diff --git "a/Alpaca_+_Mistral_7b_full_example.ipynb" "b/Alpaca_+_Mistral_7b_full_example.ipynb" --- "a/Alpaca_+_Mistral_7b_full_example.ipynb" +++ "b/Alpaca_+_Mistral_7b_full_example.ipynb" @@ -3,11 +3,11 @@ { "cell_type": "markdown", "source": [ - "To run this, press \"Runtime\" and press \"Run all\" on a **free** Tesla T4 Google Colab instance!\n", + "To run this, press \"*Runtime*\" and press \"*Run all*\" on a **free** Tesla T4 Google Colab instance!\n", "
\n", " \n", - " \n", - " Join our Discord if you need help!\n", + " \n", + " Join Discord if you need help + support us if you can!\n", "
\n", "\n", "To install Unsloth on your own computer, follow the installation instructions on our Github page [here](https://github.com/unslothai/unsloth#installation-instructions---conda).\n", @@ -59,97 +59,96 @@ "metadata": { "colab": { "base_uri": "https://localhost:8080/", - "height": 544, + "height": 527, "referenced_widgets": [ - "21fd9c71893a4a99b96e79f39213af35", - "99c5700859124f37be1156508a685e28", - "5d9562bd69f64f2bb0f3a7b271af3d9e", - "7adea9776e2348619d345699778531c4", - "3f5189d724e04588b3782479297877ce", - "20651e17aea349bdbc4f0d67522d4577", - "04b1e656bedb4b5bb0388d7239ef01cb", - "5151bb9352074dbc965d3503ed94e5c5", - "4431246852364e3990f46df5186f5cc7", - "f215b46454e44fb5a474c765e23888ac", - "acf89e72380c4e388dc72e2684a4bea1", - "27f30a48f4f04b06bbc392989c99304d", - "709c7148397d4bffb855dd2cfa3ae28f", - "53bd9b2299754baa89c805d52efb1229", - "e742e883d9e342bfb5d855b996e20c35", - "9d554c1d3d894ee8b0057d3b629a1375", - "ab0c5847e0064fa8905d4cecbc3fdb24", - "f41dfa9cdccd4dd193d794b314113608", - "c3c30bca2f034056b19c562c13d2d624", - "6113ac3a4f2d4620b47dcadd3b8ced7a", - "979028afa83f4fbd88dc7af54f2ea346", - "1a76dd74171e4275a858bc778cb09413", - "bea3f4e007fb4f8d92766ec2455e1c17", - "a2f6355eeae340b6b29ee901c1d26a42", - "958fd55cc70b404da1f44177aa86f91d", - "dbba49dde33745a68a1d2e3d783cfa64", - "2bb22f43844c4f91ab3264a736976842", - "830f4ba2895749c3859931beb0789c6f", - "d23bc863c5aa4d55aa7b7ee89f9e1967", - "aab98b960b784260a9fbe7722dcc48be", - "eaf252cf95d148619e198128fa6176c1", - "5bc33740d9dc45d9959676268cc403f1", - "e84fd33e8af149199191917d41f73995", - "d2945f43455245c491b3cafc6f6b463b", - "b2a60ed3dc304f6193bd08e2a085510c", - "93224ff8b9aa4adda5cfb72e97f31e9f", - "c2dade9e7951426087f570c912765220", - "f4477f83b0ad4a7e999b7673e77df34a", - "1066f1cd381640f3a50a6e3f5eb69310", - "8a0571e322084e559e8ea6cf2dccdec2", - "8e5f466a39c240c6932b15f90d3198c2", - "5dadd8af5a974cea83c078bd692570d8", - "d92d6d3ec5b14c6fa29224838a41dfbf", - "7fdec40cd5dd4b76b1793a1d2f1127ef", - "e939fbb3ee754005af82be95a89463b8", - 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"outputId": "c8350926-5143-40ce-fb35-2bffbe499cac" + "outputId": "a1a670b2-ee62-4bbf-901a-38f79389db28" }, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ - "/usr/local/lib/python3.10/dist-packages/unsloth/__init__.py:67: UserWarning: CUDA is not linked properly.\n", - "We shall run `ldconfig /usr/lib64-nvidia` to try to fix it.\n", + "/usr/local/lib/python3.10/dist-packages/unsloth/__init__.py:67: UserWarning: Running `ldconfig /usr/lib64-nvidia` to link CUDA.\n", " warnings.warn(\n", "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:88: UserWarning: \n", "The secret `HF_TOKEN` does not exist in your Colab secrets.\n", @@ -168,7 +167,7 @@ "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, - "model_id": "21fd9c71893a4a99b96e79f39213af35" + "model_id": "3a4a21c80b1e4232825d13fbceb4e051" } }, "metadata": {} @@ -195,7 +194,7 @@ "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, - "model_id": "27f30a48f4f04b06bbc392989c99304d" + "model_id": "8978f89cdfd64c41b504debc99fa000b" } }, "metadata": {} @@ -209,7 +208,7 @@ "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, - "model_id": "bea3f4e007fb4f8d92766ec2455e1c17" + "model_id": "c52fb02a25774735bc6cd1fd4085ffb2" } }, "metadata": {} @@ -223,7 +222,7 @@ "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, - "model_id": "d2945f43455245c491b3cafc6f6b463b" + "model_id": "6a46284e269e4044b2e082098fb0988f" } }, "metadata": {} @@ -237,7 +236,7 @@ "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, - "model_id": "e939fbb3ee754005af82be95a89463b8" + "model_id": "3e463765bc424c67b88b5b522fd12f36" } }, "metadata": {} @@ -251,7 +250,7 @@ "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, - "model_id": "4f7a08f57ddf4824b5e82d04ebbeb96c" + "model_id": "ae42b52c748240f2bcbfea01b4573622" } }, "metadata": {} @@ -265,7 +264,7 @@ "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, - "model_id": "b24b629c56774be2a728f3c7f18f4360" + "model_id": "b5eae4bf90bc4f10baa3cef668109188" } }, "metadata": {} @@ -278,7 +277,7 @@ "dtype = None # None for auto detection. Float16 for Tesla T4, V100, Bfloat16 for Ampere+\n", "load_in_4bit = True # Use 4bit quantization to reduce memory usage. Can be False.\n", "\n", - "# 4bit pre quantized models we support for 4x faster downloading!\n", + "# 4bit pre quantized models we support for 4x faster downloading + no OOMs.\n", "fourbit_models = [\n", " \"unsloth/mistral-7b-bnb-4bit\",\n", " \"unsloth/llama-2-7b-bnb-4bit\",\n", @@ -288,7 +287,7 @@ "]\n", "\n", "model, tokenizer = FastLanguageModel.from_pretrained(\n", - " model_name = \"unsloth/mistral-7b-bnb-4bit\", # \"unsloth/mistral-7b\" for 16bit loading\n", + " model_name = \"unsloth/mistral-7b-bnb-4bit\", # Choose ANY! eg mistralai/Mistral-7B-Instruct-v0.2\n", " max_seq_length = max_seq_length,\n", " dtype = dtype,\n", " load_in_4bit = load_in_4bit,\n", @@ -313,7 +312,7 @@ "colab": { "base_uri": "https://localhost:8080/" }, - "outputId": "ac6dcf4f-ecfb-41f2-8bcf-c63bc171453b" + "outputId": "7a19202f-6ef6-4a71-cf88-004c01769255" }, "outputs": [ { @@ -335,7 +334,8 @@ " bias = \"none\", # Supports any, but = \"none\" is optimized\n", " use_gradient_checkpointing = True,\n", " random_state = 3407,\n", - " max_seq_length = max_seq_length,\n", + " use_rslora = False, # We support rank stabilized LoRA\n", + " loftq_config = None, # And LoftQ\n", ")" ] }, @@ -346,7 +346,9 @@ "### Data Prep\n", "We now use the Alpaca dataset from [yahma](https://huggingface.co/datasets/yahma/alpaca-cleaned), which is a filtered version of 52K of the original [Alpaca dataset](https://crfm.stanford.edu/2023/03/13/alpaca.html). You can replace this code section with your own data prep.\n", "\n", - "**[NOTE]** To train only on completions (ignoring the user's input) read TRL's docs [here](https://huggingface.co/docs/trl/sft_trainer#train-on-completions-only)." + "**[NOTE]** To train only on completions (ignoring the user's input) read TRL's docs [here](https://huggingface.co/docs/trl/sft_trainer#train-on-completions-only).\n", + "\n", + "**[NOTE]** Remember to add the **EOS_TOKEN** to the tokenized output!! Otherwise you'll get infinite generations!" ], "metadata": { "id": "vITh0KVJ10qX" @@ -361,54 +363,53 @@ "base_uri": "https://localhost:8080/", "height": 145, "referenced_widgets": [ - "a8a8adf4394f4ea4be00c9853c2a3ee5", - "aca3ffe3a48741418e6ad9832b4c3834", - "581c364e9f8c44648d9e8808e1923028", - "7c7a322585654ac6a7bde6488caa2b30", - "60468642068c4c1f930517ec6c9dd399", - "9bc911e88d9240b79aa19c5351664dc9", - "d78e367940154413a7310304db0a7d89", - "3f614e47d3164d4fbc5235c44ab14680", - "e0625e2e1e98435c8bb5d2f6838396f4", - "4890d11383634ed7b88fd29abc79bceb", - "5202446fa7c341d4af217e46b7be79e4", - "4eac852431744e40b7dcf9ecca1e1fd1", - "a066366a6c7a4771a6d5e718fd92f88f", - "70a536f16d6c431b98b7ef12801d33a2", - "ff6f563aef26414f829868f1ba6f29c7", - "5242f675a9d940eb83c5dc6ce08417dd", - "a576ce5ff14a4f1a8a78a4fad7caf2ed", - "aa5794124ec74a29bce5663bbae3b4ec", - "beaa198d254e4521b795d4cff4b4df6e", - "f7146f6127a2489cb4c02b714d67f783", - "0b8cff5afa7242c1b817df3fd509cd04", - "423df2448502498a85107a594562cea9", - "c57f151c22ca4bb49c25613b06036b00", - "19efa2caad3e477185a566bad2d2d6d5", - "3f6f62974c1a4163ad2dc437aa705e7f", - "706f7cda0ebd47fb88fea4bc75db6761", - "15aeeaa33efd4933b52e234a8ce9b98f", - "97dfc903d38944458f094f5d7a79e4ee", - "ce44aae35b1642bda5d76a9ab5b635f9", - "e6de9852ddb9426ea43edaeb79569b00", - "96fda888005f4934ac13e68182bf71cf", - "5ff8951b0da7438097cc58b509a97f4a", - "35e25cff723e4dcfab3eeb891699d67f", - "cc4186d840ae4644a45d330480b15322", - "abb7a90a330e42b491fe4a1ca6b10bfc", - "ca7a0ff127f84bedaad3009d6c9fa0fb", - "7d0a83dbc8774bd0832aced31e6c8901", - "3066b61c1e9d4c4ba8cbab9cd106e8c9", - "146cbaafb0114bce94b306cd96b958e8", - "f55c54affd70418b8c3a5a22603d4320", - "c61e4cb0a3994bab91dd20bc02ed18ad", - "f8233de76001468bbf94151f069c2503", - "86643f0ef91740db9b4bffe394f709d4", - "1744a4f1b0164c10a54a9b912b6149cc" + "e0ea75df3b5c49ce89427e4d245a7646", + "02237d111eba4155ab5a48a1b33d82a4", + "927e1d33248d4653a785049b50b6d814", + "09979ecafec9443ba1f7335ed64de778", + "088a000de2234c9f94a8b09e8a8abbb2", + "38c7b4ead56b4540bf68fbe3a5496b9c", + "9a0787c7c98b49b8a9141e5212faa249", + "daf62811f5384d0f9aea58b40f23161a", + "6f90172dd5c240c885140d49add6208f", + "187078f1978843f2b873cee2ee55ac91", + "ca8af702ee764e7fa620476854a4f2cf", + "80b9d976e30e40ecaf35a606bca5d647", + "e9b423d370314fdfa3c22e95c3a35d92", + "307f44c0a4da4d3ca0d44b54f9a5f6c0", + "5503539430024c7586d4f8589c92fd74", + "b17542f2cd0f45e79e755df622328358", + "fe6d1bab4fd042b5af89f6bb8a73cc39", + "8cb7748eaa354d4ea0e3222685f1d9b4", + "53bfefa3e2c04cce9ab7d2b3fbca59d9", + "aa58f2124335451890857ded72414ecf", + "b331b446e257441387b331d822f570a0", + "49e3298fbe054c7abb6a041ecd63737c", + "6b104e7242cf4a608cfd0c4fc6f44db1", + "1a6288e052884d5aa486415880acf134", + "289c8b4d1686443f8bed7441673a2c15", + "1318a594d2df47779b84f22e4b0c0702", + "355ecaa94d1a43b09e1d0b3f6e6ac8e3", + "156432d2f1b74697bd74604e1bd5bf13", + "976fa37d0fa14282bc96b3c948b9da94", + "24768bbd587d4b1ba09b6c52dcd6237c", + "cdf054995f314976b7124de9564cbd9b", + "1eec25504a6f41d199940c4232b0a114", + "007a9dbfb5c84efd8ffd801bc04c0c20", + "2ffddabe91124b279f9a3ac41d1ead9d", + "f97edc77840e49c49aed8e7ab80f45d1", + "33761916bce24fde9bcff866736ddad7", + "726aae420987454e87a08b9314844895", + "071858b3801e4844a4fc5a91205335c4", + "6a98441b0ff74361a79c96178834d8bc", + "40e8b59e229548c68ccb339234a5e525", + "56b011c2482b427483508e259dc231fd", + "507c15541fe7489aa1fd1fd81c4aa222", + "4bf05ee528bb4bb2979cde4d0a09544b", + "d62a5696b9704580b69e6fc3be9ffa8a" ] }, - "cellView": "form", - "outputId": "72d478d8-c49d-4bbb-ef19-caae4d0f3895" + "outputId": "26e1bc8e-c4e8-472e-ca91-670e3381c6b3" }, "outputs": [ { @@ -420,7 +421,7 @@ "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, - "model_id": "a8a8adf4394f4ea4be00c9853c2a3ee5" + "model_id": "e0ea75df3b5c49ce89427e4d245a7646" } }, "metadata": {} @@ -434,7 +435,7 @@ "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, - "model_id": "4eac852431744e40b7dcf9ecca1e1fd1" + "model_id": "80b9d976e30e40ecaf35a606bca5d647" } }, "metadata": {} @@ -448,7 +449,7 @@ "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, - "model_id": "c57f151c22ca4bb49c25613b06036b00" + "model_id": "6b104e7242cf4a608cfd0c4fc6f44db1" } }, "metadata": {} @@ -462,14 +463,13 @@ "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, - "model_id": "cc4186d840ae4644a45d330480b15322" + "model_id": "2ffddabe91124b279f9a3ac41d1ead9d" } }, "metadata": {} } ], "source": [ - "#@title Alpaca dataset preparation code\n", "alpaca_prompt = \"\"\"Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.\n", "\n", "### Instruction:\n", @@ -481,13 +481,15 @@ "### Response:\n", "{}\"\"\"\n", "\n", + "EOS_TOKEN = tokenizer.eos_token # Must add EOS_TOKEN\n", "def formatting_prompts_func(examples):\n", " instructions = examples[\"instruction\"]\n", " inputs = examples[\"input\"]\n", " outputs = examples[\"output\"]\n", " texts = []\n", " for instruction, input, output in zip(instructions, inputs, outputs):\n", - " text = alpaca_prompt.format(instruction, input, output)\n", + " # Must add EOS_TOKEN, otherwise your generation will go on forever!\n", + " text = alpaca_prompt.format(instruction, input, output) + EOS_TOKEN\n", " texts.append(text)\n", " return { \"text\" : texts, }\n", "pass\n", @@ -517,64 +519,64 @@ "base_uri": "https://localhost:8080/", "height": 177, "referenced_widgets": [ - "15a93404b92749c883a8d12457f26c8b", - "fb8c3976bab449378f593c98fbb39ee2", - "a6a8d886daee4efca0367499a4017bf2", - "10e360552c7449bd883a92609d7fa0c6", - "4877845709dd408892e890dfb960cc43", - "ad01c612601d4397baa49462ea9a072e", - "aa3b6b80b097473abf5a668122c6fe53", - "f32eaa45bf984a8297966aa51cfeecaa", - "6085d90e846a4f35aab61d8b69e792c1", - "42a87fc42c434a29bf3b4fefaa7303b9", - "5ec7290c9829407eb9118e7e80b361ab", - "2e59b8ebd1d746a6a3fc712792b113a9", - "581aa9bfab964c34a321614bd6fe5f24", - "cc56c401fdb24eac97eac7a2ebb02941", - "12773f1437de4ad59231d2fc8f3e07c0", - "dbcee7f6de434667877d8a77122f2eec", - "6efad27478a848a79741a57f42b69569", - "521938814b4b4ee8a294313e9ecce8cf", - "d90435a8d6b344f39e4334791015b7fa", - "a66f92cd6a8446d985eccfc0c4f755a1", - "8a691a6099e3475e91d7eac4c05adcea", - "4ae0fa029fe9450994b3ff0c067748dd", - "6dd689dc12d04fba8398b026658710ab", - "e9151dd6e8214242b2160e6669411605", - "9aae260518204f4cbb2bb311ca63465a", - "97cb0c3e4b714794b31c9f632395585f", - "41454da47d524b238d57707d0adec739", - "f642257584994fc8912725796fe8fb05", - "e02eb8727d92467bb059c8d2167b9ab5", - "7d44a58f494b454d9252275f02dc82bc", - "d2edb8443f76499f9b77650905060ec1", - "6279562bf9e545bdbe69d7138a14ce96", - "e7ed16effd4e4ebab99eff7ed31f345e", - "24ba4d106a9441d99564c4686d271e08", - "3d339c599ea94d3687731f7028e30217", - "accc31937ca341c4814552a6718bf1e0", - "ff0284561a9147929a844112d99eb3ed", - "c6af98fea280457e92e73be95b5283d2", - "7f2c7b33282d409ea560de7d1a707ad3", - "ea40866a478d430880435a9d52543b88", - "6b84ee03c9504488a9c20a41097d18be", - "fe3f069852624450a384348a611141f2", - "95d18e87dac747da80b248f8a3d1ce64", - "6ee77e3cb845462d9fcae86d55277fad", - "900a9076b00d4bcebf40c97eeec7ebe9", - "0c9c1f29c76b48f0b5db845c93835d69", - "bbc7cbdac1b246b4ab777becfc19aae8", - "114373f9b133498da03f0f73dcc2a066", - "3b766d8d98bb43ceb844f571d41a2ddb", - "e937e9cbd01f4287befd13aeaade6712", - "1a033034e75f421f9e546beec5b4d77c", - "814be9f3d9444d96b4c5e51472fd4fb8", - "51efbd9d8a0d45b9b53cb4cf467954ac", - "6bf5fa33ab64465b9fe087c22719bdc6", - "d24cc25d7973414890b15e262f29e418" + "477d5041a08f4a3a9f7cfa1d98ab48ff", + "25eecdf89b8845a989ce2c8c4d9edbeb", + "e02470ee64ad4f0fb2160b088cdcba7f", + "b7c0858a80684aed9c19ce00dda85815", + "d0b82646d6f549d7ad59e9ff5f426e88", + "d94a8d1f9d294abbb156e39da065910f", + "ebb0f92e750447a39ff23a23ea73b445", + "d4d01dc3290b4174ab13454a28faf972", + "c6941f8c60ef49a8a79ed265c46652c2", + "24aa300026334db1b545bf0fa906112e", + "6d0d021108b54147a62e50fea1ba88ea", + "deb2982b19764ed5aec0b4d80e776279", + "c24904c0a7294f3a93bb64aa38e70316", + "26a77ab74a4a4e21b9afd9798a9f9a29", + "7189bac8d0474bcea50cf8711259516c", + "ce81350896a44331aa6b6960b7370325", + "73d4af57ddc64fb3afd0e2ab068cbcb4", + "672d990adee44df6b58e27fe5804986f", + "3fe95fc9bc034a2db85ed19aedfd4250", + "c1c0053b8e674ed6ac81316d4af82c48", + "74a0e53405cd4d64bd597ad5461ed5dd", + "278d35f6e08d45e2a49c3509dd442f0c", + "b19d83c6f5ad4f04941e6a684678ac07", + "88e100a175e64a3dab6f772847deffd6", + "457b5df3a4294c8a966e233d34c15a82", + "18af2c44a24b4aaabfd192e3fc4bf655", + "61944d9473394be5b19cfd48fd504481", + "68d18369acc540a594aef61a2de07e63", + "69c676782e0b496b820b55c45424177d", + "08dcaabc623c4759a00fbee5430e3ba9", + "45a3bcaffc3f4184b9ae869f7b0ccef4", + "c45f02fbb40e4041ba7f95074f8e1e82", + "0fc1037a17e541eba92a2d6f400ac6eb", + "7bd2cc9aa724408fa9e70795744cad85", + "fb0588bffd7a4238bac3f73052e09335", + "68ff2006b3794e23b4ccbc2c83c52b9b", + "910f2e6fffd24cd7b6e68c932c2a2524", + "6345dc6f40c6444aa05ed2aba7809a3c", + "92eab7fadbed4bc2bc2935b4e3d800cf", + "2dfef2cd79c8463cb7eadad6a04aba91", + "3be5c37493e742aebf0aa29b6723283b", + "273be47263384901b6cf9f249ee3409d", + "2ff515b72bbb43c889e2172c83933803", + "a1cd830a712d490181d176267ce7b6f0", + "1a8da471604841ec9f6c8e0073471c00", + "f899ae16708544379d63960be07b7c32", + "0bdbf9b92f7b4925a5ac28df93fd0fb0", + "c1d8820a789f4899a839e6d28a4f333c", + "e711d7f85eee4fe195fad9fbddcfece2", + "56ed5fd876d94ebbaa8b4e905c348a0d", + "5d461f10bdc44c6b95d070fa9d7425d1", + "f4e0e7a39ad9484f930e6673c35c2f5d", + "baad9118cade4cee9600a7fbf6426e0a", + "16fac1aad22444c4ad42ad0e6e1dfdc9", + "ac35368de2d746b4bed736459d187dbd" ] }, - "outputId": "41800a9c-69d0-4e0e-d3e0-48f3598f0071" + "outputId": "adb8cb5d-0ec3-4b79-83a7-5691873873e8" }, "outputs": [ { @@ -586,7 +588,7 @@ "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, - "model_id": "15a93404b92749c883a8d12457f26c8b" + "model_id": "477d5041a08f4a3a9f7cfa1d98ab48ff" } }, "metadata": {} @@ -600,7 +602,7 @@ "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, - "model_id": "2e59b8ebd1d746a6a3fc712792b113a9" + "model_id": "deb2982b19764ed5aec0b4d80e776279" } }, "metadata": {} @@ -614,7 +616,7 @@ "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, - "model_id": "6dd689dc12d04fba8398b026658710ab" + "model_id": "b19d83c6f5ad4f04941e6a684678ac07" } }, "metadata": {} @@ -628,7 +630,7 @@ "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, - "model_id": "24ba4d106a9441d99564c4686d271e08" + "model_id": "7bd2cc9aa724408fa9e70795744cad85" } }, "metadata": {} @@ -637,12 +639,12 @@ "output_type": "display_data", "data": { "text/plain": [ - 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" @@ -1006,19 +1009,19 @@ "colab": { "base_uri": "https://localhost:8080/" }, - "outputId": "8164562f-9c1d-4d1c-a596-ebdfb95001a1" + "outputId": "ff1b0842-5966-4dc2-bd98-c20832526b31" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ - "480.792 seconds used for training.\n", - "8.01 minutes used for training.\n", - "Peak reserved memory = 6.826 GB.\n", - "Peak reserved memory for training = 2.201 GB.\n", - "Peak reserved memory % of max memory = 46.284 %.\n", - "Peak reserved memory for training % of max memory = 14.924 %.\n" + "484.744 seconds used for training.\n", + "8.08 minutes used for training.\n", + "Peak reserved memory = 6.846 GB.\n", + "Peak reserved memory for training = 2.221 GB.\n", + "Peak reserved memory % of max memory = 46.42 %.\n", + "Peak reserved memory for training % of max memory = 15.06 %.\n" ] } ], @@ -1050,6 +1053,8 @@ { "cell_type": "code", "source": [ + "# alpaca_prompt = Copied from above\n", + "\n", "inputs = tokenizer(\n", "[\n", " alpaca_prompt.format(\n", @@ -1059,7 +1064,7 @@ " )\n", "]*1, return_tensors = \"pt\").to(\"cuda\")\n", "\n", - "outputs = model.generate(**inputs, max_new_tokens = 128, use_cache = True)\n", + "outputs = model.generate(**inputs, max_new_tokens = 64, use_cache = True)\n", "tokenizer.batch_decode(outputs)" ], "metadata": { @@ -1067,7 +1072,7 @@ "colab": { "base_uri": "https://localhost:8080/" }, - "outputId": "a14cd344-4807-4389-f357-bbd5a6661d67" + "outputId": "9f3bd036-9ed6-40af-b4e8-6193b2088d3b" }, "execution_count": null, "outputs": [ @@ -1075,15 +1080,14 @@ "output_type": "stream", "name": "stderr", "text": [ - "/usr/local/lib/python3.10/dist-packages/transformers/generation/utils.py:1547: UserWarning: You have modified the pretrained model configuration to control generation. This is a deprecated strategy to control generation and will be removed soon, in a future version. Please use and modify the model generation configuration (see https://huggingface.co/docs/transformers/generation_strategies#default-text-generation-configuration )\n", - " warnings.warn(\n" + "Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n" ] }, { "output_type": "execute_result", "data": { "text/plain": [ - "[' Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.\\n\\n### Instruction:\\nContinue the fibonnaci sequence.\\n\\n### Input:\\n1, 1, 2, 3, 5, 8\\n\\n### Response:\\nThe next number in the Fibonacci sequence is 13. The Fibonacci sequence is a series of numbers where each number is the sum of the two numbers before it. The sequence starts with 0 and 1, and each subsequent number is the sum of the two numbers before it. The sequence continues as follows: 0, 1, 1, 2, 3, 5, 8, 13, 21, 34, 55, 89, 144, 233, 377, 61']" + "[' Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.\\n\\n### Instruction:\\nContinue the fibonnaci sequence.\\n\\n### Input:\\n1, 1, 2, 3, 5, 8\\n\\n### Response:\\nThe next number in the Fibonacci sequence is 13.']" ] }, "metadata": {}, @@ -1091,12 +1095,75 @@ } ] }, + { + "cell_type": "markdown", + "source": [ + " You can also use a `TextStreamer` for continuous inference - so you can see the generation token by token, instead of waiting the whole time!" + ], + "metadata": { + "id": "CrSvZObor0lY" + } + }, + { + "cell_type": "code", + "source": [ + "# alpaca_prompt = Copied from above\n", + "\n", + "inputs = tokenizer(\n", + "[\n", + " alpaca_prompt.format(\n", + " \"Continue the fibonnaci sequence.\", # instruction\n", + " \"1, 1, 2, 3, 5, 8\", # input\n", + " \"\", # output - leave this blank for generation!\n", + " )\n", + "]*1, return_tensors = \"pt\").to(\"cuda\")\n", + "\n", + "from transformers import TextStreamer\n", + "text_streamer = TextStreamer(tokenizer)\n", + "_ = model.generate(**inputs, streamer = text_streamer, max_new_tokens = 128)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "e2pEuRb1r2Vg", + "outputId": "2188f68b-6b72-46e6-ea85-f8134df0aa46" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + " Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.\n", + "\n", + "### Instruction:\n", + "Continue the fibonnaci sequence.\n", + "\n", + "### Input:\n", + "1, 1, 2, 3, 5, 8\n", + "\n", + "### Response:\n", + "The next number in the Fibonacci sequence is 13.\n" + ] + } + ] + }, { "cell_type": "markdown", "source": [ "\n", "### Saving, loading finetuned models\n", - "To save the final model, either use Huggingface's `push_to_hub` for an online save or `save_pretrained` for a local save." + "To save the final model as LoRA adapters, either use Huggingface's `push_to_hub` for an online save or `save_pretrained` for a local save.\n", + "\n", + "**[NOTE]** This ONLY saves the LoRA adapters, and not the full model. To save to 16bit or GGUF, scroll down!" ], "metadata": { "id": "uMuVrWbjAzhc" @@ -1106,7 +1173,7 @@ "cell_type": "code", "source": [ "model.save_pretrained(\"lora_model\") # Local saving\n", - "# model.push_to_hub(\"your_name/lora_model\") # Online saving" + "# model.push_to_hub(\"your_name/lora_model\", token = \"...\") # Online saving" ], "metadata": { "id": "upcOlWe7A1vc" @@ -1117,9 +1184,7 @@ { "cell_type": "markdown", "source": [ - "Now if you want to load the LoRA adapters we just saved to use for inference, UNCOMMENT the commented parts, and copy paste the below to a new instance.\n", - "\n", - "**[HELP]** If `unsloth/mistral-7b-bnb-4bit` errors out, try `unsloth/mistral-7b`" + "Now if you want to load the LoRA adapters we just saved for inference, set `False` to `True`:" ], "metadata": { "id": "AEEcJ4qfC7Lp" @@ -1128,18 +1193,14 @@ { "cell_type": "code", "source": [ - "from peft import PeftModel\n", - "\n", - "### **** UNCOMMENT BELOW FOR INFERENCE **** ###\n", - "# from unsloth import FastLanguageModel\n", - "# model, tokenizer = FastLanguageModel.from_pretrained(\n", - "# model_name = \"unsloth/mistral-7b\" # YOUR MODEL YOU USED FOR TRAINING\n", - "# max_seq_length = max_seq_length,\n", - "# dtype = dtype,\n", - "# load_in_4bit = load_in_4bit,\n", - "# )\n", - "# model = PeftModel.from_pretrained(model, \"lora_model\")\n", - "### **** UNCOMMENT ABOVE FOR INFERENCE **** ###\n", + "if False:\n", + " from unsloth import FastLanguageModel\n", + " model, tokenizer = FastLanguageModel.from_pretrained(\n", + " model_name = \"lora_model\", # YOUR MODEL YOU USED FOR TRAINING\n", + " max_seq_length = max_seq_length,\n", + " dtype = dtype,\n", + " load_in_4bit = load_in_4bit,\n", + " )\n", "\n", "alpaca_prompt = \"\"\"Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.\n", "\n", @@ -1161,7 +1222,7 @@ " )\n", "]*1, return_tensors = \"pt\").to(\"cuda\")\n", "\n", - "outputs = model.generate(**inputs, max_new_tokens = 128, use_cache = True)\n", + "outputs = model.generate(**inputs, max_new_tokens = 64, use_cache = True)\n", "tokenizer.batch_decode(outputs)" ], "metadata": { @@ -1169,93 +1230,52 @@ "colab": { "base_uri": "https://localhost:8080/" }, - "outputId": "9a6df427-0e12-45fe-9467-78e2095346ce" + "outputId": "68f54910-2634-4cce-f3a2-4cbb90e2e990" }, "execution_count": null, "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n" + ] + }, { "output_type": "execute_result", "data": { "text/plain": [ - "[\" Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.\\n\\n### Instruction:\\nWhat is a famous tall tower in Paris?\\n\\n### Input:\\n\\n\\n### Response:\\nThe Eiffel Tower is a famous tall tower in Paris, France. It is an iron lattice tower located on the Champ de Mars and is one of the most recognizable structures in the world. It was built in 1889 as the entrance arch to the 1889 World's Fair and was designed by Gustave Eiffel. The tower stands at a height of 324 meters (1,063 feet) and is the tallest structure in Paris. It is a popular tourist attraction and is one of the most visited monuments in the world.\"]" + "[\" Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.\\n\\n### Instruction:\\nWhat is a famous tall tower in Paris?\\n\\n### Input:\\n\\n\\n### Response:\\nThe Eiffel Tower is a famous tall tower in Paris, France. It is located on the Champ de Mars and is one of the most recognizable symbols of the city. The tower was built in 1889 as the entrance arch to the 1889 World's Fair and was\"]" ] }, "metadata": {}, - "execution_count": 11 + "execution_count": 13 } ] }, + { + "cell_type": "markdown", + "source": [ + "You can also use Hugging Face's `AutoModelForPeftCausalLM`" + ], + "metadata": { + "id": "QQMjaNrjsU5_" + } + }, { "cell_type": "code", "source": [ - "#@title Code for conversion to GGUF\n", - "def colab_quantize_to_gguf(save_directory, quantization_method = \"q4_k_m\"):\n", - " from transformers.models.llama.modeling_llama import logger\n", - " import os\n", - "\n", - " logger.warning_once(\n", - " \"Unsloth: `colab_quantize_to_gguf` is still in development mode.\\n\"\\\n", - " \"If anything errors or breaks, please file a ticket on Github.\\n\"\\\n", - " \"Also, if you used this successfully, please tell us on Discord!\"\n", + "if False:\n", + " from peft import AutoModelForPeftCausalLM\n", + " from transformers import AutoTokenizer\n", + " model = AutoModelForPeftCausalLM.from_pretrained(\n", + " \"lora_model\", # YOUR MODEL YOU USED FOR TRAINING\n", + " load_in_4bit = load_in_4bit,\n", " )\n", - "\n", - " # From https://mlabonne.github.io/blog/posts/Quantize_Llama_2_models_using_ggml.html\n", - " ALLOWED_QUANTS = \\\n", - " {\n", - " \"q2_k\" : \"Uses Q4_K for the attention.vw and feed_forward.w2 tensors, Q2_K for the other tensors.\",\n", - " \"q3_k_l\" : \"Uses Q5_K for the attention.wv, attention.wo, and feed_forward.w2 tensors, else Q3_K\",\n", - " \"q3_k_m\" : \"Uses Q4_K for the attention.wv, attention.wo, and feed_forward.w2 tensors, else Q3_K\",\n", - " \"q3_k_s\" : \"Uses Q3_K for all tensors\",\n", - " \"q4_0\" : \"Original quant method, 4-bit.\",\n", - " \"q4_1\" : \"Higher accuracy than q4_0 but not as high as q5_0. However has quicker inference than q5 models.\",\n", - " \"q4_k_m\" : \"Uses Q6_K for half of the attention.wv and feed_forward.w2 tensors, else Q4_K\",\n", - " \"q4_k_s\" : \"Uses Q4_K for all tensors\",\n", - " \"q5_0\" : \"Higher accuracy, higher resource usage and slower inference.\",\n", - " \"q5_1\" : \"Even higher accuracy, resource usage and slower inference.\",\n", - " \"q5_k_m\" : \"Uses Q6_K for half of the attention.wv and feed_forward.w2 tensors, else Q5_K\",\n", - " \"q5_k_s\" : \"Uses Q5_K for all tensors\",\n", - " \"q6_k\" : \"Uses Q8_K for all tensors\",\n", - " \"q8_0\" : \"Almost indistinguishable from float16. High resource use and slow. Not recommended for most users.\",\n", - " }\n", - "\n", - " if quantization_method not in ALLOWED_QUANTS.keys():\n", - " error = f\"Unsloth: Quant method = [{quantization_method}] not supported. Choose from below:\\n\"\n", - " for key, value in ALLOWED_QUANTS.items():\n", - " error += f\"[{key}] => {value}\\n\"\n", - " raise RuntimeError(error)\n", - " pass\n", - "\n", - " print_info = \\\n", - " f\"==((====))== Unsloth: Conversion from QLoRA to GGUF information\\n\"\\\n", - " f\" \\\\\\ /| [0] Installing llama.cpp will take 3 minutes.\\n\"\\\n", - " f\"O^O/ \\_/ \\\\ [1] Converting HF to GUUF 16bits will take 3 minutes.\\n\"\\\n", - " f\"\\ / [2] Converting GGUF 16bits to q4_k_m will take 20 minutes.\\n\"\\\n", - " f' \"-____-\" In total, you will have to wait around 26 minutes.\\n'\n", - " print(print_info)\n", - "\n", - " if not os.path.exists(\"llama.cpp\"):\n", - " print(\"Unsloth: [0] Installing llama.cpp. This will take 3 minutes...\")\n", - " !git clone https://github.com/ggerganov/llama.cpp\n", - " !cd llama.cpp && make clean && LLAMA_CUBLAS=1 make -j\n", - " !pip install gguf protobuf\n", - " pass\n", - "\n", - " print(\"Unsloth: [1] Converting HF into GGUF 16bit. This will take 3 minutes...\")\n", - " !python llama.cpp/convert.py {save_directory} \\\n", - " --outfile {save_directory}-unsloth.gguf \\\n", - " --outtype f16\n", - "\n", - " print(\"Unsloth: [2] Converting GGUF 16bit into q4_k_m. This will take 20 minutes...\")\n", - " final_location = f\"./{save_directory}-{quantization_method}-unsloth.gguf\"\n", - " !./llama.cpp/quantize ./{save_directory}-unsloth.gguf \\\n", - " {final_location} {quantization_method}\n", - "\n", - " print(f\"Unsloth: Output location: {final_location}\")\n", - "pass\n" + " tokenizer = AutoTokenizer.from_pretrained(\"lora_model\")" ], "metadata": { - "cellView": "form", - "id": "nCVtR2ElF1GX" + "id": "yFfaXG0WsQuE" }, "execution_count": null, "outputs": [] @@ -1263,32 +1283,31 @@ { "cell_type": "markdown", "source": [ - "To save to `GGUF` / `llama.cpp`, we support it natively now! You can also go to our dedicated GGUF notebook [here](https://colab.research.google.com/drive/14DW0VwuqL2O3tqGlX7aUF6TOBA8S59M4?usp=sharing). Select either `save locally` for local saving or `save locally and quantize to 4bit` for 4bit quantization for llama.cpp / GGUF." + "### Saving to float16 for VLLM\n", + "\n", + "We also support saving to `float16` directly. Select `merged_16bit` for float16 or `merged_4bit` for int4. We also allow `lora` adapters as a fallback. Use `push_to_hub_merged` to upload to your Hugging Face account!" ], "metadata": { - "id": "TCv4vXHd61i7" + "id": "f422JgM9sdVT" } }, { "cell_type": "code", "source": [ - "from unsloth import unsloth_save_model\n", - "\n", - "# Change to `save locally` to save a float16 GGUF file or `\"save locally and quantize to 4bit\"`\n", - "# to quantize down to 4bit\n", - "SAVE_STRATEGY = \"none\"\n", - "\n", - "if SAVE_STRATEGY == \"save locally\":\n", + "# Merge to 16bit\n", + "if False: model.save_pretrained_merged(\"x\", tokenizer, save_method = \"merged_16bit\",)\n", + "if False: model.push_to_hub_merged(\"hf_user/x\", tokenizer, save_method = \"merged_16bit\", token = \"\")\n", "\n", - " unsloth_save_model(model, tokenizer, \"output_model\")\n", + "# Merge to 4bit\n", + "if False: model.save_pretrained_merged(\"x\", tokenizer, save_method = \"merged_4bit\",)\n", + "if False: model.push_to_hub_merged(\"hf_user/x\", tokenizer, save_method = \"merged_4bit\", token = \"\")\n", "\n", - "elif SAVE_STRATEGY == \"save locally and quantize to 4bit\":\n", - "\n", - " unsloth_save_model(model, tokenizer, \"output_model\")\n", - " colab_quantize_to_gguf(\"output_model\", quantization_method = \"q4_k_m\")" + "# Just LoRA adapters\n", + "if False: model.save_pretrained_merged(\"x\", tokenizer, save_method = \"lora\",)\n", + "if False: model.push_to_hub_merged(\"hf_user/x\", tokenizer, save_method = \"lora\", token = \"\")" ], "metadata": { - "id": "FqfebeAdT073" + "id": "iHjt_SMYsd3P" }, "execution_count": null, "outputs": [] @@ -1296,44 +1315,42 @@ { "cell_type": "markdown", "source": [ - "Now, use the `output_model.gguf` file or `output_model-q4_k_m-unsloth.gguf` file in `llama.cpp` or a UI based system like `GPT4All`. You can install GPT4All by going [here](https://gpt4all.io/index.html)." + "### GGUF / llama.cpp Conversion\n", + "To save to `GGUF` / `llama.cpp`, we support it natively now! We clone `llama.cpp` and we default save it to `q8_0`. We allow all methods like `q4_k_m`. Use `save_pretrained_gguf` for local saving and `push_to_hub_gguf` for uploading to HF." ], "metadata": { - "id": "bDp0zNpwe6U_" + "id": "TCv4vXHd61i7" } }, { - "cell_type": "markdown", + "cell_type": "code", "source": [ - "Otherwise, to merge the LoRA adapters into the 4bit model:" + "# Save to 8bit Q8_0\n", + "if False: model.save_pretrained_gguf(\"x\", tokenizer,)\n", + "if False: model.push_to_hub_gguf(\"hf_user/x\", tokenizer, token = \"\")\n", + "\n", + "# Save to 16bit GGUF\n", + "if False: model.save_pretrained_gguf(\"x\", tokenizer, quantization_method = \"f16\")\n", + "if False: model.push_to_hub_gguf(\"hf_user/x\", tokenizer, quantization_method = \"f16\", token = \"\")\n", + "\n", + "# Save to q4_k_m GGUF\n", + "if False: model.save_pretrained_gguf(\"x\", tokenizer, quantization_method = \"q4_k_m\")\n", + "if False: model.push_to_hub_gguf(\"hf_user/x\", tokenizer, quantization_method = \"q4_k_m\", token = \"\")" ], "metadata": { - "id": "acUVCgzzU1Wv" - } + "id": "FqfebeAdT073" + }, + "execution_count": null, + "outputs": [] }, { - "cell_type": "code", + "cell_type": "markdown", "source": [ - "model = model.merge_and_unload()" + "Now, use the `x.gguf` file or `x-unsloth-Q4_K_M.gguf` file in `llama.cpp` or a UI based system like `GPT4All`. You can install GPT4All by going [here](https://gpt4all.io/index.html)." ], "metadata": { - "id": "xcRjsZe0RK1b", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "896496df-5d9a-49e7-f60b-ce2304f70c39" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "stream", - "name": "stderr", - "text": [ - "/usr/local/lib/python3.10/dist-packages/peft/tuners/lora/bnb.py:229: UserWarning: Merge lora module to 4-bit linear may get different generations due to rounding errors.\n", - " warnings.warn(\n" - ] - } - ] + "id": "bDp0zNpwe6U_" + } }, { "cell_type": "markdown", @@ -1374,7 +1391,7 @@ }, "widgets": { "application/vnd.jupyter.widget-state+json": { - "21fd9c71893a4a99b96e79f39213af35": { + "3a4a21c80b1e4232825d13fbceb4e051": { "model_module": "@jupyter-widgets/controls", "model_name": "HBoxModel", "model_module_version": "1.5.0", @@ -1389,14 +1406,14 @@ "_view_name": "HBoxView", "box_style": "", "children": [ - "IPY_MODEL_99c5700859124f37be1156508a685e28", - 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"layout": "IPY_MODEL_5151bb9352074dbc965d3503ed94e5c5", + "layout": "IPY_MODEL_52995fc47dcc4b6bbbba3a158370da1b", "max": 1055, "min": 0, "orientation": "horizontal", - "style": "IPY_MODEL_4431246852364e3990f46df5186f5cc7", + "style": "IPY_MODEL_5a88fa6168b34baca965387de6f9cbf4", "value": 1055 } }, - "7adea9776e2348619d345699778531c4": { + "9309cba5e6a64d62af82b8e8403336ba": { "model_module": "@jupyter-widgets/controls", "model_name": "HTMLModel", "model_module_version": "1.5.0", @@ -1456,13 +1473,13 @@ "_view_name": "HTMLView", "description": "", "description_tooltip": null, - "layout": "IPY_MODEL_f215b46454e44fb5a474c765e23888ac", + "layout": "IPY_MODEL_320fff7da29b49d0980a5e19ecdd7e11", "placeholder": "​", - "style": "IPY_MODEL_acf89e72380c4e388dc72e2684a4bea1", - "value": " 1.05k/1.05k [00:00<00:00, 11.4kB/s]" + "style": "IPY_MODEL_b0caf6ffd475467b9c26e9258902a6d7", + "value": " 1.05k/1.05k [00:00<00:00, 26.2kB/s]" } }, - "3f5189d724e04588b3782479297877ce": { + "c339d8fa8d7d4ad8874bc59f47ef193d": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "model_module_version": "1.2.0", @@ -1514,7 +1531,7 @@ "width": null } }, - 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