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This behaviour is the source of the following dependency conflicts.\n", "hf-gradio 0.4.1 requires gradio-client<3.0,>=2.0, but you have gradio-client 1.14.0 which is incompatible.\n", "google-genai 2.12.1 requires pydantic<3.0.0,>=2.12.5, but you have pydantic 2.12.3 which is incompatible.\n", "gcsfs 2025.12.0 requires fsspec==2025.12.0, but you have fsspec 2025.3.0 which is incompatible.\n", "google-adk 2.7.1 requires starlette<2,>=1.3.1, but you have starlette 0.52.1 which is incompatible.\n", "python-fasthtml 0.14.12 requires starlette>=1.0.1, but you have starlette 0.52.1 which is incompatible.\u001b[0m\u001b[31m\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m43.1/43.1 MB\u001b[0m \u001b[31m16.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[?25h" ] } ], "source": [ "!git clone --depth 1 https://github.com/hiyouga/LLaMA-Factory.git\n", "%cd LLaMA-Factory\n", "\n", "!pip install -e .[torch,metrics] -q\n", "\n", "!pip install bitsandbytes peft accelerate datasets huggingface_hub trl -q" ] }, { "cell_type": "code", "source": [ "from huggingface_hub import login\n", "from google.colab import userdata\n", "\n", "hf_token = userdata.get('HF_TOKEN')\n", "login(token=hf_token)" ], "metadata": { "id": "7qCiy1wKTdjN" }, "execution_count": 2, "outputs": [] }, { "cell_type": "code", "source": [ "import json\n", "\n", "dataset_info_path = \"data/dataset_info.json\"\n", "\n", "with open(dataset_info_path, \"r\") as f:\n", " dataset_info = json.load(f)" ], "metadata": { "id": "jXHwm26oTpVc" }, "execution_count": 3, "outputs": [] }, { "cell_type": "code", "source": [ "# 1. Financial QA Dataset (from Hugging Face: llamafactory/fiqa)\n", "dataset_info[\"financial_qa\"] = {\n", " \"hf_hub_url\": \"llamafactory/fiqa\",\n", " \"columns\": {\n", " \"prompt\": \"instruction\",\n", " \"query\": \"input\",\n", " \"response\": \"output\"\n", " }\n", "}" ], "metadata": { "id": "uG3kvW0PT0Iw" }, "execution_count": 4, "outputs": [] }, { "cell_type": "code", "source": [ "# 2. Python Code Execution Dataset (from Hugging Face: flytech/python-codes-25k)\n", "dataset_info[\"python_code\"] = {\n", " \"hf_hub_url\": \"flytech/python-codes-25k\",\n", " \"columns\": {\n", " \"prompt\": \"instruction\",\n", " \"query\": \"input\",\n", " \"response\": \"output\"\n", " }\n", "}" ], "metadata": { "id": "9jPf3hxuT6it" }, "execution_count": 5, "outputs": [] }, { "cell_type": "code", "source": [ "# 3. Preference Dataset for DPO/RLHF (from Hugging Face: argilla/ultrafeedback-binarized-preferences-cleaned)\n", "dataset_info[\"ultrafeedback_preference\"] = {\n", " \"hf_hub_url\": \"argilla/ultrafeedback-binarized-preferences-cleaned\",\n", " \"ranking\": True,\n", " \"columns\": {\n", " \"prompt\": \"instruction\",\n", " \"chosen\": \"chosen\",\n", " \"rejected\": \"rejected\"\n", " }\n", "}" ], "metadata": { "id": "1tkDhEZYT_zt" }, "execution_count": 6, "outputs": [] }, { "cell_type": "code", "source": [ "with open(dataset_info_path, \"w\") as f:\n", " json.dump(dataset_info, f, indent=2)\n", "\n", "print(\"Hugging Face datasets successfully registered in dataset_info.json\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "TCQr5LmAUHtG", "outputId": "4af2669c-664f-4acf-d635-698c19681a55" }, "execution_count": 7, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Hugging Face datasets successfully registered in dataset_info.json\n" ] } ] }, { "cell_type": "markdown", "source": [ "# Phase 1 — Domain-Specific Supervised Fine-Tuning (SFT)\n", "4-bit QLoRA training on financial_qa" ], "metadata": { "id": "497_hINHURGG" } }, { "cell_type": "code", "source": [ "import yaml\n", "\n", "sft_config = {\n", " \"stage\": \"sft\",\n", " \"do_train\": True,\n", " \"model_name_or_path\": \"Qwen/Qwen2.5-1.5B-Instruct\",\n", " \"dataset\": \"financial_qa,python_code\",\n", " \"max_samples\": 1000,\n", " \"template\": \"qwen\",\n", " \"finetuning_type\": \"lora\",\n", " \"lora_target\": \"all\",\n", " \"output_dir\": \"saves/Qwen2.5-1.5B/lora/sft\",\n", " \"overwrite_output_dir\": True,\n", " \"per_device_train_batch_size\": 2,\n", " \"gradient_accumulation_steps\": 4,\n", " \"lr_scheduler_type\": \"cosine\",\n", " \"logging_steps\": 10,\n", " \"save_steps\": 100,\n", " \"max_steps\": 120,\n", " \"learning_rate\": 2e-4,\n", " \"quantization_bit\": 4,\n", " \"fp16\": True,\n", "}\n", "\n", "with open(\"sft_config.yaml\", \"w\") as f:\n", " yaml.dump(sft_config, f)\n", "\n", "!llamafactory-cli train sft_config.yaml" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "3YLvPC5fUZFG", "outputId": "22227d52-2e81-478d-fdf5-6861340d2d71" }, "execution_count": 9, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "/usr/local/lib/python3.13/dist-packages/jieba/_compat.py:18: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.\n", " import pkg_resources\n", "[WARNING|2026-09-27 05:30:39] llamafactory.hparams.parser:149 >> We recommend enable `upcast_layernorm` in quantized training.\n", "[INFO|2026-09-27 05:30:39] llamafactory.hparams.parser:651 >> Process rank: 0, world size: 1, device: cuda:0, distributed training: False, compute dtype: torch.float16\n", "[INFO|configuration_utils.py:780] 2026-09-27 05:30:39,445 >> loading configuration file config.json from cache at /root/.cache/huggingface/hub/models--Qwen--Qwen2.5-1.5B-Instruct/snapshots/989aa7980e4cf806f80c7fef2b1adb7bc71aa306/config.json\n", "[INFO|configuration_utils.py:856] 2026-09-27 05:30:39,452 >> Model config Qwen2Config {\n", " \"architectures\": [\n", " \"Qwen2ForCausalLM\"\n", " ],\n", " \"attention_dropout\": 0.0,\n", " \"bos_token_id\": 151643,\n", " \"dtype\": \"bfloat16\",\n", " \"eos_token_id\": 151645,\n", " \"hidden_act\": \"silu\",\n", " \"hidden_size\": 1536,\n", " \"initializer_range\": 0.02,\n", " \"intermediate_size\": 8960,\n", " \"layer_types\": [\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\"\n", " ],\n", " \"max_position_embeddings\": 32768,\n", " \"max_window_layers\": 21,\n", " \"model_type\": \"qwen2\",\n", " \"num_attention_heads\": 12,\n", " \"num_hidden_layers\": 28,\n", " \"num_key_value_heads\": 2,\n", " \"pad_token_id\": null,\n", " \"rms_norm_eps\": 1e-06,\n", " \"rope_parameters\": {\n", " \"rope_theta\": 1000000.0,\n", " \"rope_type\": \"default\"\n", " },\n", " \"sliding_window\": null,\n", " \"tie_word_embeddings\": true,\n", " \"transformers_version\": \"5.8.0\",\n", " \"use_cache\": true,\n", " \"use_sliding_window\": false,\n", " \"vocab_size\": 151936\n", "}\n", "\n", "[INFO|configuration_utils.py:780] 2026-09-27 05:30:40,800 >> loading configuration file config.json from cache at /root/.cache/huggingface/hub/models--Qwen--Qwen2.5-1.5B-Instruct/snapshots/989aa7980e4cf806f80c7fef2b1adb7bc71aa306/config.json\n", "[INFO|configuration_utils.py:856] 2026-09-27 05:30:40,802 >> Model config Qwen2Config {\n", " \"architectures\": [\n", " \"Qwen2ForCausalLM\"\n", " ],\n", " \"attention_dropout\": 0.0,\n", " \"bos_token_id\": 151643,\n", " \"dtype\": \"bfloat16\",\n", " \"eos_token_id\": 151645,\n", " \"hidden_act\": \"silu\",\n", " \"hidden_size\": 1536,\n", " \"initializer_range\": 0.02,\n", " \"intermediate_size\": 8960,\n", " \"layer_types\": [\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\"\n", " ],\n", " \"max_position_embeddings\": 32768,\n", " \"max_window_layers\": 21,\n", " \"model_type\": \"qwen2\",\n", " \"num_attention_heads\": 12,\n", " \"num_hidden_layers\": 28,\n", " \"num_key_value_heads\": 2,\n", " \"pad_token_id\": null,\n", " \"rms_norm_eps\": 1e-06,\n", " \"rope_parameters\": {\n", " \"rope_theta\": 1000000.0,\n", " \"rope_type\": \"default\"\n", " },\n", " \"sliding_window\": null,\n", " \"tie_word_embeddings\": true,\n", " \"transformers_version\": \"5.8.0\",\n", " \"use_cache\": true,\n", " \"use_sliding_window\": false,\n", " \"vocab_size\": 151936\n", "}\n", "\n", "[INFO|configuration_utils.py:780] 2026-09-27 05:30:40,904 >> loading configuration file config.json from cache at /root/.cache/huggingface/hub/models--Qwen--Qwen2.5-1.5B-Instruct/snapshots/989aa7980e4cf806f80c7fef2b1adb7bc71aa306/config.json\n", "[INFO|configuration_utils.py:856] 2026-09-27 05:30:40,905 >> Model config Qwen2Config {\n", " \"architectures\": [\n", " \"Qwen2ForCausalLM\"\n", " ],\n", " \"attention_dropout\": 0.0,\n", " \"bos_token_id\": 151643,\n", " \"dtype\": \"bfloat16\",\n", " \"eos_token_id\": 151645,\n", " \"hidden_act\": \"silu\",\n", " \"hidden_size\": 1536,\n", " \"initializer_range\": 0.02,\n", " \"intermediate_size\": 8960,\n", " \"layer_types\": [\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\"\n", " ],\n", " \"max_position_embeddings\": 32768,\n", " \"max_window_layers\": 21,\n", " \"model_type\": \"qwen2\",\n", " \"num_attention_heads\": 12,\n", " \"num_hidden_layers\": 28,\n", " \"num_key_value_heads\": 2,\n", " \"pad_token_id\": null,\n", " \"rms_norm_eps\": 1e-06,\n", " \"rope_parameters\": {\n", " \"rope_theta\": 1000000.0,\n", " \"rope_type\": \"default\"\n", " },\n", " \"sliding_window\": null,\n", " \"tie_word_embeddings\": true,\n", " \"transformers_version\": \"5.8.0\",\n", " \"use_cache\": true,\n", " \"use_sliding_window\": false,\n", " \"vocab_size\": 151936\n", "}\n", "\n", "[INFO|2026-09-27 05:30:41] llamafactory.data.loader:144 >> Loading dataset llamafactory/fiqa...\n", "Converting format of dataset: 100% 1000/1000 [00:00<00:00, 17508.00 examples/s]\n", "[INFO|2026-09-27 05:30:43] llamafactory.data.loader:144 >> Loading dataset flytech/python-codes-25k...\n", "Converting format of dataset: 100% 1000/1000 [00:00<00:00, 17525.78 examples/s]\n", "Running tokenizer on dataset: 100% 2000/2000 [00:02<00:00, 849.80 examples/s]\n", "training example:\n", "input_ids:\n", "[151644, 8948, 198, 2610, 525, 1207, 16948, 11, 3465, 553, 54364, 14817, 13, 1446, 525, 264, 10950, 17847, 13, 151645, 198, 151644, 872, 198, 2742, 551, 697, 5896, 6540, 11, 2968, 697, 4226, 476, 9459, 311, 279, 1946, 3405, 476, 3832, 624, 3838, 374, 6509, 264, 2562, 20284, 389, 264, 2562, 8411, 30, 151645, 198, 151644, 77091, 198, 785, 33383, 81561, 45931, 311, 279, 3832, 13, 220, 758, 4586, 279, 1850, 358, 646, 1977, 374, 697, 2562, 20284, 1231, 387, 77812, 13, 220, 1988, 432, 13798, 389, 279, 13161, 323, 1128, 432, 374, 498, 1366, 311, 36810, 13, 18097, 15190, 79, 5074, 879, 5821, 3123, 504, 2114, 389, 2562, 1231, 36810, 5435, 256, 18024, 11, 2670, 279, 2783, 315, 18739, 862, 9106, 11, 279, 2783, 256, 315, 76647, 323, 20969, 323, 1008, 19119, 323, 5871, 18024, 13, 256, 15190, 79, 5074, 525, 6509, 1036, 48510, 287, 3123, 504, 2114, 854, 421, 862, 21300, 256, 1373, 1105, 311, 387, 3123, 504, 2114, 31202, 5021, 1091, 458, 256, 19119, 1899, 748, 975, 323, 807, 1184, 311, 6084, 476, 2732, 311, 3367, 279, 18154, 256, 315, 862, 975, 13, 576, 5042, 2783, 315, 20969, 323, 83216, 18024, 1231, 387, 256, 95504, 476, 279, 38173, 1231, 990, 264, 5297, 15145, 40358, 323, 11046, 256, 3255, 10282, 8502, 13, 43740, 315, 279, 1714, 1483, 11, 15145, 256, 70637, 525, 8789, 7199, 311, 220, 20, 15, 3266, 438, 10982, 6788, 13, 262, 8278, 5042, 7049, 369, 76647, 1231, 387, 11660, 438, 458, 20284, 323, 256, 56480, 1969, 387, 8604, 369, 9705, 13, 91423, 1969, 387, 13276, 256, 323, 8311, 26, 70637, 369, 94850, 18024, 525, 537, 256, 84752, 13, 4398, 1995, 374, 2500, 304, 56894, 220, 19, 21, 18, 11, 18097, 11, 256, 22518, 11, 22062, 11, 323, 3261, 91423, 13, 22518, 91423, 369, 29211, 8239, 11, 6310, 476, 8256, 1231, 387, 256, 95504, 421, 807, 525, 2176, 19119, 323, 5871, 323, 3367, 825, 315, 279, 256, 2701, 7032, 25, 7139, 398, 13904, 1273, 25, 576, 1887, 7428, 315, 279, 16517, 5702, 374, 279, 6786, 315, 2562, 11, 2562, 572, 3520, 13075, 256, 2337, 279, 5702, 323, 279, 38173, 1030, 803, 1091, 264, 4586, 256, 30193, 315, 3709, 7911, 476, 1045, 1008, 3151, 2562, 8760, 256, 518, 1045, 3853, 882, 13, 256, 25403, 1273, 25, 576, 16517, 572, 5815, 448, 279, 4541, 6786, 315, 279, 38173, 748, 6559, 476, 2562, 323, 10017, 5961, 256, 1573, 476, 1283, 264, 11924, 2562, 10219, 13, 56894, 220, 19, 21, 18, 5707, 803, 16376, 16148, 315, 1493, 7032, 438, 256, 1632, 438, 1008, 9481, 323, 8502, 369, 36810, 287, 16517, 256, 18024, 13, 55960, 15190, 79, 5074, 1231, 36810, 1045, 476, 678, 315, 279, 2783, 315, 20609, 2661, 304, 279, 256, 3308, 315, 862, 6559, 476, 2562, 13, 758, 4586, 11, 279, 38843, 374, 256, 7199, 311, 400, 17, 20, 369, 20609, 2661, 5961, 476, 45245, 311, 894, 825, 256, 1697, 2337, 279, 3742, 1042, 13, 4398, 10219, 315, 279, 5601, 323, 256, 9481, 646, 387, 1730, 304, 56894, 220, 19, 21, 18, 13, 1416, 697, 15262, 97578, 56857, 498, 369, 18024, 4889, 315, 419, 18821, 432, 1265, 387, 11758, 438, 32520, 369, 3742, 9895, 13, 8340, 369, 56388, 91423, 25, 25783, 315, 5297, 15145, 40358, 13, 256, 576, 5297, 15145, 40358, 374, 256, 279, 6775, 386, 5, 5371, 4379, 13, 1752, 5821, 304, 220, 17, 15, 16, 15, 11, 279, 4379, 369, 1429, 2613, 256, 2205, 1361, 304, 279, 3639, 4180, 374, 400, 19, 21, 264, 1899, 13, 8748, 33383, 393, 19, 21, 18, 19489, 2652, 498, 1410, 95160, 518, 264, 817, 1853, 336, 4379, 151645, 198]\n", "inputs:\n", "<|im_start|>system\n", "You are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\n", "<|im_start|>user\n", "Utilize your financial knowledge, give your answer or opinion to the input question or subject.\n", "What is considered a business expense on a business trip?<|im_end|>\n", "<|im_start|>assistant\n", "The IRS Guidance pertaining to the subject. In general the best I can say is your business expense may be deductible. But it depends on the circumstances and what it is you want to deduct. Travel Taxpayers who travel away from home on business may deduct related expenses, including the cost of reaching their destination, the cost of lodging and meals and other ordinary and necessary expenses. Taxpayers are considered “traveling away from home” if their duties require them to be away from home substantially longer than an ordinary day’s work and they need to sleep or rest to meet the demands of their work. The actual cost of meals and incidental expenses may be deducted or the taxpayer may use a standard meal allowance and reduced record keeping requirements. Regardless of the method used, meal deductions are generally limited to 50 percent as stated earlier. Only actual costs for lodging may be claimed as an expense and receipts must be kept for documentation. Expenses must be reasonable and appropriate; deductions for extravagant expenses are not allowable. More information is available in Publication 463, Travel, Entertainment, Gift, and Car Expenses. Entertainment Expenses for entertaining clients, customers or employees may be deducted if they are both ordinary and necessary and meet one of the following tests: Directly-related test: The main purpose of the entertainment activity is the conduct of business, business was actually conducted during the activity and the taxpayer had more than a general expectation of getting income or some other specific business benefit at some future time. Associated test: The entertainment was associated with the active conduct of the taxpayer’s trade or business and occurred directly before or after a substantial business discussion. Publication 463 provides more extensive explanation of these tests as well as other limitations and requirements for deducting entertainment expenses. Gifts Taxpayers may deduct some or all of the cost of gifts given in the course of their trade or business. In general, the deduction is limited to $25 for gifts given directly or indirectly to any one person during the tax year. More discussion of the rules and limitations can be found in Publication 463. If your LLC reimburses you for expenses outside of this guidance it should be treated as Income for tax purposes. Edit for Meal Expenses: Amount of standard meal allowance. The standard meal allowance is the federal M&IE rate. For travel in 2010, the rate for most small localities in the United States is $46 a day. Source IRS P463 Alternately you could reimburse at a per diem rate<|im_end|>\n", "\n", "label_ids:\n", "[-100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, 785, 33383, 81561, 45931, 311, 279, 3832, 13, 220, 758, 4586, 279, 1850, 358, 646, 1977, 374, 697, 2562, 20284, 1231, 387, 77812, 13, 220, 1988, 432, 13798, 389, 279, 13161, 323, 1128, 432, 374, 498, 1366, 311, 36810, 13, 18097, 15190, 79, 5074, 879, 5821, 3123, 504, 2114, 389, 2562, 1231, 36810, 5435, 256, 18024, 11, 2670, 279, 2783, 315, 18739, 862, 9106, 11, 279, 2783, 256, 315, 76647, 323, 20969, 323, 1008, 19119, 323, 5871, 18024, 13, 256, 15190, 79, 5074, 525, 6509, 1036, 48510, 287, 3123, 504, 2114, 854, 421, 862, 21300, 256, 1373, 1105, 311, 387, 3123, 504, 2114, 31202, 5021, 1091, 458, 256, 19119, 1899, 748, 975, 323, 807, 1184, 311, 6084, 476, 2732, 311, 3367, 279, 18154, 256, 315, 862, 975, 13, 576, 5042, 2783, 315, 20969, 323, 83216, 18024, 1231, 387, 256, 95504, 476, 279, 38173, 1231, 990, 264, 5297, 15145, 40358, 323, 11046, 256, 3255, 10282, 8502, 13, 43740, 315, 279, 1714, 1483, 11, 15145, 256, 70637, 525, 8789, 7199, 311, 220, 20, 15, 3266, 438, 10982, 6788, 13, 262, 8278, 5042, 7049, 369, 76647, 1231, 387, 11660, 438, 458, 20284, 323, 256, 56480, 1969, 387, 8604, 369, 9705, 13, 91423, 1969, 387, 13276, 256, 323, 8311, 26, 70637, 369, 94850, 18024, 525, 537, 256, 84752, 13, 4398, 1995, 374, 2500, 304, 56894, 220, 19, 21, 18, 11, 18097, 11, 256, 22518, 11, 22062, 11, 323, 3261, 91423, 13, 22518, 91423, 369, 29211, 8239, 11, 6310, 476, 8256, 1231, 387, 256, 95504, 421, 807, 525, 2176, 19119, 323, 5871, 323, 3367, 825, 315, 279, 256, 2701, 7032, 25, 7139, 398, 13904, 1273, 25, 576, 1887, 7428, 315, 279, 16517, 5702, 374, 279, 6786, 315, 2562, 11, 2562, 572, 3520, 13075, 256, 2337, 279, 5702, 323, 279, 38173, 1030, 803, 1091, 264, 4586, 256, 30193, 315, 3709, 7911, 476, 1045, 1008, 3151, 2562, 8760, 256, 518, 1045, 3853, 882, 13, 256, 25403, 1273, 25, 576, 16517, 572, 5815, 448, 279, 4541, 6786, 315, 279, 38173, 748, 6559, 476, 2562, 323, 10017, 5961, 256, 1573, 476, 1283, 264, 11924, 2562, 10219, 13, 56894, 220, 19, 21, 18, 5707, 803, 16376, 16148, 315, 1493, 7032, 438, 256, 1632, 438, 1008, 9481, 323, 8502, 369, 36810, 287, 16517, 256, 18024, 13, 55960, 15190, 79, 5074, 1231, 36810, 1045, 476, 678, 315, 279, 2783, 315, 20609, 2661, 304, 279, 256, 3308, 315, 862, 6559, 476, 2562, 13, 758, 4586, 11, 279, 38843, 374, 256, 7199, 311, 400, 17, 20, 369, 20609, 2661, 5961, 476, 45245, 311, 894, 825, 256, 1697, 2337, 279, 3742, 1042, 13, 4398, 10219, 315, 279, 5601, 323, 256, 9481, 646, 387, 1730, 304, 56894, 220, 19, 21, 18, 13, 1416, 697, 15262, 97578, 56857, 498, 369, 18024, 4889, 315, 419, 18821, 432, 1265, 387, 11758, 438, 32520, 369, 3742, 9895, 13, 8340, 369, 56388, 91423, 25, 25783, 315, 5297, 15145, 40358, 13, 256, 576, 5297, 15145, 40358, 374, 256, 279, 6775, 386, 5, 5371, 4379, 13, 1752, 5821, 304, 220, 17, 15, 16, 15, 11, 279, 4379, 369, 1429, 2613, 256, 2205, 1361, 304, 279, 3639, 4180, 374, 400, 19, 21, 264, 1899, 13, 8748, 33383, 393, 19, 21, 18, 19489, 2652, 498, 1410, 95160, 518, 264, 817, 1853, 336, 4379, 151645, 198]\n", "labels:\n", "The IRS Guidance pertaining to the subject. In general the best I can say is your business expense may be deductible. But it depends on the circumstances and what it is you want to deduct. Travel Taxpayers who travel away from home on business may deduct related expenses, including the cost of reaching their destination, the cost of lodging and meals and other ordinary and necessary expenses. Taxpayers are considered “traveling away from home” if their duties require them to be away from home substantially longer than an ordinary day’s work and they need to sleep or rest to meet the demands of their work. The actual cost of meals and incidental expenses may be deducted or the taxpayer may use a standard meal allowance and reduced record keeping requirements. Regardless of the method used, meal deductions are generally limited to 50 percent as stated earlier. Only actual costs for lodging may be claimed as an expense and receipts must be kept for documentation. Expenses must be reasonable and appropriate; deductions for extravagant expenses are not allowable. More information is available in Publication 463, Travel, Entertainment, Gift, and Car Expenses. Entertainment Expenses for entertaining clients, customers or employees may be deducted if they are both ordinary and necessary and meet one of the following tests: Directly-related test: The main purpose of the entertainment activity is the conduct of business, business was actually conducted during the activity and the taxpayer had more than a general expectation of getting income or some other specific business benefit at some future time. Associated test: The entertainment was associated with the active conduct of the taxpayer’s trade or business and occurred directly before or after a substantial business discussion. Publication 463 provides more extensive explanation of these tests as well as other limitations and requirements for deducting entertainment expenses. Gifts Taxpayers may deduct some or all of the cost of gifts given in the course of their trade or business. In general, the deduction is limited to $25 for gifts given directly or indirectly to any one person during the tax year. More discussion of the rules and limitations can be found in Publication 463. If your LLC reimburses you for expenses outside of this guidance it should be treated as Income for tax purposes. Edit for Meal Expenses: Amount of standard meal allowance. The standard meal allowance is the federal M&IE rate. For travel in 2010, the rate for most small localities in the United States is $46 a day. Source IRS P463 Alternately you could reimburse at a per diem rate<|im_end|>\n", "\n", "[INFO|configuration_utils.py:780] 2026-09-27 05:30:48,699 >> loading configuration file config.json from cache at /root/.cache/huggingface/hub/models--Qwen--Qwen2.5-1.5B-Instruct/snapshots/989aa7980e4cf806f80c7fef2b1adb7bc71aa306/config.json\n", "[INFO|configuration_utils.py:856] 2026-09-27 05:30:48,701 >> Model config Qwen2Config {\n", " \"architectures\": [\n", " \"Qwen2ForCausalLM\"\n", " ],\n", " \"attention_dropout\": 0.0,\n", " \"bos_token_id\": 151643,\n", " \"dtype\": \"bfloat16\",\n", " \"eos_token_id\": 151645,\n", " \"hidden_act\": \"silu\",\n", " \"hidden_size\": 1536,\n", " \"initializer_range\": 0.02,\n", " \"intermediate_size\": 8960,\n", " \"layer_types\": [\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\"\n", " ],\n", " \"max_position_embeddings\": 32768,\n", " \"max_window_layers\": 21,\n", " \"model_type\": \"qwen2\",\n", " \"num_attention_heads\": 12,\n", " \"num_hidden_layers\": 28,\n", " \"num_key_value_heads\": 2,\n", " \"pad_token_id\": null,\n", " \"rms_norm_eps\": 1e-06,\n", " \"rope_parameters\": {\n", " \"rope_theta\": 1000000.0,\n", " \"rope_type\": \"default\"\n", " },\n", " \"sliding_window\": null,\n", " \"tie_word_embeddings\": true,\n", " \"transformers_version\": \"5.8.0\",\n", " \"use_cache\": true,\n", " \"use_sliding_window\": false,\n", " \"vocab_size\": 151936\n", "}\n", "\n", "[INFO|2026-09-27 05:30:48] llamafactory.model.model_utils.quantization:144 >> Quantizing model to 4 bit with bitsandbytes.\n", "[INFO|2026-09-27 05:30:48] llamafactory.model.model_utils.kv_cache:144 >> KV cache is disabled during training.\n", "[INFO|modeling_utils.py:769] 2026-09-27 05:30:51,079 >> loading weights file model.safetensors from cache at /root/.cache/huggingface/hub/models--Qwen--Qwen2.5-1.5B-Instruct/snapshots/989aa7980e4cf806f80c7fef2b1adb7bc71aa306/model.safetensors\n", "[INFO|modeling_utils.py:839] 2026-09-27 05:30:51,079 >> Will use dtype=torch.bfloat16 as defined in model's config object\n", "[INFO|configuration_utils.py:1086] 2026-09-27 05:30:51,080 >> Generate config GenerationConfig {\n", " \"bos_token_id\": 151643,\n", " \"eos_token_id\": 151645,\n", " \"output_attentions\": false,\n", " \"output_hidden_states\": false,\n", " \"use_cache\": false\n", "}\n", "\n", "Loading weights: 100% 338/338 [00:01<00:00, 286.26it/s]\n", "[INFO|configuration_utils.py:1039] 2026-09-27 05:30:52,738 >> loading configuration file generation_config.json from cache at /root/.cache/huggingface/hub/models--Qwen--Qwen2.5-1.5B-Instruct/snapshots/989aa7980e4cf806f80c7fef2b1adb7bc71aa306/generation_config.json\n", "[INFO|configuration_utils.py:1086] 2026-09-27 05:30:52,739 >> Generate config GenerationConfig {\n", " \"bos_token_id\": 151643,\n", " \"do_sample\": true,\n", " \"eos_token_id\": [\n", " 151645,\n", " 151643\n", " ],\n", " \"pad_token_id\": 151643,\n", " \"repetition_penalty\": 1.1,\n", " \"temperature\": 0.7,\n", " \"top_k\": 20,\n", " \"top_p\": 0.8\n", "}\n", "\n", "[INFO|2026-09-27 05:30:52] llamafactory.model.model_utils.checkpointing:144 >> Gradient checkpointing enabled.\n", "[INFO|2026-09-27 05:30:52] llamafactory.model.model_utils.attention:144 >> Using torch SDPA for faster training and inference.\n", "[INFO|2026-09-27 05:30:52] llamafactory.model.adapter:144 >> Upcasting trainable params to float32.\n", "[INFO|2026-09-27 05:30:52] llamafactory.model.adapter:144 >> Fine-tuning method: LoRA\n", "[INFO|2026-09-27 05:30:52] llamafactory.model.model_utils.misc:144 >> Found linear modules: up_proj,k_proj,gate_proj,o_proj,q_proj,down_proj,v_proj\n", "[INFO|2026-09-27 05:30:53] llamafactory.model.loader:144 >> trainable params: 9,232,384 || all params: 1,552,946,688 || trainable%: 0.5945\n", "[INFO|trainer.py:680] 2026-09-27 05:30:53,104 >> max_steps is given, it will override any value given in num_train_epochs\n", "[WARNING|2026-09-27 05:30:53] llamafactory.train.callbacks:155 >> Previous trainer log in this folder will be deleted.\n", "[WARNING|trainer_utils.py:1238] 2026-09-27 05:30:53,105 >> The tokenizer has new PAD/BOS/EOS tokens that differ from the model config and generation config. The model config and generation config were aligned accordingly, being updated with the tokenizer's values. Updated tokens: {'bos_token_id': None, 'pad_token_id': 151643}.\n", "[INFO|trainer.py:1469] 2026-09-27 05:30:53,520 >> ***** Running training *****\n", "[INFO|trainer.py:1470] 2026-09-27 05:30:53,520 >> Num examples = 2,000\n", "[INFO|trainer.py:1471] 2026-09-27 05:30:53,520 >> Num Epochs = 1\n", "[INFO|trainer.py:1472] 2026-09-27 05:30:53,520 >> Num update steps per epoch = 250\n", "[INFO|trainer.py:1473] 2026-09-27 05:30:53,520 >> Instantaneous batch size per device = 2\n", "[INFO|trainer.py:1476] 2026-09-27 05:30:53,521 >> Total train batch size (w. parallel, distributed & accumulation) = 8\n", "[INFO|trainer.py:1477] 2026-09-27 05:30:53,521 >> Gradient Accumulation steps = 4\n", "[INFO|trainer.py:1478] 2026-09-27 05:30:53,521 >> Total optimization steps = 120\n", "[INFO|trainer.py:1479] 2026-09-27 05:30:53,525 >> Number of trainable parameters = 9,232,384\n", "{'loss': '2.229', 'grad_norm': '0.5264', 'learning_rate': '0.0001972', 'epoch': '0.04'}\n", "{'loss': '2.25', 'grad_norm': '0.3491', 'learning_rate': '0.0001879', 'epoch': '0.08'}\n", "{'loss': '2.213', 'grad_norm': '0.5226', 'learning_rate': '0.0001725', 'epoch': '0.12'}\n", "{'loss': '2.267', 'grad_norm': '0.5161', 'learning_rate': '0.0001522', 'epoch': '0.16'}\n", "{'loss': '2.019', 'grad_norm': '0.4895', 'learning_rate': '0.0001284', 'epoch': '0.2'}\n", "{'loss': '1.913', 'grad_norm': '0.7686', 'learning_rate': '0.0001026', 'epoch': '0.24'}\n", "{'loss': '2.042', 'grad_norm': '0.558', 'learning_rate': '7.666e-05', 'epoch': '0.28'}\n", "{'loss': '1.876', 'grad_norm': '0.6027', 'learning_rate': '5.228e-05', 'epoch': '0.32'}\n", "{'loss': '2.189', 'grad_norm': '0.5231', 'learning_rate': '3.116e-05', 'epoch': '0.36'}\n", "{'loss': '1.954', 'grad_norm': '0.6688', 'learning_rate': '1.474e-05', 'epoch': '0.4'}\n", " 83% 100/120 [04:40<00:52, 2.61s/it][INFO|trainer.py:3815] 2026-09-27 05:35:34,061 >> Saving model checkpoint to saves/Qwen2.5-1.5B/lora/sft/checkpoint-100\n", "[INFO|configuration_utils.py:780] 2026-09-27 05:35:34,335 >> loading configuration file config.json from cache at /root/.cache/huggingface/hub/models--Qwen--Qwen2.5-1.5B-Instruct/snapshots/989aa7980e4cf806f80c7fef2b1adb7bc71aa306/config.json\n", "[INFO|configuration_utils.py:856] 2026-09-27 05:35:34,337 >> Model config Qwen2Config {\n", " \"architectures\": [\n", " \"Qwen2ForCausalLM\"\n", " ],\n", " \"attention_dropout\": 0.0,\n", " \"bos_token_id\": 151643,\n", " \"dtype\": \"bfloat16\",\n", " \"eos_token_id\": 151645,\n", " \"hidden_act\": \"silu\",\n", " \"hidden_size\": 1536,\n", " \"initializer_range\": 0.02,\n", " \"intermediate_size\": 8960,\n", " \"layer_types\": [\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\"\n", " ],\n", " \"max_position_embeddings\": 32768,\n", " \"max_window_layers\": 21,\n", " \"model_type\": \"qwen2\",\n", " \"num_attention_heads\": 12,\n", " \"num_hidden_layers\": 28,\n", " \"num_key_value_heads\": 2,\n", " \"pad_token_id\": null,\n", " \"rms_norm_eps\": 1e-06,\n", " \"rope_parameters\": {\n", " \"rope_theta\": 1000000.0,\n", " \"rope_type\": \"default\"\n", " },\n", " \"sliding_window\": null,\n", " \"tie_word_embeddings\": true,\n", " \"transformers_version\": \"5.8.0\",\n", " \"use_cache\": true,\n", " \"use_sliding_window\": false,\n", " \"vocab_size\": 151936\n", "}\n", "\n", "[INFO|tokenization_utils_base.py:3281] 2026-09-27 05:35:34,422 >> chat template saved in saves/Qwen2.5-1.5B/lora/sft/checkpoint-100/chat_template.jinja\n", "[INFO|tokenization_utils_base.py:2114] 2026-09-27 05:35:34,422 >> tokenizer config file saved in saves/Qwen2.5-1.5B/lora/sft/checkpoint-100/tokenizer_config.json\n", "{'loss': '2.303', 'grad_norm': '0.5519', 'learning_rate': '4.118e-06', 'epoch': '0.44'}\n", "{'loss': '2.212', 'grad_norm': '0.6914', 'learning_rate': '3.427e-08', 'epoch': '0.48'}\n", "100% 120/120 [05:33<00:00, 2.61s/it][INFO|trainer.py:3815] 2026-09-27 05:36:27,256 >> Saving model checkpoint to saves/Qwen2.5-1.5B/lora/sft/checkpoint-120\n", "[INFO|configuration_utils.py:780] 2026-09-27 05:36:27,553 >> loading configuration file config.json from cache at /root/.cache/huggingface/hub/models--Qwen--Qwen2.5-1.5B-Instruct/snapshots/989aa7980e4cf806f80c7fef2b1adb7bc71aa306/config.json\n", "[INFO|configuration_utils.py:856] 2026-09-27 05:36:27,555 >> Model config Qwen2Config {\n", " \"architectures\": [\n", " \"Qwen2ForCausalLM\"\n", " ],\n", " \"attention_dropout\": 0.0,\n", " \"bos_token_id\": 151643,\n", " \"dtype\": \"bfloat16\",\n", " \"eos_token_id\": 151645,\n", " \"hidden_act\": \"silu\",\n", " \"hidden_size\": 1536,\n", " \"initializer_range\": 0.02,\n", " \"intermediate_size\": 8960,\n", " \"layer_types\": [\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\"\n", " ],\n", " \"max_position_embeddings\": 32768,\n", " \"max_window_layers\": 21,\n", " \"model_type\": \"qwen2\",\n", " \"num_attention_heads\": 12,\n", " \"num_hidden_layers\": 28,\n", " \"num_key_value_heads\": 2,\n", " \"pad_token_id\": null,\n", " \"rms_norm_eps\": 1e-06,\n", " \"rope_parameters\": {\n", " \"rope_theta\": 1000000.0,\n", " \"rope_type\": \"default\"\n", " },\n", " \"sliding_window\": null,\n", " \"tie_word_embeddings\": true,\n", " \"transformers_version\": \"5.8.0\",\n", " \"use_cache\": true,\n", " \"use_sliding_window\": false,\n", " \"vocab_size\": 151936\n", "}\n", "\n", "[INFO|tokenization_utils_base.py:3281] 2026-09-27 05:36:27,626 >> chat template saved in saves/Qwen2.5-1.5B/lora/sft/checkpoint-120/chat_template.jinja\n", "[INFO|tokenization_utils_base.py:2114] 2026-09-27 05:36:27,626 >> tokenizer config file saved in saves/Qwen2.5-1.5B/lora/sft/checkpoint-120/tokenizer_config.json\n", "[INFO|trainer.py:1823] 2026-09-27 05:36:27,945 >> \n", "\n", "Training completed. Do not forget to share your model on huggingface.co/models =)\n", "\n", "\n", "{'train_runtime': '334.4', 'train_samples_per_second': '2.871', 'train_steps_per_second': '0.359', 'train_loss': '2.122', 'epoch': '0.48'}\n", "100% 120/120 [05:34<00:00, 2.79s/it]\n", "[INFO|trainer.py:3815] 2026-09-27 05:36:27,947 >> Saving model checkpoint to saves/Qwen2.5-1.5B/lora/sft\n", "[INFO|configuration_utils.py:780] 2026-09-27 05:36:28,206 >> loading configuration file config.json from cache at /root/.cache/huggingface/hub/models--Qwen--Qwen2.5-1.5B-Instruct/snapshots/989aa7980e4cf806f80c7fef2b1adb7bc71aa306/config.json\n", "[INFO|configuration_utils.py:856] 2026-09-27 05:36:28,208 >> Model config Qwen2Config {\n", " \"architectures\": [\n", " \"Qwen2ForCausalLM\"\n", " ],\n", " \"attention_dropout\": 0.0,\n", " \"bos_token_id\": 151643,\n", " \"dtype\": \"bfloat16\",\n", " \"eos_token_id\": 151645,\n", " \"hidden_act\": \"silu\",\n", " \"hidden_size\": 1536,\n", " \"initializer_range\": 0.02,\n", " \"intermediate_size\": 8960,\n", " \"layer_types\": [\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\"\n", " ],\n", " \"max_position_embeddings\": 32768,\n", " \"max_window_layers\": 21,\n", " \"model_type\": \"qwen2\",\n", " \"num_attention_heads\": 12,\n", " \"num_hidden_layers\": 28,\n", " \"num_key_value_heads\": 2,\n", " \"pad_token_id\": null,\n", " \"rms_norm_eps\": 1e-06,\n", " \"rope_parameters\": {\n", " \"rope_theta\": 1000000.0,\n", " \"rope_type\": \"default\"\n", " },\n", " \"sliding_window\": null,\n", " \"tie_word_embeddings\": true,\n", " \"transformers_version\": \"5.8.0\",\n", " \"use_cache\": true,\n", " \"use_sliding_window\": false,\n", " \"vocab_size\": 151936\n", "}\n", "\n", "[INFO|tokenization_utils_base.py:3281] 2026-09-27 05:36:28,296 >> chat template saved in saves/Qwen2.5-1.5B/lora/sft/chat_template.jinja\n", "[INFO|tokenization_utils_base.py:2114] 2026-09-27 05:36:28,298 >> tokenizer config file saved in saves/Qwen2.5-1.5B/lora/sft/tokenizer_config.json\n", "***** train metrics *****\n", " epoch = 0.48\n", " total_flos = 1838585GF\n", " train_loss = 2.1222\n", " train_runtime = 0:05:34.42\n", " train_samples_per_second = 2.871\n", " train_steps_per_second = 0.359\n", "[INFO|modelcard.py:263] 2026-09-27 05:36:28,431 >> Dropping the following result as it does not have all the necessary fields:\n", "{'task': {'name': 'Causal Language Modeling', 'type': 'text-generation'}}\n" ] } ] }, { "cell_type": "markdown", "source": [ "# Phase 2 — Preference Alignment (DPO)\n", "Direct Preference Optimization using the Hugging Face ultrafeedback_preference" ], "metadata": { "id": "K7Sthr9kXbyu" } }, { "cell_type": "code", "source": [ "import json\n", "\n", "dataset_info_path = \"data/dataset_info.json\"\n", "\n", "with open(dataset_info_path, \"r\") as f:\n", " dataset_info = json.load(f)\n", "\n", "# Register a fully compatible pairwise DPO dataset\n", "dataset_info[\"dpo_pairs\"] = {\n", " \"hf_hub_url\": \"argilla/distilabel-intel-orca-dpo-pairs\",\n", " \"ranking\": True,\n", " \"columns\": {\n", " \"prompt\": \"input\",\n", " \"chosen\": \"chosen\",\n", " \"rejected\": \"rejected\"\n", " }\n", "}\n", "\n", "with open(dataset_info_path, \"w\") as f:\n", " json.dump(dataset_info, f, indent=2)\n", "\n", "print(\"Updated dataset_info.json with compatible DPO pairs dataset\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "FrYtWFOIYKt7", "outputId": "2abbcbd7-89e6-4652-f5be-3dbeab43b45b" }, "execution_count": 13, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Updated dataset_info.json with compatible DPO pairs dataset\n" ] } ] }, { "cell_type": "code", "source": [ "import yaml\n", "\n", "dpo_config = {\n", " \"stage\": \"dpo\",\n", " \"do_train\": True,\n", " \"model_name_or_path\": \"Qwen/Qwen2.5-1.5B-Instruct\",\n", " \"adapter_name_or_path\": \"saves/Qwen2.5-1.5B/lora/sft\",\n", " \"dataset\": \"dpo_pairs\",\n", " \"max_samples\": 500,\n", " \"template\": \"qwen\",\n", " \"finetuning_type\": \"lora\",\n", " \"lora_target\": \"all\",\n", " \"output_dir\": \"saves/Qwen2.5-1.5B/lora/dpo\",\n", " \"overwrite_output_dir\": True,\n", " \"per_device_train_batch_size\": 1,\n", " \"gradient_accumulation_steps\": 8,\n", " \"lr_scheduler_type\": \"cosine\",\n", " \"logging_steps\": 5,\n", " \"save_steps\": 50,\n", " \"max_steps\": 60,\n", " \"learning_rate\": 5e-6,\n", " \"quantization_bit\": 4,\n", " \"fp16\": True,\n", "}\n", "\n", "with open(\"dpo_config.yaml\", \"w\") as f:\n", " yaml.dump(dpo_config, f)" ], "metadata": { "id": "7l4AAyAPXjng" }, "execution_count": 14, "outputs": [] }, { "cell_type": "code", "source": [ "!llamafactory-cli train dpo_config.yaml" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "IlITIUzjXs5J", "outputId": "ee759094-8f64-4c43-fa31-3c2a9082dc8b" }, "execution_count": 15, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "/usr/local/lib/python3.13/dist-packages/jieba/_compat.py:18: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.\n", " import pkg_resources\n", "[WARNING|2026-09-27 05:44:15] llamafactory.hparams.parser:149 >> We recommend enable `upcast_layernorm` in quantized training.\n", "[INFO|2026-09-27 05:44:15] llamafactory.hparams.parser:651 >> Process rank: 0, world size: 1, device: cuda:0, distributed training: False, compute dtype: torch.float16\n", "[INFO|configuration_utils.py:780] 2026-09-27 05:44:15,755 >> loading configuration file config.json from cache at /root/.cache/huggingface/hub/models--Qwen--Qwen2.5-1.5B-Instruct/snapshots/989aa7980e4cf806f80c7fef2b1adb7bc71aa306/config.json\n", "[INFO|configuration_utils.py:856] 2026-09-27 05:44:15,760 >> Model config Qwen2Config {\n", " \"architectures\": [\n", " \"Qwen2ForCausalLM\"\n", " ],\n", " \"attention_dropout\": 0.0,\n", " \"bos_token_id\": 151643,\n", " \"dtype\": \"bfloat16\",\n", " \"eos_token_id\": 151645,\n", " \"hidden_act\": \"silu\",\n", " \"hidden_size\": 1536,\n", " \"initializer_range\": 0.02,\n", " \"intermediate_size\": 8960,\n", " \"layer_types\": [\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\"\n", " ],\n", " \"max_position_embeddings\": 32768,\n", " \"max_window_layers\": 21,\n", " \"model_type\": \"qwen2\",\n", " \"num_attention_heads\": 12,\n", " \"num_hidden_layers\": 28,\n", " \"num_key_value_heads\": 2,\n", " \"pad_token_id\": null,\n", " \"rms_norm_eps\": 1e-06,\n", " \"rope_parameters\": {\n", " \"rope_theta\": 1000000.0,\n", " \"rope_type\": \"default\"\n", " },\n", " \"sliding_window\": null,\n", " \"tie_word_embeddings\": true,\n", " \"transformers_version\": \"5.8.0\",\n", " \"use_cache\": true,\n", " \"use_sliding_window\": false,\n", " \"vocab_size\": 151936\n", "}\n", "\n", "[INFO|configuration_utils.py:780] 2026-09-27 05:44:17,282 >> loading configuration file config.json from cache at /root/.cache/huggingface/hub/models--Qwen--Qwen2.5-1.5B-Instruct/snapshots/989aa7980e4cf806f80c7fef2b1adb7bc71aa306/config.json\n", "[INFO|configuration_utils.py:856] 2026-09-27 05:44:17,284 >> Model config Qwen2Config {\n", " \"architectures\": [\n", " \"Qwen2ForCausalLM\"\n", " ],\n", " \"attention_dropout\": 0.0,\n", " \"bos_token_id\": 151643,\n", " \"dtype\": \"bfloat16\",\n", " \"eos_token_id\": 151645,\n", " \"hidden_act\": \"silu\",\n", " \"hidden_size\": 1536,\n", " \"initializer_range\": 0.02,\n", " \"intermediate_size\": 8960,\n", " \"layer_types\": [\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\"\n", " ],\n", " \"max_position_embeddings\": 32768,\n", " \"max_window_layers\": 21,\n", " \"model_type\": \"qwen2\",\n", " \"num_attention_heads\": 12,\n", " \"num_hidden_layers\": 28,\n", " \"num_key_value_heads\": 2,\n", " \"pad_token_id\": null,\n", " \"rms_norm_eps\": 1e-06,\n", " \"rope_parameters\": {\n", " \"rope_theta\": 1000000.0,\n", " \"rope_type\": \"default\"\n", " },\n", " \"sliding_window\": null,\n", " \"tie_word_embeddings\": true,\n", " \"transformers_version\": \"5.8.0\",\n", " \"use_cache\": true,\n", " \"use_sliding_window\": false,\n", " \"vocab_size\": 151936\n", "}\n", "\n", "[INFO|configuration_utils.py:780] 2026-09-27 05:44:17,385 >> loading configuration file config.json from cache at /root/.cache/huggingface/hub/models--Qwen--Qwen2.5-1.5B-Instruct/snapshots/989aa7980e4cf806f80c7fef2b1adb7bc71aa306/config.json\n", "[INFO|configuration_utils.py:856] 2026-09-27 05:44:17,387 >> Model config Qwen2Config {\n", " \"architectures\": [\n", " \"Qwen2ForCausalLM\"\n", " ],\n", " \"attention_dropout\": 0.0,\n", " \"bos_token_id\": 151643,\n", " \"dtype\": \"bfloat16\",\n", " \"eos_token_id\": 151645,\n", " \"hidden_act\": \"silu\",\n", " \"hidden_size\": 1536,\n", " \"initializer_range\": 0.02,\n", " \"intermediate_size\": 8960,\n", " \"layer_types\": [\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\"\n", " ],\n", " \"max_position_embeddings\": 32768,\n", " \"max_window_layers\": 21,\n", " \"model_type\": \"qwen2\",\n", " \"num_attention_heads\": 12,\n", " \"num_hidden_layers\": 28,\n", " \"num_key_value_heads\": 2,\n", " \"pad_token_id\": null,\n", " \"rms_norm_eps\": 1e-06,\n", " \"rope_parameters\": {\n", " \"rope_theta\": 1000000.0,\n", " \"rope_type\": \"default\"\n", " },\n", " \"sliding_window\": null,\n", " \"tie_word_embeddings\": true,\n", " \"transformers_version\": \"5.8.0\",\n", " \"use_cache\": true,\n", " \"use_sliding_window\": false,\n", " \"vocab_size\": 151936\n", "}\n", "\n", "[INFO|2026-09-27 05:44:18] llamafactory.data.loader:144 >> Loading dataset argilla/distilabel-intel-orca-dpo-pairs...\n", "README.md: 100% 9.05k/9.05k [00:00<00:00, 14.4MB/s]\n", "\n", "data/train-00000-of-00001.parquet: downloading bytes: 0% 0.00/79.2M [00:00system\n", "You are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\n", "<|im_start|>user\n", "You will be given a definition of a task first, then some input of the task.\n", "This task is about using the specified sentence and converting the sentence to Resource Description Framework (RDF) triplets of the form (subject, predicate object). The RDF triplets generated must be such that the triplets accurately capture the structure and semantics of the input sentence. The input is a sentence and the output is a list of triplets of the form [subject, predicate, object] that capture the relationships present in the sentence. When a sentence has more than 1 RDF triplet possible, the output must contain all of them.\n", "\n", "AFC Ajax (amateurs)'s ground is Sportpark De Toekomst where Ajax Youth Academy also play.\n", "Output:<|im_end|>\n", "<|im_start|>assistant\n", "[\n", " [\"AFC Ajax (amateurs)\", \"has ground\", \"Sportpark De Toekomst\"],\n", " [\"Ajax Youth Academy\", \"plays at\", \"Sportpark De Toekomst\"]\n", "]<|im_end|>\n", "\n", "chosen_label_ids:\n", "[-100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, 9640, 220, 4383, 32, 6754, 41162, 320, 309, 35206, 11583, 330, 4648, 4910, 497, 330, 58737, 28736, 1581, 2014, 1225, 316, 267, 8097, 220, 4383, 40077, 29060, 15815, 497, 330, 27797, 518, 497, 330, 58737, 28736, 1581, 2014, 1225, 316, 267, 7026, 60, 151645, 198]\n", "chosen_labels:\n", "[\n", " [\"AFC Ajax (amateurs)\", \"has ground\", \"Sportpark De Toekomst\"],\n", " [\"Ajax Youth Academy\", \"plays at\", \"Sportpark De Toekomst\"]\n", "]<|im_end|>\n", "\n", "rejected_input_ids:\n", "[151644, 8948, 198, 2610, 525, 1207, 16948, 11, 3465, 553, 54364, 14817, 13, 1446, 525, 264, 10950, 17847, 13, 151645, 198, 151644, 872, 198, 2610, 686, 387, 2661, 264, 7271, 315, 264, 3383, 1156, 11, 1221, 1045, 1946, 315, 279, 3383, 624, 1986, 3383, 374, 911, 1667, 279, 5189, 11652, 323, 33437, 279, 11652, 311, 11765, 7662, 23752, 320, 49, 5262, 8, 23725, 2576, 315, 279, 1352, 320, 11501, 11, 24283, 1633, 568, 576, 68399, 23725, 2576, 7907, 1969, 387, 1741, 429, 279, 23725, 2576, 29257, 12322, 279, 5944, 323, 52694, 315, 279, 1946, 11652, 13, 576, 1946, 374, 264, 11652, 323, 279, 2550, 374, 264, 1140, 315, 23725, 2576, 315, 279, 1352, 508, 11501, 11, 24283, 11, 1633, 60, 429, 12322, 279, 11871, 3042, 304, 279, 11652, 13, 3197, 264, 11652, 702, 803, 1091, 220, 16, 68399, 98709, 3204, 11, 279, 2550, 1969, 6644, 678, 315, 1105, 382, 32, 6754, 41162, 320, 309, 35206, 21636, 82, 4910, 374, 18207, 28736, 1581, 2014, 1225, 316, 267, 1380, 41162, 29060, 15815, 1083, 1486, 624, 5097, 25, 151645, 198, 151644, 77091, 198, 22555, 11, 358, 4172, 387, 6247, 311, 1492, 0, 5692, 525, 279, 68399, 23725, 2576, 369, 279, 1946, 11652, 1447, 22519, 6754, 41162, 320, 309, 35206, 701, 702, 30714, 11, 18207, 28736, 1581, 2014, 1225, 316, 267, 921, 22519, 14518, 29060, 15815, 11, 11088, 1655, 11, 18207, 28736, 1581, 2014, 1225, 316, 267, 2533, 69769, 1447, 9, 63536, 41162, 320, 309, 35206, 8, 374, 279, 3832, 315, 279, 1156, 98709, 11, 323, 702, 30714, 374, 279, 24283, 429, 16555, 279, 5025, 1948, 63536, 41162, 320, 309, 35206, 8, 323, 18207, 28736, 1581, 2014, 1225, 316, 267, 624, 9, 41162, 29060, 15815, 374, 279, 3832, 315, 279, 2086, 98709, 11, 323, 11088, 1655, 374, 279, 24283, 429, 16555, 279, 5025, 1948, 41162, 29060, 15815, 323, 18207, 28736, 1581, 2014, 1225, 316, 267, 382, 9112, 429, 1052, 1231, 387, 1008, 3204, 68399, 23725, 2576, 429, 1410, 387, 14257, 504, 279, 1946, 11652, 11, 714, 279, 3403, 23725, 2576, 12322, 279, 1887, 11871, 3042, 304, 279, 11652, 13, 151645, 198]\n", "rejected_inputs:\n", "<|im_start|>system\n", "You are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\n", "<|im_start|>user\n", "You will be given a definition of a task first, then some input of the task.\n", "This task is about using the specified sentence and converting the sentence to Resource Description Framework (RDF) triplets of the form (subject, predicate object). The RDF triplets generated must be such that the triplets accurately capture the structure and semantics of the input sentence. The input is a sentence and the output is a list of triplets of the form [subject, predicate, object] that capture the relationships present in the sentence. When a sentence has more than 1 RDF triplet possible, the output must contain all of them.\n", "\n", "AFC Ajax (amateurs)'s ground is Sportpark De Toekomst where Ajax Youth Academy also play.\n", "Output:<|im_end|>\n", "<|im_start|>assistant\n", " Sure, I'd be happy to help! Here are the RDF triplets for the input sentence:\n", "\n", "[AFC Ajax (amateurs), hasGround, Sportpark De Toekomst]\n", "[Ajax Youth Academy, playsAt, Sportpark De Toekomst]\n", "\n", "Explanation:\n", "\n", "* AFC Ajax (amateurs) is the subject of the first triplet, and hasGround is the predicate that describes the relationship between AFC Ajax (amateurs) and Sportpark De Toekomst.\n", "* Ajax Youth Academy is the subject of the second triplet, and playsAt is the predicate that describes the relationship between Ajax Youth Academy and Sportpark De Toekomst.\n", "\n", "Note that there may be other possible RDF triplets that could be derived from the input sentence, but the above triplets capture the main relationships present in the sentence.<|im_end|>\n", "\n", "rejected_label_ids:\n", "[-100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, 22555, 11, 358, 4172, 387, 6247, 311, 1492, 0, 5692, 525, 279, 68399, 23725, 2576, 369, 279, 1946, 11652, 1447, 22519, 6754, 41162, 320, 309, 35206, 701, 702, 30714, 11, 18207, 28736, 1581, 2014, 1225, 316, 267, 921, 22519, 14518, 29060, 15815, 11, 11088, 1655, 11, 18207, 28736, 1581, 2014, 1225, 316, 267, 2533, 69769, 1447, 9, 63536, 41162, 320, 309, 35206, 8, 374, 279, 3832, 315, 279, 1156, 98709, 11, 323, 702, 30714, 374, 279, 24283, 429, 16555, 279, 5025, 1948, 63536, 41162, 320, 309, 35206, 8, 323, 18207, 28736, 1581, 2014, 1225, 316, 267, 624, 9, 41162, 29060, 15815, 374, 279, 3832, 315, 279, 2086, 98709, 11, 323, 11088, 1655, 374, 279, 24283, 429, 16555, 279, 5025, 1948, 41162, 29060, 15815, 323, 18207, 28736, 1581, 2014, 1225, 316, 267, 382, 9112, 429, 1052, 1231, 387, 1008, 3204, 68399, 23725, 2576, 429, 1410, 387, 14257, 504, 279, 1946, 11652, 11, 714, 279, 3403, 23725, 2576, 12322, 279, 1887, 11871, 3042, 304, 279, 11652, 13, 151645, 198]\n", "rejected_labels:\n", " Sure, I'd be happy to help! Here are the RDF triplets for the input sentence:\n", "\n", "[AFC Ajax (amateurs), hasGround, Sportpark De Toekomst]\n", "[Ajax Youth Academy, playsAt, Sportpark De Toekomst]\n", "\n", "Explanation:\n", "\n", "* AFC Ajax (amateurs) is the subject of the first triplet, and hasGround is the predicate that describes the relationship between AFC Ajax (amateurs) and Sportpark De Toekomst.\n", "* Ajax Youth Academy is the subject of the second triplet, and playsAt is the predicate that describes the relationship between Ajax Youth Academy and Sportpark De Toekomst.\n", "\n", "Note that there may be other possible RDF triplets that could be derived from the input sentence, but the above triplets capture the main relationships present in the sentence.<|im_end|>\n", "\n", "[INFO|configuration_utils.py:780] 2026-09-27 05:44:26,280 >> loading configuration file config.json from cache at /root/.cache/huggingface/hub/models--Qwen--Qwen2.5-1.5B-Instruct/snapshots/989aa7980e4cf806f80c7fef2b1adb7bc71aa306/config.json\n", "[INFO|configuration_utils.py:856] 2026-09-27 05:44:26,282 >> Model config Qwen2Config {\n", " \"architectures\": [\n", " \"Qwen2ForCausalLM\"\n", " ],\n", " \"attention_dropout\": 0.0,\n", " \"bos_token_id\": 151643,\n", " \"dtype\": \"bfloat16\",\n", " \"eos_token_id\": 151645,\n", " \"hidden_act\": \"silu\",\n", " \"hidden_size\": 1536,\n", " \"initializer_range\": 0.02,\n", " \"intermediate_size\": 8960,\n", " \"layer_types\": [\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\"\n", " ],\n", " \"max_position_embeddings\": 32768,\n", " \"max_window_layers\": 21,\n", " \"model_type\": \"qwen2\",\n", " \"num_attention_heads\": 12,\n", " \"num_hidden_layers\": 28,\n", " \"num_key_value_heads\": 2,\n", " \"pad_token_id\": null,\n", " \"rms_norm_eps\": 1e-06,\n", " \"rope_parameters\": {\n", " \"rope_theta\": 1000000.0,\n", " \"rope_type\": \"default\"\n", " },\n", " \"sliding_window\": null,\n", " \"tie_word_embeddings\": true,\n", " \"transformers_version\": \"5.8.0\",\n", " \"use_cache\": true,\n", " \"use_sliding_window\": false,\n", " \"vocab_size\": 151936\n", "}\n", "\n", "[INFO|2026-09-27 05:44:26] llamafactory.model.model_utils.quantization:144 >> Quantizing model to 4 bit with bitsandbytes.\n", "[INFO|2026-09-27 05:44:26] llamafactory.model.model_utils.kv_cache:144 >> KV cache is disabled during training.\n", "[INFO|modeling_utils.py:769] 2026-09-27 05:44:28,432 >> loading weights file model.safetensors from cache at /root/.cache/huggingface/hub/models--Qwen--Qwen2.5-1.5B-Instruct/snapshots/989aa7980e4cf806f80c7fef2b1adb7bc71aa306/model.safetensors\n", "[INFO|modeling_utils.py:839] 2026-09-27 05:44:28,432 >> Will use dtype=torch.bfloat16 as defined in model's config object\n", "[INFO|configuration_utils.py:1086] 2026-09-27 05:44:28,433 >> Generate config GenerationConfig {\n", " \"bos_token_id\": 151643,\n", " \"eos_token_id\": 151645,\n", " \"output_attentions\": false,\n", " \"output_hidden_states\": false,\n", " \"use_cache\": false\n", "}\n", "\n", "Loading weights: 100% 338/338 [00:01<00:00, 277.72it/s]\n", "[INFO|configuration_utils.py:1039] 2026-09-27 05:44:30,014 >> loading configuration file generation_config.json from cache at /root/.cache/huggingface/hub/models--Qwen--Qwen2.5-1.5B-Instruct/snapshots/989aa7980e4cf806f80c7fef2b1adb7bc71aa306/generation_config.json\n", "[INFO|configuration_utils.py:1086] 2026-09-27 05:44:30,015 >> Generate config GenerationConfig {\n", " \"bos_token_id\": 151643,\n", " \"do_sample\": true,\n", " \"eos_token_id\": [\n", " 151645,\n", " 151643\n", " ],\n", " \"pad_token_id\": 151643,\n", " \"repetition_penalty\": 1.1,\n", " \"temperature\": 0.7,\n", " \"top_k\": 20,\n", " \"top_p\": 0.8\n", "}\n", "\n", "[INFO|2026-09-27 05:44:30] llamafactory.model.model_utils.checkpointing:144 >> Gradient checkpointing enabled.\n", "[INFO|2026-09-27 05:44:30] llamafactory.model.model_utils.attention:144 >> Using torch SDPA for faster training and inference.\n", "[INFO|2026-09-27 05:44:30] llamafactory.model.adapter:144 >> Upcasting trainable params to float32.\n", "[INFO|2026-09-27 05:44:30] llamafactory.model.adapter:144 >> Fine-tuning method: LoRA\n", "[INFO|2026-09-27 05:44:30] llamafactory.model.adapter:144 >> Loaded adapter(s): saves/Qwen2.5-1.5B/lora/sft\n", "[INFO|2026-09-27 05:44:30] llamafactory.model.loader:144 >> trainable params: 9,232,384 || all params: 1,552,946,688 || trainable%: 0.5945\n", "/usr/local/lib/python3.13/dist-packages/trl/trainer/dpo_trainer.py:559: UserWarning: The `padding_value` property is deprecated and will be removed in version 0.26.0. Please use `pad_token_id` instead.\n", " warnings.warn(\n", "[INFO|trainer.py:680] 2026-09-27 05:44:32,718 >> max_steps is given, it will override any value given in num_train_epochs\n", "[WARNING|trainer_utils.py:1238] 2026-09-27 05:44:32,719 >> The tokenizer has new PAD/BOS/EOS tokens that differ from the model config and generation config. The model config and generation config were aligned accordingly, being updated with the tokenizer's values. Updated tokens: {'bos_token_id': None, 'pad_token_id': 151643}.\n", "[INFO|trainer.py:1469] 2026-09-27 05:44:33,321 >> ***** Running training *****\n", "[INFO|trainer.py:1470] 2026-09-27 05:44:33,321 >> Num examples = 500\n", "[INFO|trainer.py:1471] 2026-09-27 05:44:33,321 >> Num Epochs = 1\n", "[INFO|trainer.py:1472] 2026-09-27 05:44:33,321 >> Num update steps per epoch = 63\n", "[INFO|trainer.py:1473] 2026-09-27 05:44:33,321 >> Instantaneous batch size per device = 1\n", "[INFO|trainer.py:1476] 2026-09-27 05:44:33,321 >> Total train batch size (w. parallel, distributed & accumulation) = 8\n", "[INFO|trainer.py:1477] 2026-09-27 05:44:33,322 >> Gradient Accumulation steps = 8\n", "[INFO|trainer.py:1478] 2026-09-27 05:44:33,322 >> Total optimization steps = 60\n", "[INFO|trainer.py:1479] 2026-09-27 05:44:33,326 >> Number of trainable parameters = 9,232,384\n", "{'loss': '1.356', 'grad_norm': '9.257', 'learning_rate': '4.945e-06', 'rewards/chosen': '-0.7769', 'rewards/rejected': '0.01024', 'rewards/accuracies': '0.3', 'rewards/margins': '-0.7872', 'logps/chosen': '-161.2', 'logps/rejected': '-214.4', 'logits/chosen': '0.137', 'logits/rejected': '0.127', 'epoch': '0.08'}\n", "{'loss': '0.9851', 'grad_norm': '8.233', 'learning_rate': '4.728e-06', 'rewards/chosen': '-0.2445', 'rewards/rejected': '-0.0457', 'rewards/accuracies': '0.375', 'rewards/margins': '-0.1988', 'logps/chosen': '-171.8', 'logps/rejected': '-224.9', 'logits/chosen': '0.2215', 'logits/rejected': '0.1657', 'epoch': '0.16'}\n", "{'loss': '0.8467', 'grad_norm': '11.78', 'learning_rate': '4.358e-06', 'rewards/chosen': '-0.4566', 'rewards/rejected': '-0.435', 'rewards/accuracies': '0.575', 'rewards/margins': '-0.02168', 'logps/chosen': '-165', 'logps/rejected': '-243.1', 'logits/chosen': '0.02796', 'logits/rejected': '0.07079', 'epoch': '0.24'}\n", "{'loss': '1.016', 'grad_norm': '7.48', 'learning_rate': '3.862e-06', 'rewards/chosen': '-0.5373', 'rewards/rejected': '-0.136', 'rewards/accuracies': '0.3', 'rewards/margins': '-0.4013', 'logps/chosen': '-179.5', 'logps/rejected': '-231.2', 'logits/chosen': '0.02702', 'logits/rejected': '0.09938', 'epoch': '0.32'}\n", "{'loss': '1.024', 'grad_norm': '9.955', 'learning_rate': '3.273e-06', 'rewards/chosen': '-0.741', 'rewards/rejected': '-0.3571', 'rewards/accuracies': '0.4', 'rewards/margins': '-0.3839', 'logps/chosen': '-266.9', 'logps/rejected': '-186.4', 'logits/chosen': '-0.09988', 'logits/rejected': '0.01203', 'epoch': '0.4'}\n", "{'loss': '1.035', 'grad_norm': '11.42', 'learning_rate': '2.631e-06', 'rewards/chosen': '-0.7152', 'rewards/rejected': '-0.4222', 'rewards/accuracies': '0.45', 'rewards/margins': '-0.2929', 'logps/chosen': '-248.2', 'logps/rejected': '-265.4', 'logits/chosen': '-0.05337', 'logits/rejected': '0.0487', 'epoch': '0.48'}\n", "{'loss': '0.6695', 'grad_norm': '6.638', 'learning_rate': '1.98e-06', 'rewards/chosen': '-0.5244', 'rewards/rejected': '-0.9159', 'rewards/accuracies': '0.7', 'rewards/margins': '0.3915', 'logps/chosen': '-199.8', 'logps/rejected': '-223.1', 'logits/chosen': '0.1069', 'logits/rejected': '0.06608', 'epoch': '0.56'}\n", "{'loss': '0.6626', 'grad_norm': '6.494', 'learning_rate': '1.365e-06', 'rewards/chosen': '-0.4068', 'rewards/rejected': '-0.7642', 'rewards/accuracies': '0.65', 'rewards/margins': '0.3574', 'logps/chosen': '-228.5', 'logps/rejected': '-263.4', 'logits/chosen': '-0.0471', 'logits/rejected': '-0.01774', 'epoch': '0.64'}\n", "{'loss': '0.6912', 'grad_norm': '7.757', 'learning_rate': '8.272e-07', 'rewards/chosen': '-0.8172', 'rewards/rejected': '-1.323', 'rewards/accuracies': '0.575', 'rewards/margins': '0.5061', 'logps/chosen': '-249.9', 'logps/rejected': '-238', 'logits/chosen': '0.006602', 'logits/rejected': '-0.06929', 'epoch': '0.72'}\n", "{'loss': '0.8659', 'grad_norm': '14.18', 'learning_rate': '4.033e-07', 'rewards/chosen': '-0.6131', 'rewards/rejected': '-0.707', 'rewards/accuracies': '0.525', 'rewards/margins': '0.09386', 'logps/chosen': '-228.7', 'logps/rejected': '-257.4', 'logits/chosen': '-0.08923', 'logits/rejected': '-0.007486', 'epoch': '0.8'}\n", " 83% 50/60 [08:52<01:44, 10.45s/it][INFO|trainer.py:3815] 2026-09-27 05:53:25,588 >> Saving model checkpoint to saves/Qwen2.5-1.5B/lora/dpo/checkpoint-50\n", "[INFO|configuration_utils.py:780] 2026-09-27 05:53:26,908 >> loading configuration file config.json from cache at /root/.cache/huggingface/hub/models--Qwen--Qwen2.5-1.5B-Instruct/snapshots/989aa7980e4cf806f80c7fef2b1adb7bc71aa306/config.json\n", "[INFO|configuration_utils.py:856] 2026-09-27 05:53:26,911 >> Model config Qwen2Config {\n", " \"architectures\": [\n", " \"Qwen2ForCausalLM\"\n", " ],\n", " \"attention_dropout\": 0.0,\n", " \"bos_token_id\": 151643,\n", " \"dtype\": \"bfloat16\",\n", " \"eos_token_id\": 151645,\n", " \"hidden_act\": \"silu\",\n", " \"hidden_size\": 1536,\n", " \"initializer_range\": 0.02,\n", " \"intermediate_size\": 8960,\n", " \"layer_types\": [\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\"\n", " ],\n", " \"max_position_embeddings\": 32768,\n", " \"max_window_layers\": 21,\n", " \"model_type\": \"qwen2\",\n", " \"num_attention_heads\": 12,\n", " \"num_hidden_layers\": 28,\n", " \"num_key_value_heads\": 2,\n", " \"pad_token_id\": null,\n", " \"rms_norm_eps\": 1e-06,\n", " \"rope_parameters\": {\n", " \"rope_theta\": 1000000.0,\n", " \"rope_type\": \"default\"\n", " },\n", " \"sliding_window\": null,\n", " \"tie_word_embeddings\": true,\n", " \"transformers_version\": \"5.8.0\",\n", " \"use_cache\": true,\n", " \"use_sliding_window\": false,\n", " \"vocab_size\": 151936\n", "}\n", "\n", "[INFO|tokenization_utils_base.py:3281] 2026-09-27 05:53:27,048 >> chat template saved in saves/Qwen2.5-1.5B/lora/dpo/checkpoint-50/chat_template.jinja\n", "[INFO|tokenization_utils_base.py:2114] 2026-09-27 05:53:27,048 >> tokenizer config file saved in saves/Qwen2.5-1.5B/lora/dpo/checkpoint-50/tokenizer_config.json\n", "{'loss': '0.7295', 'grad_norm': '7.161', 'learning_rate': '1.224e-07', 'rewards/chosen': '-0.4321', 'rewards/rejected': '-0.7771', 'rewards/accuracies': '0.575', 'rewards/margins': '0.3449', 'logps/chosen': '-201.3', 'logps/rejected': '-245.4', 'logits/chosen': '0.1134', 'logits/rejected': '0.05672', 'epoch': '0.88'}\n", "{'loss': '0.6169', 'grad_norm': '7.107', 'learning_rate': '3.426e-09', 'rewards/chosen': '-0.7781', 'rewards/rejected': '-1.201', 'rewards/accuracies': '0.675', 'rewards/margins': '0.4226', 'logps/chosen': '-193.2', 'logps/rejected': '-257.7', 'logits/chosen': '0.02918', 'logits/rejected': '0.06772', 'epoch': '0.96'}\n", "100% 60/60 [10:46<00:00, 10.82s/it][INFO|trainer.py:3815] 2026-09-27 05:55:19,830 >> Saving model checkpoint to saves/Qwen2.5-1.5B/lora/dpo/checkpoint-60\n", "[INFO|configuration_utils.py:780] 2026-09-27 05:55:20,105 >> loading configuration file config.json from cache at /root/.cache/huggingface/hub/models--Qwen--Qwen2.5-1.5B-Instruct/snapshots/989aa7980e4cf806f80c7fef2b1adb7bc71aa306/config.json\n", "[INFO|configuration_utils.py:856] 2026-09-27 05:55:20,107 >> Model config Qwen2Config {\n", " \"architectures\": [\n", " \"Qwen2ForCausalLM\"\n", " ],\n", " \"attention_dropout\": 0.0,\n", " \"bos_token_id\": 151643,\n", " \"dtype\": \"bfloat16\",\n", " \"eos_token_id\": 151645,\n", " \"hidden_act\": \"silu\",\n", " \"hidden_size\": 1536,\n", " \"initializer_range\": 0.02,\n", " \"intermediate_size\": 8960,\n", " \"layer_types\": [\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\"\n", " ],\n", " \"max_position_embeddings\": 32768,\n", " \"max_window_layers\": 21,\n", " \"model_type\": \"qwen2\",\n", " \"num_attention_heads\": 12,\n", " \"num_hidden_layers\": 28,\n", " \"num_key_value_heads\": 2,\n", " \"pad_token_id\": null,\n", " \"rms_norm_eps\": 1e-06,\n", " \"rope_parameters\": {\n", " \"rope_theta\": 1000000.0,\n", " \"rope_type\": \"default\"\n", " },\n", " \"sliding_window\": null,\n", " \"tie_word_embeddings\": true,\n", " \"transformers_version\": \"5.8.0\",\n", " \"use_cache\": true,\n", " \"use_sliding_window\": false,\n", " \"vocab_size\": 151936\n", "}\n", "\n", "[INFO|tokenization_utils_base.py:3281] 2026-09-27 05:55:20,173 >> chat template saved in saves/Qwen2.5-1.5B/lora/dpo/checkpoint-60/chat_template.jinja\n", "[INFO|tokenization_utils_base.py:2114] 2026-09-27 05:55:20,174 >> tokenizer config file saved in saves/Qwen2.5-1.5B/lora/dpo/checkpoint-60/tokenizer_config.json\n", "[INFO|trainer.py:1823] 2026-09-27 05:55:20,521 >> \n", "\n", "Training completed. Do not forget to share your model on huggingface.co/models =)\n", "\n", "\n", "{'train_runtime': '647.2', 'train_samples_per_second': '0.742', 'train_steps_per_second': '0.093', 'train_loss': '0.8749', 'epoch': '0.96'}\n", "100% 60/60 [10:47<00:00, 10.79s/it]\n", "[INFO|trainer.py:3815] 2026-09-27 05:55:20,523 >> Saving model checkpoint to saves/Qwen2.5-1.5B/lora/dpo\n", "[INFO|configuration_utils.py:780] 2026-09-27 05:55:20,757 >> loading configuration file config.json from cache at /root/.cache/huggingface/hub/models--Qwen--Qwen2.5-1.5B-Instruct/snapshots/989aa7980e4cf806f80c7fef2b1adb7bc71aa306/config.json\n", "[INFO|configuration_utils.py:856] 2026-09-27 05:55:20,759 >> Model config Qwen2Config {\n", " \"architectures\": [\n", " \"Qwen2ForCausalLM\"\n", " ],\n", " \"attention_dropout\": 0.0,\n", " \"bos_token_id\": 151643,\n", " \"dtype\": \"bfloat16\",\n", " \"eos_token_id\": 151645,\n", " \"hidden_act\": \"silu\",\n", " \"hidden_size\": 1536,\n", " \"initializer_range\": 0.02,\n", " \"intermediate_size\": 8960,\n", " \"layer_types\": [\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\"\n", " ],\n", " \"max_position_embeddings\": 32768,\n", " \"max_window_layers\": 21,\n", " \"model_type\": \"qwen2\",\n", " \"num_attention_heads\": 12,\n", " \"num_hidden_layers\": 28,\n", " \"num_key_value_heads\": 2,\n", " \"pad_token_id\": null,\n", " \"rms_norm_eps\": 1e-06,\n", " \"rope_parameters\": {\n", " \"rope_theta\": 1000000.0,\n", " \"rope_type\": \"default\"\n", " },\n", " \"sliding_window\": null,\n", " \"tie_word_embeddings\": true,\n", " \"transformers_version\": \"5.8.0\",\n", " \"use_cache\": true,\n", " \"use_sliding_window\": false,\n", " \"vocab_size\": 151936\n", "}\n", "\n", "[INFO|tokenization_utils_base.py:3281] 2026-09-27 05:55:20,849 >> chat template saved in saves/Qwen2.5-1.5B/lora/dpo/chat_template.jinja\n", "[INFO|tokenization_utils_base.py:2114] 2026-09-27 05:55:20,850 >> tokenizer config file saved in saves/Qwen2.5-1.5B/lora/dpo/tokenizer_config.json\n", "***** train metrics *****\n", " epoch = 0.96\n", " total_flos = 3286187GF\n", " train_loss = 0.8749\n", " train_runtime = 0:10:47.19\n", " train_samples_per_second = 0.742\n", " train_steps_per_second = 0.093\n", "[INFO|modelcard.py:263] 2026-09-27 05:55:20,964 >> Dropping the following result as it does not have all the necessary fields:\n", "{'task': {'name': 'Causal Language Modeling', 'type': 'text-generation'}}\n" ] } ] }, { "cell_type": "code", "source": [ "import json\n", "import matplotlib.pyplot as plt\n", "\n", "def plot_training_loss(trainer_state_path, title):\n", " with open(trainer_state_path, \"r\") as f:\n", " data = json.load(f)\n", "\n", " steps = []\n", " losses = []\n", " for log in data.get(\"log_history\", []):\n", " if \"loss\" in log:\n", " steps.append(log[\"step\"])\n", " losses.append(log[\"loss\"])\n", "\n", " plt.figure(figsize=(8, 4))\n", " plt.plot(steps, losses, marker='o', color='#2b5c8f', label='Training Loss')\n", " plt.title(title, fontsize=12, fontweight='bold')\n", " plt.xlabel('Step')\n", " plt.ylabel('Loss')\n", " plt.grid(True, linestyle='--', alpha=0.6)\n", " plt.legend()\n", " plt.show()\n", "\n", "# Plot Phase 1 SFT Loss\n", "plot_training_loss(\"saves/Qwen2.5-1.5B/lora/sft/trainer_state.json\", \"Phase 1: SFT Training Loss\")\n", "\n", "# Plot Phase 2 DPO Alignment Loss\n", "plot_training_loss(\"saves/Qwen2.5-1.5B/lora/dpo/trainer_state.json\", \"Phase 2: DPO Alignment Loss\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 803 }, "id": "YQGZdwaIblbc", "outputId": "5658b0b9-c54f-4ad0-dbf8-76083c101d84" }, "execution_count": 17, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "
" ], "image/png": 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\n" 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\n" }, "metadata": {} } ] }, { "cell_type": "code", "source": [ "import yaml\n", "\n", "export_config = {\n", " \"model_name_or_path\": \"Qwen/Qwen2.5-1.5B-Instruct\",\n", " \"adapter_name_or_path\": \"saves/Qwen2.5-1.5B/lora/dpo\",\n", " \"template\": \"qwen\",\n", " \"finetuning_type\": \"lora\",\n", " \"export_dir\": \"merged_models/Qwen2.5-1.5B-Financial-Code-Engine\",\n", " \"export_size\": 2,\n", " \"export_device\": \"cpu\", # Merges on CPU RAM to avoid CUDA OOM\n", "}\n", "\n", "with open(\"export_config.yaml\", \"w\") as f:\n", " yaml.dump(export_config, f)\n", "\n", "# Execute Export & Weight Fusing\n", "!llamafactory-cli export export_config.yaml" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "ZavGLmf2cM6_", "outputId": "3e4b8303-65a0-4e6d-fed6-a16fc7a95414" }, "execution_count": 18, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "/usr/local/lib/python3.13/dist-packages/jieba/_compat.py:18: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.\n", " import pkg_resources\n", "[INFO|configuration_utils.py:780] 2026-09-27 05:57:54,040 >> loading configuration file config.json from cache at /root/.cache/huggingface/hub/models--Qwen--Qwen2.5-1.5B-Instruct/snapshots/989aa7980e4cf806f80c7fef2b1adb7bc71aa306/config.json\n", "[INFO|configuration_utils.py:856] 2026-09-27 05:57:54,045 >> Model config Qwen2Config {\n", " \"architectures\": [\n", " \"Qwen2ForCausalLM\"\n", " ],\n", " \"attention_dropout\": 0.0,\n", " \"bos_token_id\": 151643,\n", " \"dtype\": \"bfloat16\",\n", " \"eos_token_id\": 151645,\n", " \"hidden_act\": \"silu\",\n", " \"hidden_size\": 1536,\n", " \"initializer_range\": 0.02,\n", " \"intermediate_size\": 8960,\n", " \"layer_types\": [\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\"\n", " ],\n", " \"max_position_embeddings\": 32768,\n", " \"max_window_layers\": 21,\n", " \"model_type\": \"qwen2\",\n", " \"num_attention_heads\": 12,\n", " \"num_hidden_layers\": 28,\n", " \"num_key_value_heads\": 2,\n", " \"pad_token_id\": null,\n", " \"rms_norm_eps\": 1e-06,\n", " \"rope_parameters\": {\n", " \"rope_theta\": 1000000.0,\n", " \"rope_type\": \"default\"\n", " },\n", " \"sliding_window\": null,\n", " \"tie_word_embeddings\": true,\n", " \"transformers_version\": \"5.8.0\",\n", " \"use_cache\": true,\n", " \"use_sliding_window\": false,\n", " \"vocab_size\": 151936\n", "}\n", "\n", "[INFO|configuration_utils.py:780] 2026-09-27 05:57:55,445 >> loading configuration file config.json from cache at /root/.cache/huggingface/hub/models--Qwen--Qwen2.5-1.5B-Instruct/snapshots/989aa7980e4cf806f80c7fef2b1adb7bc71aa306/config.json\n", "[INFO|configuration_utils.py:856] 2026-09-27 05:57:55,447 >> Model config Qwen2Config {\n", " \"architectures\": [\n", " \"Qwen2ForCausalLM\"\n", " ],\n", " \"attention_dropout\": 0.0,\n", " \"bos_token_id\": 151643,\n", " \"dtype\": \"bfloat16\",\n", " \"eos_token_id\": 151645,\n", " \"hidden_act\": \"silu\",\n", " \"hidden_size\": 1536,\n", " \"initializer_range\": 0.02,\n", " \"intermediate_size\": 8960,\n", " \"layer_types\": [\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\"\n", " ],\n", " \"max_position_embeddings\": 32768,\n", " \"max_window_layers\": 21,\n", " \"model_type\": \"qwen2\",\n", " \"num_attention_heads\": 12,\n", " \"num_hidden_layers\": 28,\n", " \"num_key_value_heads\": 2,\n", " \"pad_token_id\": null,\n", " \"rms_norm_eps\": 1e-06,\n", " \"rope_parameters\": {\n", " \"rope_theta\": 1000000.0,\n", " \"rope_type\": \"default\"\n", " },\n", " \"sliding_window\": null,\n", " \"tie_word_embeddings\": true,\n", " \"transformers_version\": \"5.8.0\",\n", " \"use_cache\": true,\n", " \"use_sliding_window\": false,\n", " \"vocab_size\": 151936\n", "}\n", "\n", "[INFO|configuration_utils.py:780] 2026-09-27 05:57:55,549 >> loading configuration file config.json from cache at /root/.cache/huggingface/hub/models--Qwen--Qwen2.5-1.5B-Instruct/snapshots/989aa7980e4cf806f80c7fef2b1adb7bc71aa306/config.json\n", "[INFO|configuration_utils.py:856] 2026-09-27 05:57:55,551 >> Model config Qwen2Config {\n", " \"architectures\": [\n", " \"Qwen2ForCausalLM\"\n", " ],\n", " \"attention_dropout\": 0.0,\n", " \"bos_token_id\": 151643,\n", " \"dtype\": \"bfloat16\",\n", " \"eos_token_id\": 151645,\n", " \"hidden_act\": \"silu\",\n", " \"hidden_size\": 1536,\n", " \"initializer_range\": 0.02,\n", " \"intermediate_size\": 8960,\n", " \"layer_types\": [\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\"\n", " ],\n", " \"max_position_embeddings\": 32768,\n", " \"max_window_layers\": 21,\n", " \"model_type\": \"qwen2\",\n", " \"num_attention_heads\": 12,\n", " \"num_hidden_layers\": 28,\n", " \"num_key_value_heads\": 2,\n", " \"pad_token_id\": null,\n", " \"rms_norm_eps\": 1e-06,\n", " \"rope_parameters\": {\n", " \"rope_theta\": 1000000.0,\n", " \"rope_type\": \"default\"\n", " },\n", " \"sliding_window\": null,\n", " \"tie_word_embeddings\": true,\n", " \"transformers_version\": \"5.8.0\",\n", " \"use_cache\": true,\n", " \"use_sliding_window\": false,\n", " \"vocab_size\": 151936\n", "}\n", "\n", "[INFO|configuration_utils.py:780] 2026-09-27 05:57:56,522 >> loading configuration file config.json from cache at /root/.cache/huggingface/hub/models--Qwen--Qwen2.5-1.5B-Instruct/snapshots/989aa7980e4cf806f80c7fef2b1adb7bc71aa306/config.json\n", "[INFO|configuration_utils.py:856] 2026-09-27 05:57:56,524 >> Model config Qwen2Config {\n", " \"architectures\": [\n", " \"Qwen2ForCausalLM\"\n", " ],\n", " \"attention_dropout\": 0.0,\n", " \"bos_token_id\": 151643,\n", " \"dtype\": \"bfloat16\",\n", " \"eos_token_id\": 151645,\n", " \"hidden_act\": \"silu\",\n", " \"hidden_size\": 1536,\n", " \"initializer_range\": 0.02,\n", " \"intermediate_size\": 8960,\n", " \"layer_types\": [\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\",\n", " \"full_attention\"\n", " ],\n", " \"max_position_embeddings\": 32768,\n", " \"max_window_layers\": 21,\n", " \"model_type\": \"qwen2\",\n", " \"num_attention_heads\": 12,\n", " \"num_hidden_layers\": 28,\n", " \"num_key_value_heads\": 2,\n", " \"pad_token_id\": null,\n", " \"rms_norm_eps\": 1e-06,\n", " \"rope_parameters\": {\n", " \"rope_theta\": 1000000.0,\n", " \"rope_type\": \"default\"\n", " },\n", " \"sliding_window\": null,\n", " \"tie_word_embeddings\": true,\n", " \"transformers_version\": \"5.8.0\",\n", " \"use_cache\": true,\n", " \"use_sliding_window\": false,\n", " \"vocab_size\": 151936\n", "}\n", "\n", "[WARNING|logging.py:340] 2026-09-27 05:57:56,525 >> `torch_dtype` is deprecated! Use `dtype` instead!\n", "[INFO|2026-09-27 05:57:56] llamafactory.model.model_utils.kv_cache:144 >> KV cache is enabled for faster generation.\n", "[INFO|modeling_utils.py:769] 2026-09-27 05:57:57,709 >> loading weights file model.safetensors from cache at /root/.cache/huggingface/hub/models--Qwen--Qwen2.5-1.5B-Instruct/snapshots/989aa7980e4cf806f80c7fef2b1adb7bc71aa306/model.safetensors\n", "[INFO|modeling_utils.py:839] 2026-09-27 05:57:57,709 >> Will use dtype=torch.bfloat16 as defined in model's config object\n", "[INFO|configuration_utils.py:1086] 2026-09-27 05:57:57,710 >> Generate config GenerationConfig {\n", " \"bos_token_id\": 151643,\n", " \"eos_token_id\": 151645,\n", " \"output_attentions\": false,\n", " \"output_hidden_states\": false,\n", " \"use_cache\": true\n", "}\n", "\n", "Loading weights: 100% 338/338 [00:00<00:00, 5066.63it/s]\n", "[INFO|configuration_utils.py:1039] 2026-09-27 05:58:09,123 >> loading configuration file generation_config.json from cache at /root/.cache/huggingface/hub/models--Qwen--Qwen2.5-1.5B-Instruct/snapshots/989aa7980e4cf806f80c7fef2b1adb7bc71aa306/generation_config.json\n", "[INFO|configuration_utils.py:1086] 2026-09-27 05:58:09,123 >> Generate config GenerationConfig {\n", " \"bos_token_id\": 151643,\n", " \"do_sample\": true,\n", " \"eos_token_id\": [\n", " 151645,\n", " 151643\n", " ],\n", " \"pad_token_id\": 151643,\n", " \"repetition_penalty\": 1.1,\n", " \"temperature\": 0.7,\n", " \"top_k\": 20,\n", " \"top_p\": 0.8\n", "}\n", "\n", "[INFO|2026-09-27 05:58:09] llamafactory.model.model_utils.attention:144 >> Using torch SDPA for faster training and inference.\n", "[INFO|2026-09-27 05:58:27] llamafactory.model.adapter:144 >> Merged 1 adapter(s).\n", "[INFO|2026-09-27 05:58:27] llamafactory.model.adapter:144 >> Loaded adapter(s): saves/Qwen2.5-1.5B/lora/dpo\n", "[INFO|2026-09-27 05:58:27] llamafactory.model.loader:144 >> all params: 1,543,714,304\n", "[INFO|2026-09-27 05:58:27] llamafactory.train.tuner:144 >> Convert model dtype to: torch.bfloat16.\n", "[INFO|configuration_utils.py:542] 2026-09-27 05:58:27,239 >> Configuration saved in merged_models/Qwen2.5-1.5B-Financial-Code-Engine/config.json\n", "[INFO|configuration_utils.py:875] 2026-09-27 05:58:27,240 >> Configuration saved in merged_models/Qwen2.5-1.5B-Financial-Code-Engine/generation_config.json\n", "Writing model shards: 100% 2/2 [00:38<00:00, 19.43s/it]\n", "[INFO|modeling_utils.py:3509] 2026-09-27 05:59:06,146 >> The model is bigger than the maximum size per checkpoint (2GB) and is going to be split in 2 checkpoint shards. You can find where each parameters has been saved in the index located at merged_models/Qwen2.5-1.5B-Financial-Code-Engine/model.safetensors.index.json.\n", "[INFO|tokenization_utils_base.py:3281] 2026-09-27 05:59:06,147 >> chat template saved in merged_models/Qwen2.5-1.5B-Financial-Code-Engine/chat_template.jinja\n", "[INFO|tokenization_utils_base.py:2114] 2026-09-27 05:59:06,148 >> tokenizer config file saved in merged_models/Qwen2.5-1.5B-Financial-Code-Engine/tokenizer_config.json\n", "[INFO|2026-09-27 05:59:06] llamafactory.train.tuner:144 >> Ollama modelfile saved in merged_models/Qwen2.5-1.5B-Financial-Code-Engine/Modelfile\n" ] } ] }, { "cell_type": "markdown", "source": [ "# Upload Model & Tokenizer to Hugging Face Hub" ], "metadata": { "id": "W7dMG_UDcl6l" } }, { "cell_type": "code", "source": [ "from transformers import AutoModelForCausalLM, AutoTokenizer\n", "from huggingface_hub import login\n", "from google.colab import userdata\n", "\n", "hf_token = userdata.get('HF_TOKEN')\n", "login(token=hf_token)\n", "\n", "hf_repo_id = \"ahmadnawaz21/Qwen2.5-1.5B-Financial-Code-Engine\"\n", "export_dir = \"merged_models/Qwen2.5-1.5B-Financial-Code-Engine\"\n", "\n", "print(\"Loading merged model for upload...\")\n", "model = AutoModelForCausalLM.from_pretrained(export_dir, torch_dtype=\"auto\")\n", "tokenizer = AutoTokenizer.from_pretrained(export_dir)\n", "\n", "print(f\"Uploading model weights to Hugging Face: {hf_repo_id}...\")\n", "model.push_to_hub(hf_repo_id, token=hf_token)\n", "tokenizer.push_to_hub(hf_repo_id, token=hf_token)\n", "\n", "print(f\"\\nModel successfully published to: https://huggingface.co/{hf_repo_id}\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 377, "referenced_widgets": [ "126e85056ecb4287a6c690d6ff289bc2", "d5303a8403bb481c886d968de090be98", "d01ef800596d436180a87055d5db612d", "82f87fca525344dd9b176675fa2b35bc", "dd124b28b1ce487f8c3d84ee49a6d9c0", "885ab2cb172e4a49bb091089b9de9229", "fa00ca1d7bb241b9af97a0b4b4488b5c", "d8a26717680d45f8b0c687c56f897881", "60f0caceae90413592f8f2d991a4d9fc", "266bed38efc64c6bb468e8f4779a7a19", "2316b56cfbae4a0e80a77f986ec9dc49", "977a3baa072c430cbb9e46d9bc75efec", "470e0a1b096f4a03b67cc14c7374f79f", "e95e5025e20149b1b1d759c4de487a66", "73ff193cacb14c5ba15e00ea7ddd635d", "e7d28a63d36b4c289bef94322e05e8e5", "15f099246c544943b1025c2454754cce", "4578e42b16344aefb38bf447ded76932", "33c0c821592249108aca19b08b24cffd", "cf593e69ebae494ba00fa867d65a1a52", "bf3770a7c4a940879898cc5f09fd0d47", "54cdc864c43b4a24b3790fc24af122d4", "1dab9ec343ad4605a3ed8062208b8a52", "171314f6fa7245889e3586610ed3888e", "6a0de7e5f0a643698bece101c3f05663", "7ecf5118032a4283bcb26e6365ff92a0", "ca983ca787634a6f9af8091fec6374df", "58b9358317e849ce9f748c31e9d9eea4", "ee243142fa654dbc82399189da73d429", "fb7cfed166d244e58e3925a7d3bf9c9c", "d42b16cbaf2244198f347237b2835090", "e67d4361d8c040bbaac4d2e6f4f52200", "cde0431552724242a954af47d57eea73", "d45ea957a98240d8981e4804372adda1", "530bf101353645e882cfde5d44ef9654", "e49027cf19be44afab4623b37b522882", "e048adf2236344ee8babf62fa79b3bf3", "e9f44682323343cba3362adb35e551fd", "75e596a65fa24ebcbd1eeb576ccc09f4", "4557fa6fbfbc4521910fb9d5579334d0", "1b07420e194547c8b93e34b13440e3c0", "6f1d0660ca2d4e719a8808955a8727b5", "9632427829be4e0f97981063051e25c0", "f0a1bfbd1cf146b2be6241dc2cede8d0", "d1049400e6074f0083af10b7de3fd81e", "a5d4e41adca84bedaa8761af765219db", "391cf71467d54528bcff4a7046bec890", "362b54d9983b44a3b29750c2b479dfc0", "3516cfd1679b4270b5b3e93a1e7452af", "c2d5721969484d86b9b7ef0ceccfd0a2", "7644a5f9545f4785b92d5094696b2564", "f3fe5f77405a481fb05e8cc7f097f09a", "5f452f32383f4f3c95311b33c8837d38", "8cf973ea1e0645c3a17c3a8335f46343", "aa512f8ca2c948d89cc745297bb83cf2", "9253c6790448425f952182f7ca966c48", "375258e7818d479ebbd07200612fbd7e", "3eec279649d245b783f295bc4090f5e6", "a1377de3e2b4407aaec03edb2e87ed60", "c31511b73e154c5ea7dce02c051277c9", "db70fef5067f4542b59ae9c71bd16e5f", "29860d4f1b6d45479995a7a9283f4520", "550eff5c2874460587a49910eb348795", "7bf4111351dd4b83a93bb99f1369844a", "478f7b69f8634904b51582ca9c4e7865", "4ce884f813b44c6f85385bb9b5eaabec", "c0eb2bd390b74510b5f674a88df7a751", "0ee4805247b64900af0535265f04311c", "fb23b49ec322454389702a412f0bb64f", "d0ab786918f145daa758e930f15409e7", "b08b2347e2de45e081b612d6374b3ed4", "5b58e67cb24044cca51217d3aa4593e9", "f831701948d84d6fa4f0e5ff9b098e56", "c9af3bdaffda48da81d06159e44eebc7", "9c136b1357764d348ffed12a3eead5c8", "e87265c0d9144bc6a2956d6d998f20dc", "c083b9297b184848a3692fede197f978", "9f14a410de8d4296914a907eeffab51b", "289480811da74c25826b030f3701e60d", "b1628bba03d047c18bfc82839d9c66c8", "d21997be6e084299aaa463f28a86c506", "9cca6b7cd2e74abfaf018da96dec6eb5", "5e14d1992de04ff4bf9af6a5dd81c869", "da293f3f0e73492b93d2ac71e2d838d5", "79d2fb9692d242bc9b2ea1e874f7820f", "fd2911d4adee4916b65127f57452b305", "4b6f784dc4fb4bbda463b3d8394ca50f", "bd6ace7d8f1343998317bd8cbde0deae", "9cc9a6332c5a465c85632b0b93823f3e", "e0d7a0ef2f3e437392afefb7fabcc382", "17c63b3379ad4a8eb8bb0ce8b550af8c", "0c682d948616466cb449e24b473c5cfc", "2f5591b25bd8417f9dd6e7186cd5844c", "cc6fee30ee0247aab03322b5f4f6c480", "b69c87e83b0e4346be003760add49e4c", "3d40e90d1fa4496ca35a4efbe6551bf9", "dca992a5d4bd4356ae1a439fa0068095", "a94f4aefd5f14d5796df11cd6c82be36", "26d1ac6703d745288f7d1d4b5b3acdae" ] }, "id": "sGNU5Mopcnqs", "outputId": "b113888e-4fdd-4c18-d9a5-0b24fb2027b3" }, "execution_count": 19, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Loading merged model for upload...\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "Loading weights: 0%| | 0/338 [00:00system\\nYou are an expert financial and software engineering assistant.<|im_end|>\\n<|im_start|>user\\nWrite a Python function to calculate the Net Present Value (NPV) given a discount rate and a list of cash flows.<|im_end|>\\n<|im_start|>assistant\\n\"\n", "\n", "output = pipe(test_prompt, max_new_tokens=256, do_sample=True, temperature=0.7)\n", "print(output[0][\"generated_text\"])" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 633, "referenced_widgets": [ "9e3040746f2d4ca6b65fa4f6d320abc7", "d83b519ac08449ed98c1c534636b60e7", "1b67ffe059644bed921405f037a099a6", "c716ad195c7f4f709078dfecb97dfd21", "a2e1e1245881483bbfea6165bde04dea", "f87e9a12a94b44c7b2930b61708fe833", "1228163b07964bdb94eab339c3ad78ff", "2e33c4a7b4884b93888107560ee9f4be", "e05c8e12a6a043b0b8a318dab0e7f421", "dd63178b31d14ea8bc1146db9abf6de9", "5dc3915e72b84440aa4c2ff4377972fa", "55c89e2c21e348329e4f57b4fdceb4b4", "98f38cbe140844c6b56455e6fd93ca07", "52616a4b3bd24b55854ec15d3991582b", "75274906c06a49d79d7e182b9cc6c277", "e14962b43bde464482f6480aef712e6a", "fda3b4507988490488d0ed6d182925b5", "dac1bc96250d49b08921c5501a0a58fd", "b7ce76eabae64282bda17aad2442c0e1", "c6c38fa67f534deb853b2e4278d870e0", "8b03d868231045e8bc902e9d685e08f4", "136cf2f79685490090133b96c4ef5227", "4c636c916dcc43c39280d1bdd1db3803", "cb51cbecad0745329d483ab83b6f1048", "332933da8b3542059cf1892661fd1c2c", "c0900621cd9248ff991ee332944fe064", "db622dfaaa5a4c85857093357dfd058b", "f845bd420ed5499b85313ebd7a2ecf3a", "b77c9267650c42a68f2e857360b31e80", "21b339096be2452b8fe5c7325bbcc7e2", "fcb3035d87b2480682c7daba9deeb120", "f66c76a42b1e4c4dbebc80669d4ff645", "cd19589e197d407b9a1b71eb4d577d87", "6222d024b004483c84842a2897c6f105", "04b7252b5c89489db05f64b8889f7d23", "834992cd2f184bd6b56cfee6be6c9705", "a197fe2131b4488d93551ae2845560bc", "05c8672d50ea412bbec9b6725b70646b", "247da9b73a3644d28179fc4ccb4b0a80", "fd9389231ecc4994b2092bec67dbf208", "ff8c748224cc48e183158492ca26b56c", "5903e3daad264c948800416f24e037b1", "602be541b76d48dfad93906fd34abbd2", "2895e2bcde9a4949a0da26908c7d2c79", "e593c3182e71470c9d2bc20c66ebcf46", "77a3197ba52d4d88a65bdc786cd41992", "77afe21ee19945c49a7d948e5410760b", "24b4da45e411418aa373ac45838155ba", "20d21ec86f3b4692b85c8492d621cc02", "b75651b9290544cca4217312db07be5b", "e18eac5273874be3aa6f2df7d10a6797", "98999e4b1dba4cfeb05046caf37a55ef", "e0b2e944d27a4468a93fdffd84f696bc", "287648439fe54e468c220b3f5e8ec89f", "bb0004218f5647dd856b1e6e7de18587", "54bc35c668c9465182d3b257fc64306a", "10aead25d8a54503a452118d17120a2c", "2119d477c4084ae4a07d9a71a1a259d8", "260c6385b00d42c2959c510e765156fe", "c211f8ef9e36435794928e3398d85d06", "d342e986a25540f59d2a79fc893ce241", "6a4b1753c1ee4233b31e654a0e216fd3", "abad7e881aad4b91bb11b3ac11a601ad", "4a7eea4053d74db982197c4dc57cde18", "39c00e184cff4eb59c6447e49f802eae", "aad8fb1aae4145e895111f88e52b6724", "4e62e18a68574e83a408a36350534521", "142607119a1c462fa973635d6c4af3a9", "931690df5b5a4a8c95cd7c7b07230d3d", "952b98d122fc4576a65b275b53659cd2", "32c185e43759455a9a40742adc013186", "272e464375b04f3f92e8536fce92cbb1", "1217efab817a496ea81e316c9fc0a355", "9f261f00233944039c467b5b2eca4160", "4e2b7b24c4b848c88493940eb7a4e66d", "a840532607bc489fb5ce267d6a1d5fb0", "0b8e27f57118474c9751a6fa1711d5d4", "649c3bfcaad0410b9142a9802578c0b7", "1fb7c64653c846bcbdeb7d08119119e1", "9f4238ab68384856bff48cdf6e73e467", "e5176fb68f704ac88c6bbc2633b82a16", "a20c3370ba9c43ec88e01524258803b2", "53f6e5111421455ead9081df3d4dd7bd", "7dc613071c7b43edb5afbc0fd1b80074", "ce297235b8294a69b7e7d7924ee71b5b", "cf2d2ec7c62244cfad36f2618adbe6d6", "5911de91133042b79ade273101bf09f9", "27903067fc2c461aa6a06667ec75b63c", "f24d87858697479d9521f870804a02e7", "68e15867fda94676873973b34822acf2", "13f2f627a8874623b154fe4ad82a6b2e", "7dce0f9110994c7a904c45bea4b3a0c4", "f42d7a130ade49adb8d46ccf991478fa", "58461bb0c2b94c4195bc7753f74401d8", "d84168ec969045a5883be162585da658", "4444a5fea9a44114a819ea132bb334bb", "cb92ec92b49540f8b363881b1381802e", "b98d5615cfc04f18a58e5ec8a0b9c417", "b00b96544137470d8acd47175417c95e" ] }, "id": "h3-tO0i7duNP", "outputId": "867889d3-206b-41c0-90c1-7daf58ec6f1b" }, "execution_count": 20, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Initializing inference pipeline from Hugging Face Hub...\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "config.json: 0%| | 0.00/1.37k [00:00system\n", "You are an expert financial and software engineering assistant.<|im_end|>\n", "<|im_start|>user\n", "Write a Python function to calculate the Net Present Value (NPV) given a discount rate and a list of cash flows.<|im_end|>\n", "<|im_start|>assistant\n", "```python\n", "def calculate_NPV(discount_rate, cash_flows):\n", " if len(cash_flows) == 0:\n", " return 0\n", " else:\n", " npv = sum([cash_flow / ((1 + discount_rate)**i) for i, cash_flow in enumerate(cash_flows)])\n", " return npv\n", "```\n" ] } ] } ] }