diff --git a/examples/inferrence.ipynb b/examples/inferrence.ipynb index b09ef90..4513510 100644 --- a/examples/inferrence.ipynb +++ b/examples/inferrence.ipynb @@ -20,153 +20,153 @@ "text": [ "Defaulting to user installation because normal site-packages is not writeable\n", "Collecting transformers@ git+https://github.com/huggingface/transformers.git\n", - " Cloning https://github.com/huggingface/transformers.git to /tmp/pip-install-mv1d7wle/transformers_faf474834cbd43d1a82fcfed60794a00\n", - " Running command git clone --filter=blob:none --quiet https://github.com/huggingface/transformers.git /tmp/pip-install-mv1d7wle/transformers_faf474834cbd43d1a82fcfed60794a00\n", + " Cloning https://github.com/huggingface/transformers.git to /tmp/pip-install-64cefzsp/transformers_5f0472cc878d47368a5465679921455b\n", + " Running command git clone --filter=blob:none --quiet https://github.com/huggingface/transformers.git /tmp/pip-install-64cefzsp/transformers_5f0472cc878d47368a5465679921455b\n", " Resolved https://github.com/huggingface/transformers.git to commit 70c79940957fb25b54bd1b106935c756b90345eb\n", " Installing build dependencies ... \u001b[?25ldone\n", "\u001b[?25h Getting requirements to build wheel ... \u001b[?25ldone\n", "\u001b[?25h Preparing metadata (pyproject.toml) ... \u001b[?25ldone\n", "\u001b[?25hCollecting peft@ git+https://github.com/huggingface/peft.git\n", - 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"execution_count": 1, + "execution_count": 2, "id": "1692b132-9280-49ba-8c73-fe71f733f2a5", "metadata": {}, "outputs": [], @@ -213,7 +213,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, "id": "96673ee5-4481-4070-8fae-6310c4138431", "metadata": {}, "outputs": [ @@ -236,7 +236,7 @@ "CUDA SETUP: libcudart.so path is None\n", "CUDA SETUP: Is seems that your cuda installation is not in your path. See https://github.com/TimDettmers/bitsandbytes/issues/85 for more information.\n", "CUDA SETUP: CUDA version lower than 11 are currently not supported for LLM.int8(). You will be only to use 8-bit optimizers and quantization routines!!\n", - "CUDA SETUP: Highest compute capability among GPUs detected: 8.0\n", + "CUDA SETUP: Highest compute capability among GPUs detected: 9.0\n", "CUDA SETUP: Detected CUDA version 00\n", "CUDA SETUP: Loading binary /home/ubuntu/.local/lib/python3.8/site-packages/bitsandbytes/libbitsandbytes_cpu.so...\n" ] @@ -249,8 +249,6 @@ " warn(\"The installed version of bitsandbytes was compiled without GPU support. \"\n", "/home/ubuntu/.local/lib/python3.8/site-packages/bitsandbytes/cuda_setup/main.py:149: UserWarning: WARNING: The following directories listed in your path were found to be non-existent: {PosixPath('/run/lambda-jupyter-lab.pid')}\n", " warn(msg)\n", - "/home/ubuntu/.local/lib/python3.8/site-packages/bitsandbytes/cuda_setup/main.py:149: UserWarning: WARNING: The following directories listed in your path were found to be non-existent: {PosixPath('/home/ubuntu/Anima/examples/Untitled.ipynb')}\n", - " warn(msg)\n", "/home/ubuntu/.local/lib/python3.8/site-packages/bitsandbytes/cuda_setup/main.py:149: UserWarning: WARNING: The following directories listed in your path were found to be non-existent: {PosixPath('module'), PosixPath('//ipykernel.pylab.backend_inline')}\n", " warn(msg)\n", "/home/ubuntu/.local/lib/python3.8/site-packages/bitsandbytes/cuda_setup/main.py:149: UserWarning: WARNING: The following directories listed in your path were found to be non-existent: {PosixPath('/usr/local/cuda/lib64')}\n", @@ -283,7 +281,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "91521f6488c9442d9f51ba2cd0935519", + "model_id": "e0907f2e60d24e53bb0adfb39e07c55a", "version_major": 2, "version_minor": 0 }, @@ -297,7 +295,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "251afd5898624f2daacdef4a66224054", + "model_id": "b89c2ccff9d5467ab47c1f180ec868f0", "version_major": 2, "version_minor": 0 }, @@ -311,7 +309,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "a1f6ce9751304177ab27cddf8418d1ed", + "model_id": "0bf37509fe72499eb074879a5b5badbb", "version_major": 2, "version_minor": 0 }, @@ -338,7 +336,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "a53fb9ee15f04598b36df9c69e787d14", + "model_id": "67a7af9075af4e6b9d0a964df0a8deec", "version_major": 2, "version_minor": 0 }, @@ -352,7 +350,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "30465824d17e4618a53d1e04290494ea", + "model_id": "b96b2b6b2d8045409538c939d637a89f", "version_major": 2, "version_minor": 0 }, @@ -366,7 +364,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "527bb95c194f4a19b759ee46d44b2731", + "model_id": "5daad442b5a143bc913693413f65edfb", "version_major": 2, "version_minor": 0 }, @@ -380,7 +378,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "2d4ea59e746b455ebcd11a0f387a2be1", + "model_id": "4cd9e08369964aa7bead2d818d0bce5e", "version_major": 2, "version_minor": 0 }, @@ -394,7 +392,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "9cff8a1fe9be4ac4a10d351061dc7f3b", + "model_id": "24fabe9e4d4b4380ab7ce25f1b73b9a9", "version_major": 2, "version_minor": 0 }, @@ -408,7 +406,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "ad49c9aa24cc429a945be24be7fd9e0f", + "model_id": "4b312fc6194e46dcbdbd23b2050267aa", "version_major": 2, "version_minor": 0 }, @@ -422,7 +420,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "0d451f49d26e4be4a6cc45583512e530", + "model_id": "9d4583b23c8e4f78aa87420f022815dc", "version_major": 2, "version_minor": 0 }, @@ -436,7 +434,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "4ed73b1f0c66448e90900bdd5ee50bbe", + "model_id": "99249522f6c54846b449e35fc1babf7c", "version_major": 2, "version_minor": 0 }, @@ -450,7 +448,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "ea91db2162384c688ae0a216c4210c13", + "model_id": "3ed218039f6e4dbfba2926125b1a9cea", "version_major": 2, "version_minor": 0 }, @@ -464,7 +462,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "7f5e05bdea914363abcbfe0a37df14f2", + "model_id": "96e3c8ce1e6140ee9eab8062385671bc", "version_major": 2, "version_minor": 0 }, @@ -485,7 +483,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "6472b4b465634199b59a2dcb40c5063d", + "model_id": "6965927d7c7b4127b942a31327986f5c", "version_major": 2, "version_minor": 0 }, @@ -499,7 +497,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "3f91e6f6538743d7b0f24ed3f4400829", + "model_id": "80f7ed64ad794248a7674bd955c1596f", "version_major": 2, "version_minor": 0 }, @@ -522,10 +520,6547 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "id": "249c9ef2-ce90-4ce3-8b8c-2685839d837d", - "metadata": {}, - "outputs": [], + "metadata": { + "collapsed": true, + "jupyter": { + "outputs_hidden": true + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "bb1ccc9f54854c768a9c5aa2299378bc", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Downloading (…)/adapter_config.json: 0%| | 0.00/505 [00:00