diff --git a/md/02.Application/03.AdvancedReasoning/Phi4/AdvancedResoningPhi4mini/mlx_ft_phi_4_reasoning_with_medicaldata.ipynb b/md/02.Application/03.AdvancedReasoning/Phi4/AdvancedResoningPhi4mini/mlx_ft_phi_4_reasoning_with_medicaldata.ipynb index b2fbdb6f33..93f3f7dbad 100644 --- a/md/02.Application/03.AdvancedReasoning/Phi4/AdvancedResoningPhi4mini/mlx_ft_phi_4_reasoning_with_medicaldata.ipynb +++ b/md/02.Application/03.AdvancedReasoning/Phi4/AdvancedResoningPhi4mini/mlx_ft_phi_4_reasoning_with_medicaldata.ipynb @@ -1,2687 +1,2689 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## **Sample : fine-tuning Phi-4-mini-reasoning with Apple MLX Framewrok**\n", - "\n", - "This is based on the data from FreedomIntelligence/medical-o1-reasoning-SFT, which enhances the ability of Phi-4-mini-reasoning to reason about medical events. If you want to learn more about the data, you can use this link to learn more about it: https://huggingface.co/datasets/FreedomIntelligence/medical-o1-reasoning-SFT \n", - "\n", - "*Note： Please use 64gb memory Apple silicon devices to run this sample*" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "! pip install -U mlx-lm" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "UifUppNJUHvb", - "outputId": "f7dcbccd-db29-4bbb-dcfb-b311e1733ecd" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Requirement already satisfied: datasets in /Users/lokinfey/miniforge3/envs/mlxdev/lib/python3.11/site-packages (3.5.1)\n", - "Requirement already satisfied: filelock in /Users/lokinfey/miniforge3/envs/mlxdev/lib/python3.11/site-packages (from datasets) (3.18.0)\n", - "Requirement already satisfied: numpy>=1.17 in /Users/lokinfey/miniforge3/envs/mlxdev/lib/python3.11/site-packages (from datasets) (2.2.5)\n", - 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Note that apple mlx names the training data by default. train is named train.jsonl and valid is named valid.json" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "id": "14nAK48WZZGU" - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/lokinfey/miniforge3/envs/mlxdev/lib/python3.11/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", - " from .autonotebook import tqdm as notebook_tqdm\n" - ] - } - ], - "source": [ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## **Sample : fine-tuning Phi-4-mini-reasoning with Apple MLX Framewrok**\n", + "\n", + "This is based on the data from FreedomIntelligence/medical-o1-reasoning-SFT, which enhances the ability of Phi-4-mini-reasoning to reason about medical events. If you want to learn more about the data, you can use this link to learn more about it: https://huggingface.co/datasets/FreedomIntelligence/medical-o1-reasoning-SFT \n", + "\n", + "*Note： Please use 64gb memory Apple silicon devices to run this sample*" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "! pip install -U mlx-lm" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "UifUppNJUHvb", + "outputId": "f7dcbccd-db29-4bbb-dcfb-b311e1733ecd" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Requirement already satisfied: datasets in /Users/lokinfey/miniforge3/envs/mlxdev/lib/python3.11/site-packages (3.5.1)\n", + "Requirement already satisfied: filelock in /Users/lokinfey/miniforge3/envs/mlxdev/lib/python3.11/site-packages (from datasets) (3.18.0)\n", + "Requirement already satisfied: 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already satisfied: python-dateutil>=2.8.2 in /Users/lokinfey/miniforge3/envs/mlxdev/lib/python3.11/site-packages (from pandas->datasets) (2.9.0.post0)\n", + "Requirement already satisfied: pytz>=2020.1 in /Users/lokinfey/miniforge3/envs/mlxdev/lib/python3.11/site-packages (from pandas->datasets) (2025.2)\n", + "Requirement already satisfied: tzdata>=2022.7 in /Users/lokinfey/miniforge3/envs/mlxdev/lib/python3.11/site-packages (from pandas->datasets) (2025.2)\n", + "Requirement already satisfied: six>=1.5 in /Users/lokinfey/miniforge3/envs/mlxdev/lib/python3.11/site-packages (from python-dateutil>=2.8.2->pandas->datasets) (1.17.0)\n" + ] + } + ], + "source": [ + "! pip install datasets" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### **Data**\n", + "\n", + "We need to divide the data into train and valid and place them in the data folder. Note that apple mlx names the training data by default. train is named train.jsonl and valid is named valid.json" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "id": "14nAK48WZZGU" + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/lokinfey/miniforge3/envs/mlxdev/lib/python3.11/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", + " from .autonotebook import tqdm as notebook_tqdm\n" + ] + } + ], + "source": [ + "from pathlib import Path\n", "from datasets import load_dataset" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 98, - "referenced_widgets": [ - "a30f63cf6c1f47cea45230aa32c3837a", - "113ef3b2af264b31a83faf9ad93c279b", - "9094a984e9e44ac8978d0b1bee410dbd", - "9cf009d9296b44e09d0ca0587737f82a", - "edd274f944fb4d5893c055463a340484", - "f38a5e4004e841eaa6a44249f635ab14", - "8b18fd76d74d435d96b254334a0f9fb6", - "58136726831943b088142d11bfa50977", - "2fa9bfc9aafa4ff58fdc28a057804132", - "35ce7a542f8744a89868561092c31972", - "8cafed7b529f451a92a3c71c95669ecc", - "e9bd836d1eb74319b6aa85fa6fcdfb37", - "384fcc344ae04a8490584f25064c5eb3", - "236febcd5c7b4f3cbb463bdc9ffdcc2e", - "3e62aec96eea44e5b494fe0296044723", - "6dd2a739d0904c46a532654523402b90", - "94188750b53943be84c3522c20a5c01a", - "4dd55c34da914019bf5b6daf713e864e", - "22b948aa7e6f4eca9d83e3f8b5228d6d", - "2e82e9674b2341748d64d233bed1c9e9", - "9fe342911d29430a9809571e4edff63e", - "b43b87d324ed47ce939f4be5f8d75ace" - ] - }, - "id": "wXCUgS9DZemr", - "outputId": "17abe0a6-9161-4b3d-a33b-a9367dc51445" - }, - "outputs": [], - "source": [ - "prompt_template = \"\"\"<|user|>{}<|end|><|assistant|>{}{}<|end|>\"\"\"\n", - "\n", - "\n", - "def formatting_prompts_func(examples):\n", - " inputs = examples[\"Question\"]\n", - " cots = examples[\"Complex_CoT\"]\n", - " outputs = examples[\"Response\"]\n", - " texts = []\n", - " for input, cot, output in zip(inputs, cots, outputs):\n", - " # text = prompt_template.format(input, cot, output) + \"<|end|>\"\n", - " text = prompt_template.format(input, cot, output) + \"<|endoftext|>\"\n", - " texts.append(text)\n", - " return {\n", - " \"text\": texts,\n", - " }\n" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "dataset = load_dataset(\"FreedomIntelligence/medical-o1-reasoning-SFT\",\"en\", trust_remote_code=True)\n", - "split_dataset = dataset[\"train\"].train_test_split(test_size=0.2, seed=200)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "train_dataset = split_dataset['train']\n", - "validation_dataset = split_dataset['test']" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Map: 100%|██████████| 15763/15763 [00:00<00:00, 100186.71 examples/s]\n", - "Creating json from Arrow format: 100%|██████████| 16/16 [00:00<00:00, 93.83ba/s]\n" - ] - }, - { - "data": { - "text/plain": [ - "46545696" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 98, + "referenced_widgets": [ + "a30f63cf6c1f47cea45230aa32c3837a", + "113ef3b2af264b31a83faf9ad93c279b", + "9094a984e9e44ac8978d0b1bee410dbd", + "9cf009d9296b44e09d0ca0587737f82a", + "edd274f944fb4d5893c055463a340484", + "f38a5e4004e841eaa6a44249f635ab14", + "8b18fd76d74d435d96b254334a0f9fb6", + "58136726831943b088142d11bfa50977", + "2fa9bfc9aafa4ff58fdc28a057804132", + "35ce7a542f8744a89868561092c31972", + "8cafed7b529f451a92a3c71c95669ecc", + "e9bd836d1eb74319b6aa85fa6fcdfb37", + "384fcc344ae04a8490584f25064c5eb3", + "236febcd5c7b4f3cbb463bdc9ffdcc2e", + "3e62aec96eea44e5b494fe0296044723", + "6dd2a739d0904c46a532654523402b90", + "94188750b53943be84c3522c20a5c01a", + "4dd55c34da914019bf5b6daf713e864e", + "22b948aa7e6f4eca9d83e3f8b5228d6d", + "2e82e9674b2341748d64d233bed1c9e9", + "9fe342911d29430a9809571e4edff63e", + "b43b87d324ed47ce939f4be5f8d75ace" + ] + }, + "id": "wXCUgS9DZemr", + "outputId": "17abe0a6-9161-4b3d-a33b-a9367dc51445" + }, + "outputs": [], + "source": [ + "prompt_template = \"\"\"<|user|>{}<|end|><|assistant|>{}{}<|end|>\"\"\"\n", + "\n", + "\n", + "def formatting_prompts_func(examples):\n", + " inputs = examples[\"Question\"]\n", + " cots = examples[\"Complex_CoT\"]\n", + " outputs = examples[\"Response\"]\n", + " texts = []\n", + " for input, cot, output in zip(inputs, cots, outputs):\n", + " # text = prompt_template.format(input, cot, output) + \"<|end|>\"\n", + " text = prompt_template.format(input, cot, output) + \"<|endoftext|>\"\n", + " texts.append(text)\n", + " return {\n", + " \"text\": texts,\n", + " }\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "dataset = load_dataset(\"FreedomIntelligence/medical-o1-reasoning-SFT\",\"en\", trust_remote_code=True)\n", + "split_dataset = dataset[\"train\"].train_test_split(test_size=0.2, seed=200)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "train_dataset = split_dataset['train']\n", + "validation_dataset = split_dataset['test']" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Map: 100%|██████████| 15763/15763 [00:00<00:00, 100186.71 examples/s]\n", + "Creating json from Arrow format: 100%|██████████| 16/16 [00:00<00:00, 93.83ba/s]\n" + ] + }, + { + "data": { + "text/plain": [ + "46545696" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Path(\"./data\").mkdir(exist_ok=True)\n", "train_dataset = train_dataset.map(formatting_prompts_func, batched = True,remove_columns=[\"Question\", \"Complex_CoT\", \"Response\"])\n", - "train_dataset.to_json(\"./data/train.jsonl\")" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Map: 100%|██████████| 3941/3941 [00:00<00:00, 88383.53 examples/s]\n", - "Creating json from Arrow format: 100%|██████████| 4/4 [00:00<00:00, 95.53ba/s]\n" - ] - }, - { - "data": { - "text/plain": [ - "11674511" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "\n", - "validation_dataset = validation_dataset.map(formatting_prompts_func, batched = True,remove_columns=[\"Question\", \"Complex_CoT\", \"Response\"])\n", - "validation_dataset.to_json(\"./data/valid.jsonl\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### **Training**" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Calling `python -m mlx_lm.lora...` directly is deprecated. Use `mlx_lm.lora...` or `python -m mlx_lm lora ...` instead.\n", - "Loading pretrained model\n", - "Loading datasets\n", - "Training\n", - "Trainable parameters: 0.027% (1.049M/3836.022M)\n", - "Starting training..., iters: 100\n", - "Iter 1: Val loss 2.827, Val took 89.684s\n", - "Iter 10: Train loss 2.761, Learning Rate 1.000e-05, It/sec 0.174, Tokens/sec 396.129, Trained Tokens 22765, Peak mem 23.744 GB\n", - "Iter 20: Train loss 2.110, Learning Rate 1.000e-05, It/sec 0.152, Tokens/sec 383.076, Trained Tokens 47982, Peak mem 28.763 GB\n", - "Iter 30: Train loss 1.931, Learning Rate 1.000e-05, It/sec 0.180, Tokens/sec 412.540, Trained Tokens 70944, Peak mem 28.763 GB\n", - "Iter 40: Train loss 1.925, Learning Rate 1.000e-05, It/sec 0.180, Tokens/sec 431.207, Trained Tokens 94878, Peak mem 28.763 GB\n", - "Iter 50: Train loss 1.804, Learning Rate 1.000e-05, It/sec 0.174, Tokens/sec 413.008, Trained Tokens 118679, Peak mem 28.763 GB\n", - "Iter 60: Train loss 1.710, Learning Rate 1.000e-05, It/sec 0.169, Tokens/sec 413.904, Trained Tokens 143103, Peak mem 28.763 GB\n", - "Iter 70: Train loss 1.737, Learning Rate 1.000e-05, It/sec 0.172, Tokens/sec 413.545, Trained Tokens 167120, Peak mem 28.763 GB\n", - "Iter 80: Train loss 1.763, Learning Rate 1.000e-05, It/sec 0.170, Tokens/sec 386.846, Trained Tokens 189896, Peak mem 28.763 GB\n", - "Iter 90: Train loss 1.745, Learning Rate 1.000e-05, It/sec 0.165, Tokens/sec 409.207, Trained Tokens 214650, Peak mem 28.763 GB\n", - "Iter 100: Val loss 1.696, Val took 84.853s\n", - "Iter 100: Train loss 1.702, Learning Rate 1.000e-05, It/sec 0.175, Tokens/sec 418.543, Trained Tokens 238626, Peak mem 28.763 GB\n", - "Iter 100: Saved adapter weights to adapters/adapters.safetensors and adapters/0000100_adapters.safetensors.\n", - "Saved final weights to adapters/adapters.safetensors.\n" - ] - } - ], - "source": [ - "! python -m mlx_lm.lora --model ./phi-4-mini-reasoning --train --data ./data --iters 100" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### **Testing**" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - ":128: RuntimeWarning: 'mlx_lm.generate' found in sys.modules after import of package 'mlx_lm', but prior to execution of 'mlx_lm.generate'; this may result in unpredictable behaviour\n", - "Calling `python -m mlx_lm.generate...` directly is deprecated. Use `mlx_lm.generate...` or `python -m mlx_lm generate ...` instead.\n", - "==========\n", - "Okay, let's see. The patient is a 54-year-old construction worker with a long history of smoking. He has swelling in his upper extremity and face, and dilated veins in this region. The CT scan and venogram of the neck are done. Hmm, what could be causing these symptoms?\n", - "\n", - "First, let's think about the symptoms. Swelling in the upper extremity and face, along with dilated veins. That sounds like edema, which is swelling due to fluid accumulation. The dilated veins might be related to increased pressure or venous insufficiency. \n", - "\n", - "Now, considering the patient's history of smoking, that might be a clue. Smoking can contribute to vascular issues, like atherosclerosis or even something like a varicitis? Wait, no, varicitis is more about varicose veins. But the swelling in the upper extremity and face, along with dilated veins, could be pointing towards something like a venous thrombosis. But wait, venous thrombosis would cause swelling and dilated veins, right? But then, the CT scan and venogram would show a thrombus in the veins. \n", - "\n", - "Alternatively, could it be something like a varicoceles? No, that usually involves varicose veins, which are more about veins in the legs. \n", - "\n", - "Wait, the upper extremity and face swelling. Hmm, maybe it's a case of a venous obstruction. But the venogram would show dilated veins around the area, which is typical of venous obstruction. \n", - "\n", - "Another possibility is a venous thrombosis, like deep vein Thrombosis (DVT) in the upper extremities. But DVT is more common in the legs. However, the upper extremity could be involved, especially if there's some kind of venous obstruction. \n", - "\n", - "Wait, but the CT scan and venogram of the neck. The venogram would show dilated veins in the neck region, which is unusual. So, the venous obstruction in the neck area could be due to a thrombus in the jugular vein. \n", - "\n", - "But why would the jugular vein be dilated? That could be due to a venous obstruction, like a thrombosis. \n", - "\n", - "So, putting it all together, the patient has swelling in the upper extremity and face, which is likely due to increased venous pressure. The dilated veins in the neck region, as seen on the venogram, suggest a venous obstruction. \n", - "\n", - "Given the patient's history of smoking, which is a risk factor for thrombosis, this could be a case of jugular vein thrombosis. But wait, the jugular vein is part of the venous system in the neck, and if there's a thrombus there, it could cause swelling in the upper extremity and face due to increased pressure. \n", - "\n", - "Alternatively, could it be something like a varicoceles? No, that doesn't seem right. \n", - "\n", - "Another possibility is a venous obstruction in the subclavian vein. If there's a thrombus in the subclavian vein, it could lead to swelling in the upper extremity and face, and the venogram would show dilated veins in the neck region. \n", - "\n", - "So, the most likely diagnosis is a venous thrombosis in the subclavian vein. That would explain the swelling and the dilated veins seen on the venogram. \n", - "\n", - "But wait, the CT scan and venogram of the neck. The venogram would show dilated veins around the area, which is typical of venous obstruction. \n", - "\n", - "So, yeah, the diagnosis is a venous thrombosis in the subclavian vein. That makes sense given the patient's symptoms and history. Smoking is a risk factor for thrombosis, and the venous obstruction would cause the swelling and dilated veins. \n", - "\n", - "I think that's the most likely diagnosis.The patient’s symptoms—swelling in the upper extremity and face along with dilated veins in the neck—are consistent with increased venous pressure and venous obstruction. Given his history of smoking, which is a known risk factor for thrombosis, the most likely diagnosis is a venous thrombosis in the subclavian vein. This condition can lead to venous obstruction, resulting in the observed swelling and dilated veins seen on the venogram.\n", - "==========\n", - "Prompt: 76 tokens, 329.797 tokens-per-sec\n", - "Generation: 871 tokens, 29.596 tokens-per-sec\n", - "Peak memory: 7.846 GB\n" - ] - } - ], - "source": [ - "! python -m mlx_lm.generate --model ./phi-4-mini-reasoning --adapter-path ./adapters --max-token 4096 --prompt \"A 54-year-old construction worker with a long history of smoking presents with swelling in his upper extremity and face, along with dilated veins in this region. After conducting a CT scan and venogram of the neck, what is the most likely diagnosis for the cause of these symptoms?\" --extra-eos-token \"<|end|>\"" - ] - } - ], - "metadata": { - "accelerator": "GPU", - "colab": { - "gpuType": "A100", - "machine_shape": "hm", - "provenance": [] - }, - "kernelspec": { - "display_name": "mlxdev", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.8" - }, - "widgets": { - "application/vnd.jupyter.widget-state+json": { - "004dfcdd5fda4829955c973579e593ea": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "FloatProgressModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "FloatProgressModel", - 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"disabled": false, - "indent": true, - "layout": "IPY_MODEL_3b946bf67ad94e53bc10396d8cca8d1d", - "style": "IPY_MODEL_e7ac6efaa5874144baa9b7c712774db5", - "value": true - } - }, - "fb89d6ed877e4469bb528a35eba3867c": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "ButtonModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "ButtonModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "ButtonView", - "button_style": "", - "description": "Login", - "disabled": false, - "icon": "", - "layout": "IPY_MODEL_769f0eff90004eb9b2b8a7f06ef88d0c", - "style": "IPY_MODEL_7f2512bd77ed4d58ba03c9438b59ed33", - "tooltip": "" - } - } - } - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} + "train_dataset.to_json(\"./data/train.jsonl\")" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Map: 100%|██████████| 3941/3941 [00:00<00:00, 88383.53 examples/s]\n", + "Creating json from Arrow format: 100%|██████████| 4/4 [00:00<00:00, 95.53ba/s]\n" + ] + }, + { + "data": { + "text/plain": [ + "11674511" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\n", + "validation_dataset = validation_dataset.map(formatting_prompts_func, batched = True,remove_columns=[\"Question\", \"Complex_CoT\", \"Response\"])\n", + "validation_dataset.to_json(\"./data/valid.jsonl\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### **Training**" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Calling `python -m mlx_lm.lora...` directly is deprecated. Use `mlx_lm.lora...` or `python -m mlx_lm lora ...` instead.\n", + "Loading pretrained model\n", + "Loading datasets\n", + "Training\n", + "Trainable parameters: 0.027% (1.049M/3836.022M)\n", + "Starting training..., iters: 100\n", + "Iter 1: Val loss 2.827, Val took 89.684s\n", + "Iter 10: Train loss 2.761, Learning Rate 1.000e-05, It/sec 0.174, Tokens/sec 396.129, Trained Tokens 22765, Peak mem 23.744 GB\n", + "Iter 20: Train loss 2.110, Learning Rate 1.000e-05, It/sec 0.152, Tokens/sec 383.076, Trained Tokens 47982, Peak mem 28.763 GB\n", + "Iter 30: Train loss 1.931, Learning Rate 1.000e-05, It/sec 0.180, Tokens/sec 412.540, Trained Tokens 70944, Peak mem 28.763 GB\n", + "Iter 40: Train loss 1.925, Learning Rate 1.000e-05, It/sec 0.180, Tokens/sec 431.207, Trained Tokens 94878, Peak mem 28.763 GB\n", + "Iter 50: Train loss 1.804, Learning Rate 1.000e-05, It/sec 0.174, Tokens/sec 413.008, Trained Tokens 118679, Peak mem 28.763 GB\n", + "Iter 60: Train loss 1.710, Learning Rate 1.000e-05, It/sec 0.169, Tokens/sec 413.904, Trained Tokens 143103, Peak mem 28.763 GB\n", + "Iter 70: Train loss 1.737, Learning Rate 1.000e-05, It/sec 0.172, Tokens/sec 413.545, Trained Tokens 167120, Peak mem 28.763 GB\n", + "Iter 80: Train loss 1.763, Learning Rate 1.000e-05, It/sec 0.170, Tokens/sec 386.846, Trained Tokens 189896, Peak mem 28.763 GB\n", + "Iter 90: Train loss 1.745, Learning Rate 1.000e-05, It/sec 0.165, Tokens/sec 409.207, Trained Tokens 214650, Peak mem 28.763 GB\n", + "Iter 100: Val loss 1.696, Val took 84.853s\n", + "Iter 100: Train loss 1.702, Learning Rate 1.000e-05, It/sec 0.175, Tokens/sec 418.543, Trained Tokens 238626, Peak mem 28.763 GB\n", + "Iter 100: Saved adapter weights to adapters/adapters.safetensors and adapters/0000100_adapters.safetensors.\n", + "Saved final weights to adapters/adapters.safetensors.\n" + ] + } + ], + "source": [ + "! python -m mlx_lm.lora --model ./phi-4-mini-reasoning --train --data ./data --iters 100" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### **Testing**" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + ":128: RuntimeWarning: 'mlx_lm.generate' found in sys.modules after import of package 'mlx_lm', but prior to execution of 'mlx_lm.generate'; this may result in unpredictable behaviour\n", + "Calling `python -m mlx_lm.generate...` directly is deprecated. Use `mlx_lm.generate...` or `python -m mlx_lm generate ...` instead.\n", + "==========\n", + "Okay, let's see. The patient is a 54-year-old construction worker with a long history of smoking. He has swelling in his upper extremity and face, and dilated veins in this region. The CT scan and venogram of the neck are done. Hmm, what could be causing these symptoms?\n", + "\n", + "First, let's think about the symptoms. Swelling in the upper extremity and face, along with dilated veins. That sounds like edema, which is swelling due to fluid accumulation. The dilated veins might be related to increased pressure or venous insufficiency. \n", + "\n", + "Now, considering the patient's history of smoking, that might be a clue. Smoking can contribute to vascular issues, like atherosclerosis or even something like a varicitis? Wait, no, varicitis is more about varicose veins. But the swelling in the upper extremity and face, along with dilated veins, could be pointing towards something like a venous thrombosis. But wait, venous thrombosis would cause swelling and dilated veins, right? But then, the CT scan and venogram would show a thrombus in the veins. \n", + "\n", + "Alternatively, could it be something like a varicoceles? No, that usually involves varicose veins, which are more about veins in the legs. \n", + "\n", + "Wait, the upper extremity and face swelling. Hmm, maybe it's a case of a venous obstruction. But the venogram would show dilated veins around the area, which is typical of venous obstruction. \n", + "\n", + "Another possibility is a venous thrombosis, like deep vein Thrombosis (DVT) in the upper extremities. But DVT is more common in the legs. However, the upper extremity could be involved, especially if there's some kind of venous obstruction. \n", + "\n", + "Wait, but the CT scan and venogram of the neck. The venogram would show dilated veins in the neck region, which is unusual. So, the venous obstruction in the neck area could be due to a thrombus in the jugular vein. \n", + "\n", + "But why would the jugular vein be dilated? That could be due to a venous obstruction, like a thrombosis. \n", + "\n", + "So, putting it all together, the patient has swelling in the upper extremity and face, which is likely due to increased venous pressure. The dilated veins in the neck region, as seen on the venogram, suggest a venous obstruction. \n", + "\n", + "Given the patient's history of smoking, which is a risk factor for thrombosis, this could be a case of jugular vein thrombosis. But wait, the jugular vein is part of the venous system in the neck, and if there's a thrombus there, it could cause swelling in the upper extremity and face due to increased pressure. \n", + "\n", + "Alternatively, could it be something like a varicoceles? No, that doesn't seem right. \n", + "\n", + "Another possibility is a venous obstruction in the subclavian vein. If there's a thrombus in the subclavian vein, it could lead to swelling in the upper extremity and face, and the venogram would show dilated veins in the neck region. \n", + "\n", + "So, the most likely diagnosis is a venous thrombosis in the subclavian vein. That would explain the swelling and the dilated veins seen on the venogram. \n", + "\n", + "But wait, the CT scan and venogram of the neck. The venogram would show dilated veins around the area, which is typical of venous obstruction. \n", + "\n", + "So, yeah, the diagnosis is a venous thrombosis in the subclavian vein. That makes sense given the patient's symptoms and history. Smoking is a risk factor for thrombosis, and the venous obstruction would cause the swelling and dilated veins. \n", + "\n", + "I think that's the most likely diagnosis.The patient’s symptoms—swelling in the upper extremity and face along with dilated veins in the neck—are consistent with increased venous pressure and venous obstruction. Given his history of smoking, which is a known risk factor for thrombosis, the most likely diagnosis is a venous thrombosis in the subclavian vein. This condition can lead to venous obstruction, resulting in the observed swelling and dilated veins seen on the venogram.\n", + "==========\n", + "Prompt: 76 tokens, 329.797 tokens-per-sec\n", + "Generation: 871 tokens, 29.596 tokens-per-sec\n", + "Peak memory: 7.846 GB\n" + ] + } + ], + "source": [ + "! python -m mlx_lm.generate --model ./phi-4-mini-reasoning --adapter-path ./adapters --max-token 4096 --prompt \"A 54-year-old construction worker with a long history of smoking presents with swelling in his upper extremity and face, along with dilated veins in this region. 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