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" @@ -31,7 +31,7 @@ "major_version, minor_version = torch.cuda.get_device_capability()\n", "if major_version >= 8:\n", " # Use this for new GPUs like Ampere, Hopper GPUs (RTX 30xx, RTX 40xx, A100, H100, L40)\n", - " !pip install \"unsloth[colab_ampere] @ git+https://github.com/unslothai/unsloth.git\"\n", + " !pip install \"unsloth[colab-ampere] @ git+https://github.com/unslothai/unsloth.git\"\n", "else:\n", " # Use this for older GPUs (V100, Tesla T4, RTX 20xx)\n", " !pip install \"unsloth[colab] @ git+https://github.com/unslothai/unsloth.git\"\n", @@ -57,102 +57,96 @@ "metadata": { "colab": { "base_uri": "https://localhost:8080/", - "height": 527, + "height": 403, "referenced_widgets": [ - "3a4a21c80b1e4232825d13fbceb4e051", - "f16087f7a4f94b18ae92a2615bd3719c", - "06eac325390f467d9825c489eb4cb442", - "9309cba5e6a64d62af82b8e8403336ba", - "c339d8fa8d7d4ad8874bc59f47ef193d", - "c73d502c8be34400bd4fd31f1c14f354", - "34e7e54d02cf4120acc14cfa9430674d", - "52995fc47dcc4b6bbbba3a158370da1b", - "5a88fa6168b34baca965387de6f9cbf4", - "320fff7da29b49d0980a5e19ecdd7e11", - "b0caf6ffd475467b9c26e9258902a6d7", - "8978f89cdfd64c41b504debc99fa000b", - "fad0db53f8d84346996a49bb8548a28f", - "d719af3c7e434c4fbd88ce6442ff94f3", - "1dffcda7345949cca7a01373e87bef91", - "2da70f09f484477ebbb8f81458556515", - "db33c13036a74c5eb2bcab082f9ce07d", - "dca1b3a080e74bf3b00a27dd1ed4b99c", - "838d7a4c2c3a4fe99a3eb0689fa7ef27", - "e0ed107826af43db93b4fbd171bb9f36", - "1eef883b99634b8abefd9c008eddbd1f", - "a858db7a4c8a40a5bedc4ea817491c4c", - "c52fb02a25774735bc6cd1fd4085ffb2", - "2c64fd94311247d89b2bbb37b0a72d52", - "28ffcbb08f9c443c87df1b6d6d5f38bb", - "699373e9a43d4433b1b3eacdc4eb338d", - "ed41c69c299e42ccaea8cf4e31162b8e", - "f85bc895a6ee4c5b8cfd26a1c827888f", - "e5f70528f2834d2b861dc303346eff3a", - "e76d203cc320464cadfc7b2649d8fdb7", - 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"outputId": "a1a670b2-ee62-4bbf-901a-38f79389db28" + "outputId": "5eff0d61-05b4-471c-eea2-c2e84a915109" }, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ - "/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", - "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n", - "You will be able to reuse this secret in all of your notebooks.\n", - "Please note that authentication is recommended but still optional to access public models or datasets.\n", + "/usr/local/lib/python3.10/dist-packages/unsloth/__init__.py:66: UserWarning: Running `ldconfig /usr/lib64-nvidia` to link CUDA.\n", " warnings.warn(\n" ] }, @@ -165,21 +159,26 @@ "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, - "model_id": "3a4a21c80b1e4232825d13fbceb4e051" + "model_id": "4b6316870ae142ba9abf2b399edaaeea" } }, "metadata": {} }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "==((====))== Unsloth: Fast Mistral patching release 2024.2\n", + " \\\\ /| GPU: Tesla T4. Max memory: 14.748 GB. Platform = Linux.\n", + "O^O/ \\_/ \\ Pytorch: 2.1.0+cu121. CUDA = 7.5. CUDA Toolkit = 12.1.\n", + "\\ / Bfloat16 = FALSE. Xformers = 0.0.22.post7. FA = False.\n", + " \"-____-\" Apache 2 free license: http://github.com/unslothai/unsloth\n" + ] + }, { "output_type": "stream", "name": "stderr", "text": [ - "==((====))== Unsloth: Fast Mistral patching release 2024.1\n", - " \\\\ /| GPU: Tesla T4. Max memory: 14.748 GB\n", - "O^O/ \\_/ \\ CUDA capability = 7.5. Xformers = 0.0.22.post7. FA = False.\n", - "\\ / Pytorch version: 2.1.0+cu121. CUDA Toolkit = 12.1\n", - " \"-____-\" bfloat16 = FALSE. Platform = Linux\n", - "\n", "You passed `quantization_config` to `from_pretrained` but the model you're loading already has a `quantization_config` attribute. The `quantization_config` attribute will be overwritten with the one you passed to `from_pretrained`.\n" ] }, @@ -192,7 +191,7 @@ "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, - "model_id": "8978f89cdfd64c41b504debc99fa000b" + "model_id": "78d1378806944b54b6dc7d9088d0540a" } }, "metadata": {} @@ -206,7 +205,7 @@ "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, - "model_id": "c52fb02a25774735bc6cd1fd4085ffb2" + "model_id": "661c67646b5948449c2d71f5814776b4" } }, "metadata": {} @@ -220,7 +219,7 @@ "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, - "model_id": "6a46284e269e4044b2e082098fb0988f" + "model_id": "3f40e8b99e1043e8917ff2c3e1e3dd29" } }, "metadata": {} @@ -234,7 +233,7 @@ "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, - "model_id": "3e463765bc424c67b88b5b522fd12f36" + "model_id": "ced6f4b20c304ab6b19858766505a596" } }, "metadata": {} @@ -248,7 +247,7 @@ "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, - "model_id": "ae42b52c748240f2bcbfea01b4573622" + "model_id": "3f03fca984754fd7897ac389e661511e" } }, "metadata": {} @@ -262,7 +261,7 @@ "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, - "model_id": "b5eae4bf90bc4f10baa3cef668109188" + "model_id": "ff1229fe81b2467892fcebb5516c586a" } }, "metadata": {} @@ -311,14 +310,14 @@ "colab": { "base_uri": "https://localhost:8080/" }, - "outputId": "7a19202f-6ef6-4a71-cf88-004c01769255" + "outputId": "b630cc80-ff95-45a2-cc0d-38666010d73b" }, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ - "Unsloth 2024.1 patched 32 layers with 32 QKV layers, 32 O layers and 32 MLP layers.\n" + "Unsloth 2024.2 patched 32 layers with 32 QKV layers, 32 O layers and 32 MLP layers.\n" ] } ], @@ -366,53 +365,53 @@ "base_uri": "https://localhost:8080/", "height": 145, "referenced_widgets": [ - "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", - 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"outputId": "26e1bc8e-c4e8-472e-ca91-670e3381c6b3" + "outputId": "9f40f734-788c-4793-c1af-e9d003337612" }, "outputs": [ { @@ -424,7 +423,7 @@ "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, - "model_id": "e0ea75df3b5c49ce89427e4d245a7646" + "model_id": "9ae302db43ac477fbfac4524d9aa3382" } }, "metadata": {} @@ -438,7 +437,7 @@ "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, - "model_id": "80b9d976e30e40ecaf35a606bca5d647" + "model_id": "e7163211ff064cf68f2a9a92a1a44b14" } }, "metadata": {} @@ -452,7 +451,7 @@ "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, - "model_id": "6b104e7242cf4a608cfd0c4fc6f44db1" + "model_id": "49606713136e4b4f809e88edec0ee31f" } }, "metadata": {} @@ -466,7 +465,7 @@ "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, - "model_id": "2ffddabe91124b279f9a3ac41d1ead9d" + "model_id": "add655fe6f304c5b8c7f5c374f73fe84" } }, "metadata": {} @@ -520,124 +519,24 @@ "id": "95_Nn-89DhsL", "colab": { "base_uri": "https://localhost:8080/", - "height": 177, + "height": 49, "referenced_widgets": [ - "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", - 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" @@ -1013,15 +905,15 @@ "colab": { "base_uri": "https://localhost:8080/" }, - "outputId": "ff1b0842-5966-4dc2-bd98-c20832526b31" + "outputId": "cf63d152-e152-468c-ba0d-938e0d2f71a0" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ - "484.744 seconds used for training.\n", - "8.08 minutes used for training.\n", + "488.38 seconds used for training.\n", + "8.14 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", @@ -1076,7 +968,7 @@ "colab": { "base_uri": "https://localhost:8080/" }, - "outputId": "9f3bd036-9ed6-40af-b4e8-6193b2088d3b" + "outputId": "5b71f982-38c0-44c8-a4e5-58cd20b5a585" }, "execution_count": null, "outputs": [ @@ -1131,7 +1023,7 @@ "base_uri": "https://localhost:8080/" }, "id": "e2pEuRb1r2Vg", - "outputId": "2188f68b-6b72-46e6-ea85-f8134df0aa46" + "outputId": "084aab62-2122-436a-c0cb-8871986640eb" }, "execution_count": null, "outputs": [ @@ -1226,7 +1118,7 @@ "colab": { "base_uri": "https://localhost:8080/" }, - "outputId": "68f54910-2634-4cce-f3a2-4cbb90e2e990" + "outputId": "05e5a193-dab0-41db-e07c-4b3afbdd7932" }, "execution_count": null, "outputs": [ @@ -1241,11 +1133,11 @@ "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 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\"]" + "[' 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 structures in the world.']" ] }, "metadata": {}, - "execution_count": 13 + "execution_count": 12 } ] }, @@ -1263,9 +1155,9 @@ "source": [ "if False:\n", " # I highly do NOT suggest - use Unsloth if possible\n", - " from peft import AutoModelForPeftCausalLM\n", + " from peft import AutoPeftModelForCausalLM\n", " from transformers import AutoTokenizer\n", - " model = AutoModelForPeftCausalLM.from_pretrained(\n", + " model = AutoPeftModelForCausalLM.from_pretrained(\n", " \"lora_model\", # YOUR MODEL YOU USED FOR TRAINING\n", " load_in_4bit = load_in_4bit,\n", " )\n", @@ -1395,7 +1287,7 @@ }, "widgets": { "application/vnd.jupyter.widget-state+json": { - "3a4a21c80b1e4232825d13fbceb4e051": { + "4b6316870ae142ba9abf2b399edaaeea": { "model_module": "@jupyter-widgets/controls", "model_name": "HBoxModel", "model_module_version": "1.5.0", @@ -1410,14 +1302,14 @@ "_view_name": "HBoxView", "box_style": "", "children": [ - "IPY_MODEL_f16087f7a4f94b18ae92a2615bd3719c", - "IPY_MODEL_06eac325390f467d9825c489eb4cb442", - "IPY_MODEL_9309cba5e6a64d62af82b8e8403336ba" + "IPY_MODEL_c3315eb9e7ef42c298b7ba5f7636c373", + "IPY_MODEL_b553db45b4204434b015593b84861d9d", + "IPY_MODEL_ee88fbb010e349d4a840275150cc59eb" ], - "layout": "IPY_MODEL_c339d8fa8d7d4ad8874bc59f47ef193d" + "layout": "IPY_MODEL_5d9ac0fdb1c24c59a89390b3fbb6481e" } }, - "f16087f7a4f94b18ae92a2615bd3719c": { + "c3315eb9e7ef42c298b7ba5f7636c373": { "model_module": "@jupyter-widgets/controls", "model_name": "HTMLModel", "model_module_version": "1.5.0", @@ -1432,13 +1324,13 @@ "_view_name": "HTMLView", "description": "", "description_tooltip": null, - "layout": "IPY_MODEL_c73d502c8be34400bd4fd31f1c14f354", + "layout": "IPY_MODEL_ee2d57cc909b47e7a96a6348c3bc64c0", "placeholder": "​", - "style": "IPY_MODEL_34e7e54d02cf4120acc14cfa9430674d", - "value": "config.json: 100%" + "style": "IPY_MODEL_e8f5782342af48549b22edf99517e71d", + "value": "config.json: 100%" } }, - "06eac325390f467d9825c489eb4cb442": { + "b553db45b4204434b015593b84861d9d": { "model_module": "@jupyter-widgets/controls", "model_name": "FloatProgressModel", "model_module_version": "1.5.0", @@ -1454,15 +1346,15 @@ "bar_style": "success", "description": "", "description_tooltip": null, - 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