Isekai-Qwen/recipes/finetune/deepspeed/finetune_fullparameter_single_gpu.ipynb

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    "# Fine-Tuning Qwen-Chat Large Language Model (Single GPU)\n",
    "\n",
    "Tongyi Qianwen is a large language model developed by Alibaba Cloud based on the Transformer architecture, trained on an extensive set of pre-training data. The pre-training data is diverse and covers a wide range, including a large amount of internet text, specialized books, code, etc. In addition, an AI assistant called Qwen-Chat has been created based on the pre-trained model using alignment mechanism.\n",
    "\n",
    "This notebook uses Qwen-1.8B-Chat as an example to introduce how to fine-tune the Qianwen model using Deepspeed.\n",
    "\n",
    "## Environment Requirements\n",
    "\n",
    "Please refer to **requirements.txt** to install the required dependencies.\n",
    "\n",
    "## Preparation\n",
    "\n",
    "### Download Qwen-1.8B-Chat\n",
    "\n",
    "First, download the model files. You can choose to download directly from ModelScope."
   ]
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   "source": [
    "from modelscope.hub.snapshot_download import snapshot_download\n",
    "model_dir = snapshot_download('Qwen/Qwen-1_8B-Chat', cache_dir='.', revision='master')"
   ]
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    "### Download Example Training Data\n",
    "\n",
    "Download the data required for training; here, we provide a tiny dataset as an example. It is sampled from [Belle](https://github.com/LianjiaTech/BELLE).\n",
    "\n",
    "Disclaimer: the dataset can be only used for the research purpose."
   ]
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   "source": [
    "!wget https://atp-modelzoo-sh.oss-cn-shanghai.aliyuncs.com/release/tutorials/qwen_recipes/Belle_sampled_qwen.json"
   ]
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    "You can also refer to this format to prepare the dataset. Below is a simple example list with 1 sample:\n",
    "\n",
    "```json\n",
    "[\n",
    "  {\n",
    "    \"id\": \"identity_0\",\n",
    "    \"conversations\": [\n",
    "      {\n",
    "        \"from\": \"user\",\n",
    "        \"value\": \"你好\"\n",
    "      },\n",
    "      {\n",
    "        \"from\": \"assistant\",\n",
    "        \"value\": \"我是一个语言模型，我叫通义千问。\"\n",
    "      }\n",
    "    ]\n",
    "  }\n",
    "]\n",
    "```\n",
    "\n",
    "You can also use multi-turn conversations as the training set. Here is a simple example:\n",
    "\n",
    "```json\n",
    "[\n",
    "  {\n",
    "    \"id\": \"identity_0\",\n",
    "    \"conversations\": [\n",
    "      {\n",
    "        \"from\": \"user\",\n",
    "        \"value\": \"你好，能告诉我遛狗的最佳时间吗？\"\n",
    "      },\n",
    "      {\n",
    "        \"from\": \"assistant\",\n",
    "        \"value\": \"当地最佳遛狗时间因地域差异而异，请问您所在的城市是哪里？\"\n",
    "      },\n",
    "      {\n",
    "        \"from\": \"user\",\n",
    "        \"value\": \"我在纽约市。\"\n",
    "      },\n",
    "      {\n",
    "        \"from\": \"assistant\",\n",
    "        \"value\": \"纽约市的遛狗最佳时间通常在早晨6点至8点和晚上8点至10点之间，因为这些时间段气温较低，遛狗更加舒适。但具体时间还需根据气候、气温和季节变化而定。\"\n",
    "      }\n",
    "    ]\n",
    "  }\n",
    "]\n",
    "```\n",
    "\n",
    "\n",
    "## Fine-Tune the Model\n",
    "\n",
    "You can directly run the prepared training script to fine-tune the model."
   ]
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   "source": [
    "!python ../../finetune.py \\\n",
    "    --model_name_or_path \"Qwen/Qwen-1_8B-Chat/\"\\\n",
    "    --data_path  \"Belle_sampled_qwen.json\"\\\n",
    "    --bf16 \\\n",
    "    --output_dir \"output_qwen\" \\\n",
    "    --num_train_epochs 5 \\\n",
    "    --per_device_train_batch_size 1 \\\n",
    "    --per_device_eval_batch_size 1 \\\n",
    "    --gradient_accumulation_steps 16 \\\n",
    "    --evaluation_strategy \"no\" \\\n",
    "    --save_strategy \"steps\" \\\n",
    "    --save_steps 1000 \\\n",
    "    --save_total_limit 10 \\\n",
    "    --learning_rate 1e-5 \\\n",
    "    --weight_decay 0.1 \\\n",
    "    --adam_beta2 0.95 \\\n",
    "    --warmup_ratio 0.01 \\\n",
    "    --lr_scheduler_type \"cosine\" \\\n",
    "    --logging_steps 1 \\\n",
    "    --report_to \"none\" \\\n",
    "    --model_max_length 512 \\\n",
    "    --gradient_checkpointing \\\n",
    "    --lazy_preprocess"
   ]
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   "source": [
    "## Test the Model\n",
    "\n",
    "We can test the model as follows:"
   ]
  },
  {
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   "metadata": {},
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   "source": [
    "from transformers import AutoModelForCausalLM, AutoTokenizer\n",
    "from transformers.generation import GenerationConfig\n",
    "\n",
    "tokenizer = AutoTokenizer.from_pretrained(\"output_qwen\", trust_remote_code=True)\n",
    "model = AutoModelForCausalLM.from_pretrained(\n",
    "    \"output_qwen\",\n",
    "    device_map=\"auto\",\n",
    "    trust_remote_code=True\n",
    ").eval()\n",
    "\n",
    "response, history = model.chat(tokenizer, \"你好\", history=None)\n",
    "print(response)"
   ]
  }
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