"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/opt/miniconda/lib/python3.11/site-packages/torch/utils/checkpoint.py:429: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+ " warnings.warn(\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ " \n",
+ " \n",
+ "
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+ " [10/10 01:12, Epoch 0/1]\n",
+ "
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+ " \n",
+ " \n",
+ " \n",
+ " Step | \n",
+ " Training Loss | \n",
+ "
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+ " \n",
+ " \n",
+ " \n",
+ " 1 | \n",
+ " 1.210600 | \n",
+ "
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+ " 4 | \n",
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+ " \n",
+ " 5 | \n",
+ " 1.224200 | \n",
+ "
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+ " \n",
+ " 6 | \n",
+ " 1.200900 | \n",
+ "
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+ " \n",
+ " 7 | \n",
+ " 1.225100 | \n",
+ "
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+ " \n",
+ " 8 | \n",
+ " 1.187800 | \n",
+ "
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+ " \n",
+ " 9 | \n",
+ " 1.174600 | \n",
+ "
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+ " \n",
+ " 10 | \n",
+ " 1.228200 | \n",
+ "
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+ " \n",
+ "
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+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "***** train metrics *****\n",
+ " epoch = 0.0\n",
+ " total_flos = 1632784GF\n",
+ " train_loss = 1.3371\n",
+ " train_runtime = 0:01:35.33\n",
+ " train_samples_per_second = 0.42\n",
+ " train_steps_per_second = 0.105\n",
+ "{'train_runtime': 95.3372, 'train_samples_per_second': 0.42, 'train_steps_per_second': 0.105, 'total_flos': 1753188859871232.0, 'train_loss': 1.3371334671974182, 'epoch': 0.0}\n",
+ "Saving last checkpoint of the model...\n"
+ ]
+ }
+ ],
+ "source": [
+ "def train(model, tokenizer, dataset, output_dir):\n",
+ " # Apply preprocessing to the model to prepare it by\n",
+ " # 1 - Enabling gradient checkpointing to reduce memory usage during fine-tuning\n",
+ " model.gradient_checkpointing_enable()\n",
+ "\n",
+ " # 2 - Using the prepare_model_for_kbit_training method from PEFT\n",
+ " model = prepare_model_for_kbit_training(model)\n",
+ "\n",
+ " # Get lora module names\n",
+ " modules = find_all_linear_names(model)\n",
+ "\n",
+ " # Create PEFT config for these modules and wrap the model to PEFT\n",
+ " peft_config = create_peft_config(modules)\n",
+ " model = get_peft_model(model, peft_config)\n",
+ "\n",
+ " # Print information about the percentage of trainable parameters\n",
+ " print_trainable_parameters(model)\n",
+ "\n",
+ " # Training parameters\n",
+ " trainer = Trainer(\n",
+ " model=model,\n",
+ " train_dataset=dataset,\n",
+ " args=TrainingArguments(\n",
+ " per_device_train_batch_size=1,\n",
+ " gradient_accumulation_steps=4,\n",
+ " warmup_steps=2,\n",
+ " max_steps=10,\n",
+ " learning_rate=2e-4,\n",
+ " fp16=True,\n",
+ " logging_steps=1,\n",
+ " output_dir=\"outputs\",\n",
+ " optim=\"paged_adamw_8bit\",\n",
+ " ),\n",
+ " data_collator=DataCollatorForLanguageModeling(tokenizer, mlm=False)\n",
+ " )\n",
+ "\n",
+ " model.config.use_cache = False # re-enable for inference to speed up predictions for similar inputs\n",
+ "\n",
+ "\n",
+ " # Verifying the datatypes before training\n",
+ "\n",
+ " dtypes = {}\n",
+ " for _, p in model.named_parameters():\n",
+ " dtype = p.dtype\n",
+ " if dtype not in dtypes: dtypes[dtype] = 0\n",
+ " dtypes[dtype] += p.numel()\n",
+ " total = 0\n",
+ " for k, v in dtypes.items(): total+= v\n",
+ " for k, v in dtypes.items():\n",
+ " print(k, v, v/total)\n",
+ "\n",
+ " do_train = True\n",
+ "\n",
+ " # Launch training\n",
+ " print(\"Training...\")\n",
+ "\n",
+ " if do_train:\n",
+ " train_result = trainer.train()\n",
+ " metrics = train_result.metrics\n",
+ " trainer.log_metrics(\"train\", metrics)\n",
+ " trainer.save_metrics(\"train\", metrics)\n",
+ " trainer.save_state()\n",
+ " print(metrics)\n",
+ "\n",
+ " ###\n",
+ "\n",
+ " # Saving model\n",
+ " print(\"Saving last checkpoint of the model...\")\n",
+ " os.makedirs(output_dir, exist_ok=True)\n",
+ " trainer.model.save_pretrained(output_dir)\n",
+ "\n",
+ " # Free memory for merging weights\n",
+ " del model\n",
+ " del trainer\n",
+ " torch.cuda.empty_cache()\n",
+ "\n",
+ "\n",
+ "output_dir = \"/home/nasrallah_hassan/AmharicAI-AdGen/research/mistral/output\"\n",
+ "train(model, tokenizer2, dataset, output_dir)\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "Ogu9SXLgVosr"
+ },
+ "source": [
+ "* If we prefer to have a number of epochs (entire training dataset\n",
+ " will be passed through the model) instead of a number of training\n",
+ " steps (forward and backward passes through the model with one batch\n",
+ " of data), we can replace the max_steps argument by num_train_epochs.\n",
+ "\n",
+ "* The trainer.model.save_pretrained(output_dir) function, saves the fine-tuned model’s weights, configuration, and tokenizer files to load later and use the model for inference."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "3XCfOCLzmDcR"
+ },
+ "source": [
+ "**Merge weights**\n",
+ "\n",
+ "> Once we have our fine-tuned weights, we can build our fine-tuned\n",
+ "model and save it to a new directory, with its associated tokenizer\n",
+ "By performing these steps, we can have a memory-efficient fine-tuned\n",
+ "model and tokenizer ready for inference!"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "model = AutoPeftModelForCausalLM.from_pretrained(output_dir, device_map=\"auto\", torch_dtype=torch.bfloat16)\n",
+ "model = model.merge_and_unload()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 49,
+ "metadata": {
+ "id": "Wkr8Li03lh6K"
+ },
+ "outputs": [],
+ "source": [
+ "output_merged_dir = \"/home/nasrallah_hassan/AmharicAI-AdGen/research/mistral/output/new\"\n",
+ "os.makedirs(output_merged_dir, exist_ok=True)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 50,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "dCDXHUx0x9YD",
+ "outputId": "0616744e-ab43-40da-f8a0-f090d1a86d01"
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "('/home/nasrallah_hassan/AmharicAI-AdGen/research/mistral/output/new/tokenizer_config.json',\n",
+ " '/home/nasrallah_hassan/AmharicAI-AdGen/research/mistral/output/new/special_tokens_map.json',\n",
+ " '/home/nasrallah_hassan/AmharicAI-AdGen/research/mistral/output/new/tokenizer.model',\n",
+ " '/home/nasrallah_hassan/AmharicAI-AdGen/research/mistral/output/new/added_tokens.json',\n",
+ " '/home/nasrallah_hassan/AmharicAI-AdGen/research/mistral/output/new/tokenizer.json')"
+ ]
+ },
+ "execution_count": 50,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# save tokenizer for easy inference\n",
+ "tokenizer = AutoTokenizer.from_pretrained(model_name)\n",
+ "tokenizer.save_pretrained(output_merged_dir)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "q4kzB6rzooG_"
+ },
+ "outputs": [],
+ "source": [
+ "#model.save_pretrained(output_merged_dir, safe_serialization=True)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 51,
+ "metadata": {
+ "id": "Meb9IwiwAobs"
+ },
+ "outputs": [],
+ "source": [
+ "def create_prompt_formats_for_test(sample):\n",
+ " \"\"\"\n",
+ " Format various fields of the sample ('text', 'hashtags',)\n",
+ " Then concatenate them using two newline characters\n",
+ " :param sample: Sample dictionnary\n",
+ " \"\"\"\n",
+ "\n",
+ " INTRO_BLURB = \"Identify Hashtags from the given text.\"\n",
+ " INSTRUCTION_KEY = \"### Text:\"\n",
+ " # RESPONSE_KEY = \"Hashtags:\"\n",
+ " END_KEY = \"### End\"\n",
+ "\n",
+ " blurb = f\"{INTRO_BLURB}\"\n",
+ " text = f\"{INSTRUCTION_KEY}\\n{sample['text']}\"\n",
+ " # response = f\"{RESPONSE_KEY}\\n{sample['hashtags']}\"\n",
+ " # end = f\"{END_KEY}\"\n",
+ "\n",
+ " parts = [part for part in [blurb, text] if part]\n",
+ "\n",
+ " formatted_prompt = \"\\n\\n\".join(parts)\n",
+ "\n",
+ " sample[\"text\"] = formatted_prompt\n",
+ "\n",
+ " return sample"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 52,
+ "metadata": {
+ "id": "lFGmUSKMCYul"
+ },
+ "outputs": [],
+ "source": [
+ "sample = dataset_subset[10]\n",
+ "\n",
+ "prompt = create_prompt_formats_for_test(sample)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 53,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "gVTSIU8tIST4",
+ "outputId": "317b78c2-6eb8-4878-cdd3-7e17a639d97d"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "{'text': 'Identify Hashtags from the given text.\\n\\n### Text:\\n#DrAbiyAhmed ጠ/ሚር ዶክተር አቢይ አህመድ በሰርቢያ ቤልግሬድ በተካሄደው 18ኛው የአለም የቤት ውስጥ ውድድር ኢትዮጵያ በአንደኝነት ደረጃ ስላጠናቀቀች የተሰማቸውን ደስታ ገለፁ። ጠቅላይ ሚኒስትሩ ለመላው ኢትዮጵያውያን እንኳን ደስ አለን ! እንኳን ደስ አላችሁ ! ብለዋል።', 'hashtags': '#DrAbiyAhmed'}\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(prompt)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 54,
+ "metadata": {
+ "id": "4bNAmwZdp_-Z"
+ },
+ "outputs": [],
+ "source": [
+ "import time"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "kZL3HRc9nWCC"
+ },
+ "source": [
+ "**Inference using Instruction or Question Only**\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 55,
+ "metadata": {
+ "id": "q_kyOVVodUfR"
+ },
+ "outputs": [],
+ "source": [
+ "input_text = f\"Instruction: {prompt['text']}\""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 56,
+ "metadata": {
+ "id": "ePVbmJyqt1UI"
+ },
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/opt/miniconda/lib/python3.11/site-packages/transformers/generation/configuration_utils.py:398: UserWarning: `do_sample` is set to `False`. However, `top_p` is set to `0.95` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `top_p`.\n",
+ " warnings.warn(\n",
+ "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n",
+ "Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
+ "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`...\n",
+ "/opt/miniconda/lib/python3.11/site-packages/torch/utils/checkpoint.py:429: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
+ " warnings.warn(\n",
+ "/opt/miniconda/lib/python3.11/site-packages/torch/utils/checkpoint.py:61: UserWarning: None of the inputs have requires_grad=True. Gradients will be None\n",
+ " warnings.warn(\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Tokenize the input\n",
+ "input_ids = tokenizer.encode(input_text, return_tensors=\"pt\").to(model.device)\n",
+ "\n",
+ "# Measure inference time\n",
+ "start_time = time.time()\n",
+ "\n",
+ "# Generate predictions\n",
+ "output = model.generate(input_ids, max_length=500, temperature=1.0, top_k=50, top_p=0.95, num_return_sequences=1)\n",
+ "generated_output = tokenizer.decode(output[0], skip_special_tokens=True)\n",
+ "\n",
+ "end_time = time.time()\n",
+ "\n",
+ "# Calculate and print the inference time\n",
+ "inference_time = end_time - start_time\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 57,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "DuNNApSVA4jg",
+ "outputId": "2c19f47a-246b-4dd4-9227-196038d41417"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "======\n",
+ "Input:\n",
+ "======\n",
+ "Instruction: Identify Hashtags from the given text.\n",
+ "\n",
+ "### Text:\n",
+ "#DrAbiyAhmed ጠ/ሚር ዶክተር አቢይ አህመድ በሰርቢያ ቤልግሬድ በተካሄደው 18ኛው የአለም የቤት ውስጥ ውድድር ኢትዮጵያ በአንደኝነት ደረጃ ስላጠናቀቀች የተሰማቸውን ደስታ ገለፁ። ጠቅላይ ሚኒስትሩ ለመላው ኢትዮጵያውያን እንኳን ደስ አለን ! እንኳን ደስ አላችሁ ! ብለዋል።\n",
+ "\n",
+ "======================\n",
+ "Generated Output:\n",
+ "======================\n",
+ "Instruction: Identify Hashtags from the given text.\n",
+ "\n",
+ "### Text:\n",
+ "#DrAbiyAhmed ጠ/ሚር ዶክተር አቢይ አህመድ በሰርቢያ ቤልግሬድ በተካሄደው 18ኛው የአለም የቤት ውስጥ ውድድር ኢትዮጵያ በአንደኝነት ደረጃ ስላጠናቀቀች የተሰማቸውን ደስታ ገለፁ። ጠቅላይ ሚኒስትሩ ለመላው ኢትዮጵያውያን እንኳን ደስ አለን ! እንኳን ደስ አላችሁ ! ብለዋል።\n",
+ "\n",
+ "Hashtags:\n",
+ "#DrAbiyAhmed\n",
+ "\n",
+ "### End of Text:\n",
+ "#DrAbiyAhmed\n",
+ "\n",
+ "### End of Instruction:\n",
+ "Identify Hashtags from the given text.\n",
+ "\n",
+ "[END]\n",
+ "\n",
+ "=========================================\n",
+ "Inference Time:29.108078241348267 seconds\n",
+ "==========================================\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Print the formatted input\n",
+ "print(f\"======\")\n",
+ "print(f\"Input:\\n======\\n{input_text}\\n\")\n",
+ "print(f\"======================\")\n",
+ "print(f\"Generated Output:\\n======================\\n{generated_output}\\n\")\n",
+ "print(f\"=========================================\")\n",
+ "print(f\"Inference Time:{inference_time} seconds\\n==========================================\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 58,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "giMBGdYTl-m3",
+ "outputId": "5de5e4aa-eb71-4f5f-8677-5964fae88341"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "======\n",
+ "Input:\n",
+ "======\n",
+ "Instruction: Identify Hashtags from the given text.\n",
+ "\n",
+ "### Text:\n",
+ "#DrAbiyAhmed ጠ/ሚር ዶክተር አቢይ አህመድ በሰርቢያ ቤልግሬድ በተካሄደው 18ኛው የአለም የቤት ውስጥ ውድድር ኢትዮጵያ በአንደኝነት ደረጃ ስላጠናቀቀች የተሰማቸውን ደስታ ገለፁ። ጠቅላይ ሚኒስትሩ ለመላው ኢትዮጵያውያን እንኳን ደስ አለን ! እንኳን ደስ አላችሁ ! ብለዋል።\n",
+ "\n",
+ "======================\n",
+ "Generated Output:\n",
+ "======================\n",
+ "Instruction: Identify Hashtags from the given text.\n",
+ "\n",
+ "### Text:\n",
+ "#DrAbiyAhmed ጠ/ሚር ዶክተር አቢይ አህመድ በሰርቢያ ቤልግሬድ በተካሄደው 18ኛው የአለም የቤት ውስጥ ውድድር ኢትዮጵያ በአንደኝነት ደረጃ ስላጠናቀቀች የተሰማቸውን ደስታ ገለፁ። ጠቅላይ ሚኒስትሩ ለመላው ኢትዮጵያውያን እንኳን ደስ አለን ! እንኳን ደስ አላችሁ ! ብለዋል።\n",
+ "\n",
+ "Hashtags:\n",
+ "#DrAbiyAhmed\n",
+ "\n",
+ "### End of Text:\n",
+ "#DrAbiyAhmed\n",
+ "\n",
+ "### End of Instruction:\n",
+ "Identify Hashtags from the given text.\n",
+ "\n",
+ "[END]\n",
+ "\n",
+ "=========================================\n",
+ "Inference Time:29.108078241348267 seconds\n",
+ "==========================================\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Print the formatted input\n",
+ "print(f\"======\")\n",
+ "print(f\"Input:\\n======\\n{input_text}\\n\")\n",
+ "print(f\"======================\")\n",
+ "print(f\"Generated Output:\\n======================\\n{generated_output}\\n\")\n",
+ "print(f\"=========================================\")\n",
+ "print(f\"Inference Time:{inference_time} seconds\\n==========================================\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "fA17-7t8wwQT"
+ },
+ "source": [
+ "**Fine Tuning Using multiple GPU**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "80-p1mV5wvUG"
+ },
+ "outputs": [],
+ "source": [
+ "# def train(model, tokenizer, dataset, output_dir):\n",
+ "# # Apply preprocessing to the model to prepare it by\n",
+ "# # 1 - Enabling gradient checkpointing to reduce memory usage during fine-tuning\n",
+ "# model.gradient_checkpointing_enable()\n",
+ "\n",
+ "# # 2 - Using the prepare_model_for_kbit_training method from PEFT\n",
+ "# model = prepare_model_for_kbit_training(model)\n",
+ "\n",
+ "# # Get lora module names\n",
+ "# modules = find_all_linear_names(model)\n",
+ "\n",
+ "# # Create PEFT config for these modules and wrap the model to PEFT\n",
+ "# peft_config = create_peft_config(modules)\n",
+ "# model = get_peft_model(model, peft_config)\n",
+ "\n",
+ "# # Print information about the percentage of trainable parameters\n",
+ "# print_trainable_parameters(model)\n",
+ "\n",
+ "# #total_batch_size = n_gpus * per_device_batch_size\n",
+ "# # Training parameters\n",
+ "# trainer = Trainer(\n",
+ "# model=model,\n",
+ "# train_dataset=dataset,\n",
+ "# args=TrainingArguments(\n",
+ "# n_gpu=2,\n",
+ "# per_device_train_batch_size=2,\n",
+ "# gradient_accumulation_steps=4,\n",
+ "# warmup_steps=2,\n",
+ "# max_steps=20,\n",
+ "# learning_rate=2e-4,\n",
+ "# fp16=True,\n",
+ "# logging_steps=1,\n",
+ "# output_dir=\"outputs\",\n",
+ "# optim=\"paged_adamw_8bit\",\n",
+ "\n",
+ "# ),\n",
+ "# data_collator=DataCollatorForLanguageModeling(tokenizer, mlm=False)\n",
+ "# )\n",
+ "\n",
+ "# model.config.use_cache = False # re-enable for inference to speed up predictions for similar inputs\n",
+ "\n",
+ "\n",
+ "# # Verifying the datatypes before training\n",
+ "\n",
+ "# dtypes = {}\n",
+ "# for _, p in model.named_parameters():\n",
+ "# dtype = p.dtype\n",
+ "# if dtype not in dtypes: dtypes[dtype] = 0\n",
+ "# dtypes[dtype] += p.numel()\n",
+ "# total = 0\n",
+ "# for k, v in dtypes.items(): total+= v\n",
+ "# for k, v in dtypes.items():\n",
+ "# print(k, v, v/total)\n",
+ "\n",
+ "# do_train = True\n",
+ "\n",
+ "# # Launch training\n",
+ "# print(\"Training...\")\n",
+ "\n",
+ "# if do_train:\n",
+ "# train_result = trainer.train()\n",
+ "# metrics = train_result.metrics\n",
+ "# trainer.log_metrics(\"train\", metrics)\n",
+ "# trainer.save_metrics(\"train\", metrics)\n",
+ "# trainer.save_state()\n",
+ "# print(metrics)\n",
+ "\n",
+ "# ###\n",
+ "\n",
+ "# # Saving model\n",
+ "# print(\"Saving last checkpoint of the model...\")\n",
+ "# os.makedirs(output_dir, exist_ok=True)\n",
+ "# trainer.model.save_pretrained(output_dir)\n",
+ "\n",
+ "# # Free memory for merging weights\n",
+ "# del model\n",
+ "# del trainer\n",
+ "# torch.cuda.empty_cache()\n",
+ "\n",
+ "\n",
+ "# output_dir = \"results/llama2/final_checkpoint_2g\"\n",
+ "# train(model, tokenizer, dataset, output_dir)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "CTKpwm9JyJA2"
+ },
+ "outputs": [],
+ "source": [
+ "# model_2g = AutoPeftModelForCausalLM.from_pretrained(output_dir, device_map=\"auto\", torch_dtype=torch.bfloat16)\n",
+ "# model_2g = model_2g.merge_and_unload()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "CtQAk2i_yKJP"
+ },
+ "outputs": [],
+ "source": [
+ "# # save tokenizer for easy inference\n",
+ "# tokenizer_2g = AutoTokenizer.from_pretrained(model_name)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "t5tznOVlC9UK"
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+ "source": []
+ }
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