BOS: 1 / &lt;s&gt;
[2023-12-28 15:44:11,412] [DEBUG] [axolotl.load_tokenizer:186] [PID:2812] [RANK:0] PAD: 2 / &lt;/s&gt;
[2023-12-28 15:44:11,412] [DEBUG] [axolotl.load_tokenizer:187] [PID:2812] [RANK:0] UNK: 0 / &lt;unk&gt;
[2023-12-28 15:44:11,413] [INFO] [axolotl.load_tokenized_prepared_datasets:143] [PID:2812] [RANK:0] Loading prepared dataset from disk at tilemachos/GF_new.json/1adc45d2edc1e98ce657814412c6593c...
[2023-12-28 15:44:11,415] [INFO] [axolotl.load_tokenized_prepared_datasets:145] [PID:2812] [RANK:0] Prepared dataset loaded from disk...
[2023-12-28 15:44:11,432] [DEBUG] [axolotl.load_tokenizer:184] [PID:2814] [RANK:2] EOS: 2 / &lt;/s&gt;
[2023-12-28 15:44:11,432] [DEBUG] [axolotl.load_tokenizer:185] [PID:2814] [RANK:2] BOS: 1 / &lt;s&gt;
[2023-12-28 15:44:11,432] [DEBUG] [axolotl.load_tokenizer:186] [PID:2814] [RANK:2] PAD: 2 / &lt;/s&gt;
[2023-12-28 15:44:11,432] [DEBUG] [axolotl.load_tokenizer:187] [PID:2814] [RANK:2] UNK: 0 / &lt;unk&gt;
[2023-12-28 15:44:11,530] [DEBUG] [axolotl.load_tokenizer:184] [PID:2813] [RANK:1] EOS: 2 / &lt;/s&gt;
[2023-12-28 15:44:11,531] [DEBUG] [axolotl.load_tokenizer:185] [PID:2813] [RANK:1] BOS: 1 / &lt;s&gt;
[2023-12-28 15:44:11,531] [DEBUG] [axolotl.load_tokenizer:186] [PID:2813] [RANK:1] PAD: 2 / &lt;/s&gt;
[2023-12-28 15:44:11,531] [DEBUG] [axolotl.load_tokenizer:187] [PID:2813] [RANK:1] UNK: 0 / &lt;unk&gt;
[2023-12-28 15:44:12,158] [INFO] [axolotl.load_tokenized_prepared_datasets:143] [PID:2813] [RANK:1] Loading prepared dataset from disk at tilemachos/GF_new.json/1adc45d2edc1e98ce657814412c6593c...
[2023-12-28 15:44:12,158] [INFO] [axolotl.load_tokenized_prepared_datasets:143] [PID:2814] [RANK:2] Loading prepared dataset from disk at tilemachos/GF_new.json/1adc45d2edc1e98ce657814412c6593c...
[2023-12-28 15:44:12,160] [INFO] [axolotl.load_tokenized_prepared_datasets:145] [PID:2813] [RANK:1] Prepared dataset loaded from disk...
[2023-12-28 15:44:12,161] [INFO] [axolotl.load_tokenized_prepared_datasets:145] [PID:2814] [RANK:2] Prepared dataset loaded from disk...
[2023-12-28 15:44:12,236] [DEBUG] [axolotl.log:60] [PID:2812] [RANK:0] total_num_tokens: 28120
[2023-12-28 15:44:12,238] [DEBUG] [axolotl.log:60] [PID:2812] [RANK:0] `total_supervised_tokens: 7990`
[2023-12-28 15:44:12,238] [DEBUG] [axolotl.log:60] [PID:2812] [RANK:0] total_num_steps: 6
[2023-12-28 15:44:12,242] [DEBUG] [axolotl.train.log:60] [PID:2812] [RANK:0] loading tokenizer... mistralai/Mistral-7B-v0.1
[2023-12-28 15:44:12,518] [DEBUG] [axolotl.load_tokenizer:184] [PID:2812] [RANK:0] EOS: 2 / &lt;/s&gt;
[2023-12-28 15:44:12,518] [DEBUG] [axolotl.load_tokenizer:185] [PID:2812] [RANK:0] BOS: 1 / &lt;s&gt;
[2023-12-28 15:44:12,518] [DEBUG] [axolotl.load_tokenizer:186] [PID:2812] [RANK:0] PAD: 2 / &lt;/s&gt;
[2023-12-28 15:44:12,518] [DEBUG] [axolotl.load_tokenizer:187] [PID:2812] [RANK:0] UNK: 0 / &lt;unk&gt;
[2023-12-28 15:44:12,518] [DEBUG] [axolotl.train.log:60] [PID:2812] [RANK:0] loading model and peft_config...
[2023-12-28 15:44:12,589] [DEBUG] [axolotl.load_tokenizer:184] [PID:2814] [RANK:2] EOS: 2 / &lt;/s&gt;
[2023-12-28 15:44:12,589] [DEBUG] [axolotl.load_tokenizer:185] [PID:2814] [RANK:2] BOS: 1 / &lt;s&gt;
[2023-12-28 15:44:12,589] [DEBUG] [axolotl.load_tokenizer:186] [PID:2814] [RANK:2] PAD: 2 / &lt;/s&gt;
[2023-12-28 15:44:12,589] [DEBUG] [axolotl.load_tokenizer:187] [PID:2814] [RANK:2] UNK: 0 / &lt;unk&gt;
[2023-12-28 15:44:12,599] [DEBUG] [axolotl.load_tokenizer:184] [PID:2813] [RANK:1] EOS: 2 / &lt;/s&gt;
[2023-12-28 15:44:12,599] [DEBUG] [axolotl.load_tokenizer:185] [PID:2813] [RANK:1] BOS: 1 / &lt;s&gt;
[2023-12-28 15:44:12,599] [DEBUG] [axolotl.load_tokenizer:186] [PID:2813] [RANK:1] PAD: 2 / &lt;/s&gt;
[2023-12-28 15:44:12,599] [DEBUG] [axolotl.load_tokenizer:187] [PID:2813] [RANK:1] UNK: 0 / &lt;unk&gt;
[2023-12-28 15:44:13,049] [INFO] [partition_parameters.py:348:__exit__] finished initializing model - num_params = 291, num_elems = 7.24B
Loading checkpoint shards: 100%|██████████████████| 2/2 [00:11&lt;00:00, 5.81s/it]
Loading checkpoint shards: 100%|██████████████████| 2/2 [00:11&lt;00:00, 5.98s/it]
[2023-12-28 15:44:25,395] [INFO] [axolotl.load_model:503] [PID:2813] [RANK:1] GPU memory usage after model load: 7.576GB (+0.524GB cache, +0.708GB misc)
[2023-12-28 15:44:25,399] [INFO] [axolotl.load_model:526] [PID:2813] [RANK:1] converting PEFT model w/ prepare_model_for_kbit_training
[2023-12-28 15:44:25,403] [INFO] [axolotl.load_model:538] [PID:2813] [RANK:1] converting modules to torch.bfloat16 for flash attention
trainable params: 3,407,872 || all params: 7,245,139,968 || trainable%: 0.04703666202518836
[2023-12-28 15:44:25,480] [INFO] [axolotl.load_model:568] [PID:2813] [RANK:1] GPU memory usage after adapters: 7.589GB (+1.501GB cache, +0.708GB misc)
[2023-12-28 15:44:25,572] [INFO] [axolotl.load_model:503] [PID:2814] [RANK:2] GPU memory usage after model load: 7.576GB (+0.410GB cache, +0.708GB misc)
[2023-12-28 15:44:25,576] [INFO] [axolotl.load_model:526] [PID:2814] [RANK:2] converting PEFT model w/ prepare_model_for_kbit_training
[2023-12-28 15:44:25,580] [INFO] [axolotl.load_model:538] [PID:2814] [RANK:2] converting modules to torch.bfloat16 for flash attention
trainable params: 3,407,872 || all params: 7,245,139,968 || trainable%: 0.04703666202518836
[2023-12-28 15:44:25,660] [INFO] [axolotl.load_model:568] [PID:2814] [RANK:2] GPU memory usage after adapters: 7.589GB (+1.388GB cache, +0.708GB misc)
Loading checkpoint shards: 100%|██████████████████| 2/2 [00:12&lt;00:00, 6.30s/it]
[2023-12-28 15:44:26,170] [INFO] [axolotl.load_model:503] [PID:2812] [RANK:0] GPU memory usage after model load: 7.576GB (+0.776GB cache, +0.741GB misc)
[2023-12-28 15:44:26,177] [INFO] [axolotl.load_model:526] [PID:2812] [RANK:0] converting PEFT model w/ prepare_model_for_kbit_training
[2023-12-28 15:44:26,181] [INFO] [axolotl.load_model:538] [PID:2812] [RANK:0] converting modules to torch.bfloat16 for flash attention
trainable params: 3,407,872 || all params: 7,245,139,968 || trainable%: 0.04703666202518836
[2023-12-28 15:44:26,259] [INFO] [axolotl.load_model:568] [PID:2812] [RANK:0] GPU memory usage after adapters: 7.589GB (+1.753GB cache, +0.741GB misc)
[2023-12-28 15:44:26,293] [INFO] [axolotl.train.log:60] [PID:2812] [RANK:0] Pre-saving adapter config to ./out
[2023-12-28 15:44:26,296] [INFO] [axolotl.train.log:60] [PID:2812] [RANK:0] Starting trainer...
Using /root/.cache/torch_extensions/py310_cu121 as PyTorch extensions root...
Using /root/.cache/torch_extensions/py310_cu121 as PyTorch extensions root...
Using /root/.cache/torch_extensions/py310_cu121 as PyTorch extensions root...
Detected CUDA files, patching ldflags
Emitting ninja build file /root/.cache/torch_extensions/py310_cu121/fused_adam/build.ninja...
Building extension module fused_adam...
Allowing ninja to set a default number of workers... (overridable by setting the environment variable MAX_JOBS=N)
ninja: no work to do.
Loading extension module fused_adam...
Time to load fused_adam op: 0.05891108512878418 seconds
Loading extension module fused_adam...
Time to load fused_adam op: 0.10173463821411133 seconds
Loading extension module fused_adam...
Time to load fused_adam op: 0.10152459144592285 seconds
/opt/conda/lib/python3.10/site-packages/deepspeed/ops/adam/fused_adam.py:96: UserWarning: The torch.cuda.*DtypeTensor constructors are no longer recommended. It's best to use methods such as torch.tensor(data, dtype=*, device='cuda') to create tensors. (Triggered internally at /opt/conda/conda-bld/pytorch_1699449201336/work/torch/csrc/tensor/python_tensor.cpp:83.)
self._dummy_overflow_buf = get_accelerator().IntTensor([0])
/opt/conda/lib/python3.10/site-packages/deepspeed/ops/adam/fused_adam.py:96: UserWarning: The torch.cuda.*DtypeTensor constructors are no longer recommended. It's best to use methods such as torch.tensor(data, dtype=*, device='cuda') to create tensors. (Triggered internally at /opt/conda/conda-bld/pytorch_1699449201336/work/torch/csrc/tensor/python_tensor.cpp:83.)
self._dummy_overflow_buf = get_accelerator().IntTensor([0])
/opt/conda/lib/python3.10/site-packages/deepspeed/ops/adam/fused_adam.py:96: UserWarning: The torch.cuda.*DtypeTensor constructors are no longer recommended. It's best to use methods such as torch.tensor(data, dtype=*, device='cuda') to create tensors. (Triggered internally at /opt/conda/conda-bld/pytorch_1699449201336/work/torch/csrc/tensor/python_tensor.cpp:83.)
self._dummy_overflow_buf = get_accelerator().IntTensor([0])
Parameter Offload: Total persistent parameters: 3674112 in 193 params
0%| | 0/17 [00:00&lt;?, ?it/s]/opt/conda/lib/python3.10/site-packages/torch/utils/checkpoint.py:429: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.
warnings.warn(
/opt/conda/lib/python3.10/site-packages/torch/utils/checkpoint.py:429: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.
warnings.warn(
/opt/conda/lib/python3.10/site-packages/torch/utils/checkpoint.py:429: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.
warnings.warn(
/opt/conda/lib/python3.10/site-packages/bitsandbytes/autograd/_functions.py:322: UserWarning: MatMul8bitLt: inputs will be cast from torch.bfloat16 to float16 during quantization
warnings.warn(f"MatMul8bitLt: inputs will be cast from {A.dtype} to float16 during quantization")
/opt/conda/lib/python3.10/site-packages/bitsandbytes/autograd/_functions.py:322: UserWarning: MatMul8bitLt: inputs will be cast from torch.bfloat16 to float16 during quantization
warnings.warn(f"MatMul8bitLt: inputs will be cast from {A.dtype} to float16 during quantization")
/opt/conda/lib/python3.10/site-packages/bitsandbytes/autograd/_functions.py:322: UserWarning: MatMul8bitLt: inputs will be cast from torch.bfloat16 to float16 during quantization
warnings.warn(f"MatMul8bitLt: inputs will be cast from {A.dtype} to float16 during quantization")
{'loss': 2.0448, 'learning_rate': 2e-05, 'epoch': 0.06}
6%|██▌ | 1/17 [00:28&lt;07:32, 28.30s/it]
0%| | 0/3 [00:00&lt;?, ?it/s]
67%|██████████████████████████████ | 2/3 [00:03&lt;00:01, 1.85s/it]
{'eval_loss': 1.9694719314575195, 'eval_runtime': 11.391, 'eval_samples_per_second': 1.492, 'eval_steps_per_second': 0.263, 'epoch': 0.06}
6%|██▌ | 1/17 [00:39&lt;07:32, 28.30s/it]
100%|█████████████████████████████████████████████| 3/3 [00:07&lt;00:00, 2.65s/it]
[2023-12-28 15:45:35,358] [INFO] [axolotl.callbacks.on_step_end:122] [PID:2812] [RANK:0] GPU memory usage while training: 12.210GB (+4.259GB cache, +0.776GB misc)
12%|█████▏ | 2/17 [01:04&lt;08:18, 33.20s/it][2023-12-28 15:45:35,358] [INFO] [axolotl.callbacks.on_step_end:122] [PID:2814] [RANK:2] GPU memory usage while training: 12.269GB (+4.522GB cache, +0.743GB misc)
[2023-12-28 15:45:35,358] [INFO] [axolotl.callbacks.on_step_end:122] [PID:2813] [RANK:1] GPU memory usage while training: 12.283GB (+4.493GB cache, +0.743GB misc)
{'loss': 2.0022, 'learning_rate': 4e-05, 'epoch': 0.12}
{'loss': 2.1054, 'learning_rate': 6e-05, 'epoch': 0.17}
{'loss': 1.9004, 'learning_rate': 8e-05, 'epoch': 0.23}
{'loss': 1.8794, 'learning_rate': 0.0001, 'epoch': 0.29}
29%|████████████▉ | 5/17 [02:20&lt;05:23, 26.92s/it]
0%| | 0/3 [00:00&lt;?, ?it/s]
67%|██████████████████████████████ | 2/3 [00:03&lt;00:01, 1.88s/it]
{'eval_loss': 1.7912336587905884, 'eval_runtime': 11.3106, 'eval_samples_per_second': 1.503, 'eval_steps_per_second': 0.265, 'epoch': 0.29}
29%|████████████▉ | 5/17 [02:32&lt;05:23, 26.92s/it]
100%|█████████████████████████████████████████████| 3/3 [00:07&lt;00:00, 2.67s/it]
{'loss': 1.7871, 'learning_rate': 0.00012, 'epoch': 0.35}
{'loss': 1.7758, 'learning_rate': 0.00014, 'epoch': 0.4}
{'loss': 1.4645, 'learning_rate': 0.00016, 'epoch': 0.46}
{'loss': 1.4009, 'learning_rate': 0.00018, 'epoch': 0.52}
{'loss': 1.3927, 'learning_rate': 0.0002, 'epoch': 0.58}
59%|█████████████████████████▎ | 10/17 [04:38&lt;03:04, 26.33s/it]
0%| | 0/3 [00:00&lt;?, ?it/s]
67%|██████████████████████████████ | 2/3 [00:03&lt;00:01, 1.89s/it]
{'eval_loss': 1.1426481008529663, 'eval_runtime': 11.3344, 'eval_samples_per_second': 1.5, 'eval_steps_per_second': 0.265, 'epoch': 0.58}
59%|█████████████████████████▎ | 10/17 [04:49&lt;03:04, 26.33s/it]
100%|█████████████████████████████████████████████| 3/3 [00:07&lt;00:00, 2.68s/it]
{'loss': 1.0122, 'learning_rate': 0.0001900968867902419, 'epoch': 0.63}
{'loss': 1.0019, 'learning_rate': 0.00016234898018587337, 'epoch': 0.69}
{'loss': 0.8976, 'learning_rate': 0.00012225209339563145, 'epoch': 0.75}
{'loss': 0.9301, 'learning_rate': 7.774790660436858e-05, 'epoch': 0.81}
{'loss': 0.8595, 'learning_rate': 3.7651019814126654e-05, 'epoch': 0.87}
88%|█████████████████████████████████████▉ | 15/17 [06:55&lt;00:52, 26.17s/it]
0%| | 0/3 [00:00&lt;?, ?it/s]
67%|██████████████████████████████ | 2/3 [00:03&lt;00:01, 1.88s/it]
{'eval_loss': 0.8175248503684998, 'eval_runtime': 11.2932, 'eval_samples_per_second': 1.505, 'eval_steps_per_second': 0.266, 'epoch': 0.87}
88%|█████████████████████████████████████▉ | 15/17 [07:06&lt;00:52, 26.17s/it]
100%|█████████████████████████████████████████████| 3/3 [00:07&lt;00:00, 2.67s/it]
{'loss': 0.7931, 'learning_rate': 9.903113209758096e-06, 'epoch': 0.92}
{'loss': 0.6909, 'learning_rate': 0.0, 'epoch': 0.98}
100%|███████████████████████████████████████████| 17/17 [07:56&lt;00:00, 28.03s/it]/opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py:1879: UserWarning: Positional args are being deprecated, use kwargs instead. Refer to https://pytorch.org/docs/master/generated/torch.nn.Module.html#torch.nn.Module.state_dict for details.
warnings.warn(
/opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py:1879: UserWarning: Positional args are being deprecated, use kwargs instead. Refer to https://pytorch.org/docs/master/generated/torch.nn.Module.html#torch.nn.Module.state_dict for details.
warnings.warn(
/opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py:1879: UserWarning: Positional args are being deprecated, use kwargs instead. Refer to https://pytorch.org/docs/master/generated/torch.nn.Module.html#torch.nn.Module.state_dict for details.
warnings.warn(
{'train_runtime': 489.0649, 'train_samples_per_second': 0.63, 'train_steps_per_second': 0.035, 'train_loss': 1.408153467318591, 'epoch': 0.98}
100%|███████████████████████████████████████████| 17/17 [08:09&lt;00:00, 28.77s/it]
[2023-12-28 15:52:39,488] [INFO] [axolotl.train.log:60] [PID:2812] [RANK:0] Training Completed!!! Saving pre-trained model to ./out</code></pre>
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citeInfo.cites.forEach(function(cite) {
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citeDiv.classList.add('hanging-indent');
citeDiv.classList.add('csl-entry');
var biblioDiv = window.document.getElementById('ref-' + cite);
if (biblioDiv) {
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}
popup.appendChild(citeDiv);
});
return popup.innerHTML;
});
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});
</script>
</div> <!-- /content -->
</body></html>
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