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" @@ -59,89 +59,89 @@ "metadata": { "colab": { "base_uri": "https://localhost:8080/", - "height": 438, + "height": 544, "referenced_widgets": [ - "33654c38847c4fbea61fd33aba4897e6", - "53b5f3f173794702b88f3ec02ea27c49", - "e863762a7346415da9cebb7c89415b34", - "20457665371a4183813d08b213b62936", - "88f6415a2e3b4020a586c11d86bae0fb", - "66a31666936342b882d8d677a2586eba", - "c8e42da29b8843a1a91df4f62597f897", - "0abfe64c39ce4984b2a7423af42c05e3", - "8b0f6cadfb8f4b3ba14059629cb43ec7", - "fbffe559df824fa392abaaafd5c27f4d", - "c8fd636900ea4342927f7b929e1165cc", - "f37f1288a1d345daac6f00df2148dffc", - "022a936488a8492996a5177d8da49757", - "83a02e755c73486ab97793e9aa2afdda", - "bbc6352c1927436eb98df146ef13ed1a", - "8adc8c844b914321b9ff037d5cf4004d", - "a0aa772f33ff49c4810c89d239b8fd79", - "de52728ff67d4147b4e3e48bd25e7db5", - 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"outputId": "9e920276-3f96-48aa-c136-8e97d69e7c53" + "outputId": "c8350926-5143-40ce-fb35-2bffbe499cac" }, "outputs": [ { @@ -150,6 +150,12 @@ "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", + " 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", " warnings.warn(\n" ] }, @@ -162,7 +168,7 @@ "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, - "model_id": "33654c38847c4fbea61fd33aba4897e6" + "model_id": "21fd9c71893a4a99b96e79f39213af35" } }, "metadata": {} @@ -171,7 +177,7 @@ "output_type": "stream", "name": "stderr", "text": [ - "==((====))== Unsloth: Fast Mistral patching release 2023.12\n", + "==((====))== 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", @@ -189,7 +195,7 @@ "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, - "model_id": "f37f1288a1d345daac6f00df2148dffc" + "model_id": "27f30a48f4f04b06bbc392989c99304d" } }, "metadata": {} @@ -203,7 +209,7 @@ "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, - "model_id": "5171167f780b4fffb6a5cf20dcdd57cc" + "model_id": "bea3f4e007fb4f8d92766ec2455e1c17" } }, "metadata": {} @@ -217,7 +223,7 @@ "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, - "model_id": "a436d851fce3499094c0eecf714c7eea" + "model_id": "d2945f43455245c491b3cafc6f6b463b" } }, "metadata": {} @@ -231,7 +237,7 @@ "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, - "model_id": "03d87ed1a3034b41bced14363e6586a6" + "model_id": "e939fbb3ee754005af82be95a89463b8" } }, "metadata": {} @@ -245,7 +251,7 @@ "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, - "model_id": "7ef49647229b4073995c9775a0c474d4" + "model_id": "4f7a08f57ddf4824b5e82d04ebbeb96c" } }, "metadata": {} @@ -259,7 +265,7 @@ "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, - "model_id": "592e9bb20547467297a7221a8cff2515" + "model_id": "b24b629c56774be2a728f3c7f18f4360" } }, "metadata": {} @@ -307,14 +313,14 @@ "colab": { "base_uri": "https://localhost:8080/" }, - "outputId": "caaacb5f-aa34-42a3-e3f2-af261cca9dde" + "outputId": "ac6dcf4f-ecfb-41f2-8bcf-c63bc171453b" }, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ - "Unsloth 2023.12 patched 32 layers with 32 QKV layers, 32 O layers and 32 MLP layers.\n" + "Unsloth 2024.1 patched 32 layers with 32 QKV layers, 32 O layers and 32 MLP layers.\n" ] } ], @@ -355,54 +361,54 @@ "base_uri": "https://localhost:8080/", "height": 145, "referenced_widgets": [ - "ea8764cf06604ba4ac96fd28a66a7461", - "45992f61f8cf4da9825463f5ae290f66", - "c5ff72ce12494a7eadbafc1192dac93c", - "1bfb3f5439204526a36e6bfa2ecba5de", - "82143e4f3d3c488b89628698b8970ab7", - "cabfb611179549d4ac9a0537716b5929", - "b0d4567f91b24ed4bf30d43bf728f87f", - "9b24944c91ee45e9911a4d8ddef78e75", - "7d5b4744fb934126afcdd430c8f748f6", - "c56999b4c54244539faceb214625e8b7", - "2b4f862927924016b3a99abd8b4808f7", - "f6b71b55bc144a638cf642061d661354", - "37b002b142a24b5e809446286eb54f41", - "d1ecf548d52c47f5b4e55b2ecae44e7a", - "ddc3498d387e48399d1d4370d4f49a4f", - "71e7fbdf0c65424ba5ed7822437151bc", - "0ed128938db14b6b8f0c865909ff836a", - "e0ecdc1fb1404975a6076a41835aa473", - "da967c697bb24274b63f4d59e5188524", - "db887203ee8f46f4bf07530b529bd420", - "5b91df05162c465f9a612c652cc4b2fa", - "ca91be5dc4e2481c92a263d1e3c6fb68", - "669ae84508644d1590c6240b53f75f2c", - "2d04b4a899c44836b1f2685569b9fea5", - "af88ece8f6b24b08a753b229a364d9f2", - "388ca302d0674d069bef7fe6b876e064", - "5ad0263fe4984b738ebe1d4d8b0377b7", - "4db33220da1545618b782b30fab2bd88", - "f2a6314d757b4da782f9f1a450d88583", - "1d31160c12ad4a32b498323bfba257cf", - "aa8a99500a98486d8039fa5fb9fdb1ea", - 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"outputId": "cd525794-f56f-4aaf-9d8f-4049d6390c42" + "outputId": "72d478d8-c49d-4bbb-ef19-caae4d0f3895" }, "outputs": [ { @@ -414,7 +420,7 @@ "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, - "model_id": "ea8764cf06604ba4ac96fd28a66a7461" + "model_id": "a8a8adf4394f4ea4be00c9853c2a3ee5" } }, "metadata": {} @@ -428,7 +434,7 @@ "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, - "model_id": "f6b71b55bc144a638cf642061d661354" + "model_id": "4eac852431744e40b7dcf9ecca1e1fd1" } }, "metadata": {} @@ -442,7 +448,7 @@ "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, - "model_id": "669ae84508644d1590c6240b53f75f2c" + "model_id": "c57f151c22ca4bb49c25613b06036b00" } }, "metadata": {} @@ -456,7 +462,7 @@ "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, - "model_id": "1c40faad752444a0830cb0476864b06a" + "model_id": "cc4186d840ae4644a45d330480b15322" } }, "metadata": {} @@ -509,34 +515,134 @@ "id": "95_Nn-89DhsL", "colab": { "base_uri": "https://localhost:8080/", - "height": 49, + "height": 177, "referenced_widgets": [ - "6990362a61794020b01d2cda10fbffcf", - "ccae22f63683472e851fbf50c960d511", - "d93703cc45164742ae17aac139b3f4d9", - "5125d99599224261be728ccb82c8e228", - "c8fb186fab87419dbef4c7061fc56a1b", - "0edb2c7d169f4095a6f98b085c9db5b4", - "efab873fe53d40929e2620f5e1466b62", - "9dab8f7114fe48c9b7443579bd34cd47", - "7852d732d8bc4b2a9ec14378ec3c2480", - "f1a7c874ef0941b5b07d3c611329a291", - "336de31e1f004f728ca58d71f128dec8" + "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" ] }, - 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" @@ -900,19 +1006,19 @@ "colab": { "base_uri": "https://localhost:8080/" }, - "outputId": "3fc393e1-6f2b-43e5-9e51-5f74227d5e5f" + "outputId": "8164562f-9c1d-4d1c-a596-ebdfb95001a1" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ - "479.1353 seconds used for training.\n", - "7.99 minutes used for training.\n", - "Peak reserved memory = 6.564 GB.\n", - "Peak reserved memory for training = 1.939 GB.\n", - "Peak reserved memory % of max memory = 44.508 %.\n", - "Peak reserved memory for training % of max memory = 13.148 %.\n" + "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" ] } ], @@ -961,7 +1067,7 @@ "colab": { "base_uri": "https://localhost:8080/" }, - "outputId": "f3b0dba3-ad15-4a5b-bebf-dea3fba93645" + "outputId": "a14cd344-4807-4389-f357-bbd5a6661d67" }, "execution_count": null, "outputs": [ @@ -977,7 +1083,7 @@ "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:\\n13, 21, 34, 55, 89, 144, 233, 377, 610, 987, 1597, 2584, 4181, 6765, 10946, 17711, 28657, 46368, 75025, 121393, 196418, 317811, ']" + "[' 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']" ] }, "metadata": {}, @@ -1011,7 +1117,9 @@ { "cell_type": "markdown", "source": [ - "Now if you want to load the LoRA adapters we just saved, we can!" + "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`" ], "metadata": { "id": "AEEcJ4qfC7Lp" @@ -1021,22 +1129,61 @@ "cell_type": "code", "source": [ "from peft import PeftModel\n", - "model = PeftModel.from_pretrained(model, \"lora_model\")" + "\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", + "\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", + "{}\n", + "\n", + "### Input:\n", + "{}\n", + "\n", + "### Response:\n", + "{}\"\"\"\n", + "\n", + "inputs = tokenizer(\n", + "[\n", + " alpaca_prompt.format(\n", + " \"What is a famous tall tower in Paris?\", # instruction\n", + " \"\", # input\n", + " \"\", # output - leave this blank for generation!\n", + " )\n", + "]*1, return_tensors = \"pt\").to(\"cuda\")\n", + "\n", + "outputs = model.generate(**inputs, max_new_tokens = 128, use_cache = True)\n", + "tokenizer.batch_decode(outputs)" ], "metadata": { - "id": "MKX_XKs_BNZR" + "id": "MKX_XKs_BNZR", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "9a6df427-0e12-45fe-9467-78e2095346ce" }, "execution_count": null, - "outputs": [] - }, - { - "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." - ], - "metadata": { - "id": "TCv4vXHd61i7" - } + "outputs": [ + { + "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.\"]" + ] + }, + "metadata": {}, + "execution_count": 11 + } + ] }, { "cell_type": "code", @@ -1113,6 +1260,15 @@ "execution_count": null, "outputs": [] }, + { + "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." + ], + "metadata": { + "id": "TCv4vXHd61i7" + } + }, { "cell_type": "code", "source": [ @@ -1137,6 +1293,15 @@ "execution_count": null, "outputs": [] }, + { + "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)." + ], + "metadata": { + "id": "bDp0zNpwe6U_" + } + }, { "cell_type": "markdown", "source": [ @@ -1156,7 +1321,7 @@ "colab": { "base_uri": "https://localhost:8080/" }, - "outputId": "b40d4730-b9dc-4a4b-e42c-c191060ff66c" + "outputId": "896496df-5d9a-49e7-f60b-ce2304f70c39" }, "execution_count": null, "outputs": [ @@ -1209,7 +1374,7 @@ }, "widgets": { "application/vnd.jupyter.widget-state+json": { - "33654c38847c4fbea61fd33aba4897e6": { + "21fd9c71893a4a99b96e79f39213af35": { "model_module": "@jupyter-widgets/controls", "model_name": "HBoxModel", "model_module_version": "1.5.0", @@ -1224,14 +1389,14 @@ "_view_name": "HBoxView", "box_style": "", "children": [ - "IPY_MODEL_53b5f3f173794702b88f3ec02ea27c49", - "IPY_MODEL_e863762a7346415da9cebb7c89415b34", - "IPY_MODEL_20457665371a4183813d08b213b62936" + "IPY_MODEL_99c5700859124f37be1156508a685e28", + "IPY_MODEL_5d9562bd69f64f2bb0f3a7b271af3d9e", + 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"description_tooltip": null, - "layout": "IPY_MODEL_84922628bb85478eb2acae6082b11dd0", + "layout": "IPY_MODEL_146cbaafb0114bce94b306cd96b958e8", "placeholder": "​", - "style": "IPY_MODEL_0afc5bad19c8499ca44572d338fd0746", + "style": "IPY_MODEL_f55c54affd70418b8c3a5a22603d4320", "value": "Map: 100%" } }, - "a8e388cca90846af9c32bdf372766b6e": { + "ca7a0ff127f84bedaad3009d6c9fa0fb": { "model_module": "@jupyter-widgets/controls", "model_name": "FloatProgressModel", "model_module_version": "1.5.0", @@ -4688,15 +4853,15 @@ "bar_style": "success", "description": "", "description_tooltip": null, - "layout": "IPY_MODEL_4f0444c5544c48318e9c90f7265666f9", + "layout": "IPY_MODEL_c61e4cb0a3994bab91dd20bc02ed18ad", "max": 51760, "min": 0, "orientation": "horizontal", - "style": "IPY_MODEL_2f175e4216694b04ac6a5f56aa51c29f", + "style": "IPY_MODEL_f8233de76001468bbf94151f069c2503", "value": 51760 } }, - "75e585db02e94b358408c8268ec8a26f": { + "7d0a83dbc8774bd0832aced31e6c8901": { "model_module": 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