tutorial/notebook/Gathnex_Mistral_7B_fine_tuning_Guide.ipynb

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            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
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            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m298.4/298.4 kB\u001b[0m \u001b[31m28.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m76.0/76.0 kB\u001b[0m \u001b[31m8.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Building wheel for ffmpy (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Building wheel for pathtools (setup.py) ... \u001b[?25l\u001b[?25hdone\n"
          ]
        }
      ],
      "source": [
        "!pip install -q -U bitsandbytes\n",
        "!pip install -q -U git+https://github.com/huggingface/transformers.git\n",
        "!pip install -q -U git+https://github.com/huggingface/peft.git\n",
        "!pip install -q -U git+https://github.com/huggingface/accelerate.git\n",
        "!pip install -q trl xformers wandb datasets einops gradio sentencepiece"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig,HfArgumentParser,TrainingArguments,pipeline, logging, TextStreamer\n",
        "from peft import LoraConfig, PeftModel, prepare_model_for_kbit_training, get_peft_model\n",
        "import os,torch, wandb, platform, gradio, warnings\n",
        "from datasets import load_dataset\n",
        "from trl import SFTTrainer\n",
        "from huggingface_hub import notebook_login\n",
        "base_model, dataset_name, new_model = \"mistralai/Mistral-7B-v0.1\" , \"gathnex/Gath_baize\", \"gathnex/Gath_mistral_7b\""
      ],
      "metadata": {
        "id": "VLzgZ14X_rMs"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Importing the dataset\n",
        "dataset = load_dataset(dataset_name, split=\"train\")\n",
        "dataset[\"chat_sample\"][0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
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        "outputId": "b98544e4-1f5f-4d60-b417-ac5e835452fb"
      },
      "execution_count": null,
      "outputs": [
        {
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          "data": {
            "text/plain": [
              "Downloading readme:   0%|          | 0.00/3.37k [00:00<?, ?B/s]"
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        {
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          "data": {
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          "data": {
            "text/plain": [
              "Generating train split:   0%|          | 0/52002 [00:00<?, ? examples/s]"
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        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'Below is an instruction that describes a task. Write a response that appropriately completes the request.\\n\\n### Instruction:\\nGive three tips for staying healthy.\\n\\n### Response:\\n1. Eat a balanced and nutritious diet: Make sure your meals are inclusive of a variety of fruits and vegetables, lean protein, whole grains, and healthy fats. This helps to provide your body with the essential nutrients to function at its best and can help prevent chronic diseases.\\n\\n2. Engage in regular physical activity: Exercise is crucial for maintaining strong bones, muscles, and cardiovascular health. Aim for at least 150 minutes of moderate aerobic exercise or 75 minutes of vigorous exercise each week.\\n\\n3. Get enough sleep: Getting enough quality sleep is crucial for physical and mental well-being. It helps to regulate mood, improve cognitive function, and supports healthy growth and immune function. Aim for 7-9 hours of sleep each night.'"
            ],
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          "metadata": {},
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    },
    {
      "cell_type": "code",
      "source": [
        "# Load base model(Mistral 7B)\n",
        "bnb_config = BitsAndBytesConfig(\n",
        "    load_in_4bit= True,\n",
        "    bnb_4bit_quant_type= \"nf4\",\n",
        "    bnb_4bit_compute_dtype= torch.bfloat16,\n",
        "    bnb_4bit_use_double_quant= False,\n",
        ")\n",
        "model = AutoModelForCausalLM.from_pretrained(\n",
        "   base_model,\n",
        "    quantization_config=bnb_config,\n",
        "    device_map={\"\": 0}\n",
        ")\n",
        "model.config.use_cache = False # silence the warnings. Please re-enable for inference!\n",
        "model.config.pretraining_tp = 1\n",
        "model.gradient_checkpointing_enable()\n",
        "# Load tokenizer\n",
        "tokenizer = AutoTokenizer.from_pretrained(base_model, trust_remote_code=True)\n",
        "tokenizer.padding_side = 'right'\n",
        "tokenizer.pad_token = tokenizer.eos_token\n",
        "tokenizer.add_eos_token = True\n",
        "tokenizer.add_bos_token, tokenizer.add_eos_token\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
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        "id": "StJKGiDDHzdk",
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        {
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          "data": {
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            ],
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          "metadata": {}
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Adding the adapters in the layers\n",
        "model = prepare_model_for_kbit_training(model)\n",
        "peft_config = LoraConfig(\n",
        "        r=16,\n",
        "        lora_alpha=16,\n",
        "        lora_dropout=0.05,\n",
        "        bias=\"none\",\n",
        "        task_type=\"CAUSAL_LM\",\n",
        "        target_modules=[\"q_proj\", \"k_proj\", \"v_proj\", \"o_proj\",\"gate_proj\"]\n",
        "    )\n",
        "model = get_peft_model(model, peft_config)"
      ],
      "metadata": {
        "id": "DZoClwHcPTqQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Monitering the LLM\n",
        "wandb.login(key = \"Wandb authorization key\")\n",
        "run = wandb.init(project='Fine tuning mistral 7B', job_type=\"training\", anonymous=\"allow\")"
      ],
      "metadata": {
        "id": "na9CAoHC5gM9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Hyperparamter\n",
        "training_arguments = TrainingArguments(\n",
        "    output_dir= \"./results\",\n",
        "    num_train_epochs= 2,\n",
        "    per_device_train_batch_size= 8,\n",
        "    gradient_accumulation_steps= 2,\n",
        "    optim = \"paged_adamw_8bit\",\n",
        "    save_steps= 1000,\n",
        "    logging_steps= 30,\n",
        "    learning_rate= 2e-4,\n",
        "    weight_decay= 0.001,\n",
        "    fp16= False,\n",
        "    bf16= False,\n",
        "    max_grad_norm= 0.3,\n",
        "    max_steps= -1,\n",
        "    warmup_ratio= 0.3,\n",
        "    group_by_length= True,\n",
        "    lr_scheduler_type= \"constant\",\n",
        "    report_to=\"wandb\"\n",
        ")\n",
        "# Setting sft parameters\n",
        "trainer = SFTTrainer(\n",
        "    model=model,\n",
        "    train_dataset=dataset,\n",
        "    peft_config=peft_config,\n",
        "    max_seq_length= None,\n",
        "    dataset_text_field=\"text\",\n",
        "    tokenizer=tokenizer,\n",
        "    args=training_arguments,\n",
        "    packing= False,\n",
        ")"
      ],
      "metadata": {
        "id": "peOnLAAhS0y1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Save the fine-tuned model\n",
        "trainer.model.save_pretrained(new_model)\n",
        "wandb.finish()\n",
        "model.config.use_cache = True\n",
        "model.eval()"
      ],
      "metadata": {
        "id": "nKgZBEGVS5a2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def stream(user_prompt):\n",
        "    runtimeFlag = \"cuda:0\"\n",
        "    system_prompt = 'The conversation between Human and AI assisatance named Gathnex\\n'\n",
        "    B_INST, E_INST = \"[INST]\", \"[/INST]\"\n",
        "\n",
        "    prompt = f\"{system_prompt}{B_INST}{user_prompt.strip()}\\n{E_INST}\"\n",
        "\n",
        "    inputs = tokenizer([prompt], return_tensors=\"pt\").to(runtimeFlag)\n",
        "\n",
        "    streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)\n",
        "\n",
        "    _ = model.generate(**inputs, streamer=streamer, max_new_tokens=200)"
      ],
      "metadata": {
        "id": "L7vHP41ITQPb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "stream(\"what is newtons 2rd law and its formula\")"
      ],
      "metadata": {
        "id": "V6seEpq-TQXg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Clear the memory footprint\n",
        "del model, trainer\n",
        "torch.cuda.empty_cache()\n",
        "\n",
        "# Reload the base model\n",
        "base_model_reload = AutoModelForCausalLM.from_pretrained(\n",
        "    base_model, low_cpu_mem_usage=True,\n",
        "    return_dict=True,torch_dtype=torch.bfloat16,\n",
        "    device_map= {\"\": 0})\n",
        "model = PeftModel.from_pretrained(base_model_reload, new_model)\n",
        "model = model.merge_and_unload()\n",
        "\n",
        "# Reload tokenizer\n",
        "tokenizer = AutoTokenizer.from_pretrained(base_model, trust_remote_code=True)\n",
        "tokenizer.pad_token = tokenizer.eos_token\n",
        "tokenizer.padding_side = \"right\""
      ],
      "metadata": {
        "id": "rKW0P0jgTWP1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#push the model to hub\n",
        "!huggingface-cli login\n",
        "model.push_to_hub(new_model, use_temp_dir=False)\n",
        "tokenizer.push_to_hub(new_model, use_temp_dir=False)"
      ],
      "metadata": {
        "id": "tNrpuLgBTYKk"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}