ft-smollm-135m-instruct-with-simple-calculation.ipynb

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     "text": [
      "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\r\n",
      "cudf 24.6.1 requires cubinlinker, which is not installed.\r\n",
      "cudf 24.6.1 requires cupy-cuda11x>=12.0.0, which is not installed.\r\n",
      "cudf 24.6.1 requires ptxcompiler, which is not installed.\r\n",
      "cuml 24.6.1 requires cupy-cuda11x>=12.0.0, which is not installed.\r\n",
      "dask-cudf 24.6.1 requires cupy-cuda11x>=12.0.0, which is not installed.\r\n",
      "cudf 24.6.1 requires cuda-python<12.0a0,>=11.7.1, but you have cuda-python 12.5.0 which is incompatible.\r\n",
      "distributed 2024.5.1 requires dask==2024.5.1, but you have dask 2024.7.0 which is incompatible.\r\n",
      "gcsfs 2024.5.0 requires fsspec==2024.5.0, but you have fsspec 2024.2.0 which is incompatible.\r\n",
      "rapids-dask-dependency 24.6.0a0 requires dask==2024.5.1, but you have dask 2024.7.0 which is incompatible.\r\n",
      "s3fs 2024.5.0 requires fsspec==2024.5.0.*, but you have fsspec 2024.2.0 which is incompatible.\u001b[0m\u001b[31m\r\n",
      "\u001b[0m\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\r\n",
      "kfp 2.5.0 requires google-cloud-storage<3,>=2.2.1, but you have google-cloud-storage 1.44.0 which is incompatible.\u001b[0m\u001b[31m\r\n",
      "\u001b[0m"
     ]
    }
   ],
   "source": [
    "!pip install -U -q transformers==4.39.3\n",
    "!pip install -U -q accelerate==0.28.0\n",
    "!pip install -U -q datasets==2.18.0\n",
    "# !pip install -U -q peft==0.10.0\n",
    "!pip install -U -q bitsandbytes==0.43.1\n",
    "!pip install -U -q trl==0.8.6"
   ]
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   "source": [
    "import warnings\n",
    "\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
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   "id": "53de4151",
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     "text": [
      "The token has not been saved to the git credentials helper. Pass `add_to_git_credential=True` in this function directly or `--add-to-git-credential` if using via `huggingface-cli` if you want to set the git credential as well.\n",
      "Token is valid (permission: write).\n",
      "Your token has been saved to /root/.cache/huggingface/token\n",
      "Login successful\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "from huggingface_hub import login\n",
    "from kaggle_secrets import UserSecretsClient\n",
    "user_secrets = UserSecretsClient()\n",
    "login(token=user_secrets.get_secret(\"HUGGINGFACE_W\"))\n",
    "\n",
    "os.environ[\"WANDB_API_KEY\"]=user_secrets.get_secret(\"WANDB_API_KEY\")\n",
    "os.environ[\"WANDB_PROJECT\"] = \"Fine-tuning HuggingFace SmolLM-135M-Instruct with simple calculation\"\n",
    "os.environ[\"WANDB_NAME\"] = \"ft-smollm-135M-instruct-with-simple-cal\"\n",
    "os.environ[\"MODEL_NAME\"] = \"HuggingFaceTB/SmolLM-135M-Instruct\"\n",
    "os.environ[\"TOKENIZER_NAME\"] = \"HuggingFaceTB/SmolLM-135M-Instruct\"\n",
    "os.environ[\"DATASET\"] = \"micost/simple_calculation_orpo\""
   ]
  },
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    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "5a3016141bd645b099edab820c174be5",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading readme:   0%|          | 0.00/487 [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Downloading data: 100%|██████████| 2.14M/2.14M [00:00<00:00, 5.96MB/s]\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "bdddfbc29c65411494555ed043721dfa",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Generating train split:   0%|          | 0/50000 [00:00<?, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "[Dataset({\n",
       "     features: ['prompt', 'chosen', 'rejected'],\n",
       "     num_rows: 50000\n",
       " })]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from datasets import load_dataset\n",
    "\n",
    "ds=load_dataset(os.getenv(\"DATASET\"), split=[\"train\"])\n",
    "ds"
   ]
  },
  {
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset({\n",
      "    features: ['prompt', 'chosen', 'rejected'],\n",
      "    num_rows: 50000\n",
      "})\n"
     ]
    }
   ],
   "source": [
    "train_ds=ds[0].shuffle(seed=42).select(range(50000))\n",
    "\n",
    "print(train_ds)"
   ]
  },
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   "outputs": [
    {
     "data": {
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       "model_id": "de42a1f4e9d142bab81aa1db7d3662eb",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "tokenizer_config.json:   0%|          | 0.00/3.59k [00:00<?, ?B/s]"
      ]
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    },
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       "model_id": "783c97a4118346539d96ddb7df542ac9",
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       "version_minor": 0
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      "text/plain": [
       "vocab.json:   0%|          | 0.00/801k [00:00<?, ?B/s]"
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     },
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    },
    {
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       "model_id": "cf9cdd7c15024e1889d1fd7e1684c6ee",
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       "version_minor": 0
      },
      "text/plain": [
       "merges.txt:   0%|          | 0.00/466k [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
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    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0ae95debb90948e381b9cf2968c4625f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "tokenizer.json:   0%|          | 0.00/2.10M [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8c79ee078987440297ecb8f4861f2a99",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "special_tokens_map.json:   0%|          | 0.00/565 [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from transformers import AutoTokenizer\n",
    "\n",
    "tokenizer=AutoTokenizer.from_pretrained(os.getenv(\"TOKENIZER_NAME\"))\n",
    "\n",
    "# decoder-only architecture, no need eos token\n",
    "tokenizer.pad_token=tokenizer.eos_token"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "4a1dbd90",
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    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "6ae4e20c3c8942ae8b0b07a03fd1d2c1",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Map (num_proc=4):   0%|          | 0/50000 [00:00<?, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import torch, multiprocessing\n",
    "\n",
    "def preprocess(x):\n",
    "    x[\"chosen\"]=tokenizer.apply_chat_template(x[\"chosen\"], tokenize=False)\n",
    "    x[\"rejected\"]=tokenizer.apply_chat_template(x[\"rejected\"], tokenize=False)\n",
    "    return x\n",
    "\n",
    "train_ds=train_ds.map(preprocess, num_proc=multiprocessing.cpu_count(), load_from_cache_file=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
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   "outputs": [
    {
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       "model_id": "f82535bf35ef47f78b5e485f0705bd7f",
       "version_major": 2,
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      "text/plain": [
       "config.json:   0%|          | 0.00/723 [00:00<?, ?B/s]"
      ]
     },
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    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ea2886c0078740d8bc88f6803da67437",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "model.safetensors:   0%|          | 0.00/269M [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
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    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8ff109c6cb8c41059c6c3eb22dbbb1a2",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "generation_config.json:   0%|          | 0.00/156 [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
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    },
    {
     "data": {
      "text/plain": [
       "device(type='cuda', index=0)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from transformers import AutoModelForCausalLM\n",
    "model=AutoModelForCausalLM.from_pretrained(\n",
    "    os.getenv(\"MODEL_NAME\"),\n",
    "    torch_dtype=torch.float16,\n",
    "#     device_map={\"\": 0},\n",
    "    device_map=\"cuda\",\n",
    "    trust_remote_code=True\n",
    ")\n",
    "\n",
    "model.gradient_checkpointing_enable()\n",
    "model.device"
   ]
  },
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   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-10-29 02:25:42.074781: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:9261] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
      "2024-10-29 02:25:42.074893: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:607] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n",
      "2024-10-29 02:25:42.211842: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1515] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d02573f989134bf5a32b5b013d1cf1cd",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Map:   0%|          | 0/50000 [00:00<?, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "You have loaded a model on multiple GPUs. `is_model_parallel` attribute will be force-set to `True` to avoid any unexpected behavior such as device placement mismatching.\n",
      "Currently training with a batch size of: 4\n",
      "***** Running training *****\n",
      "  Num examples = 50,000\n",
      "  Num Epochs = 1\n",
      "  Instantaneous batch size per device = 4\n",
      "  Total train batch size (w. parallel, distributed & accumulation) = 8\n",
      "  Gradient Accumulation steps = 2\n",
      "  Total optimization steps = 6,250\n",
      "  Number of trainable parameters = 134,515,008\n",
      "Could not estimate the number of tokens of the input, floating-point operations will not be computed\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "    <div>\n",
       "      \n",
       "      <progress value='6250' max='6250' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      [6250/6250 40:37, Epoch 1/1]\n",
       "    </div>\n",
       "    <table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       " <tr style=\"text-align: left;\">\n",
       "      <th>Step</th>\n",
       "      <th>Training Loss</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>1000</td>\n",
       "      <td>0.526900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2000</td>\n",
       "      <td>0.286100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3000</td>\n",
       "      <td>0.248600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4000</td>\n",
       "      <td>0.234000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5000</td>\n",
       "      <td>0.220600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6000</td>\n",
       "      <td>0.214200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table><p>"
      ],
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     },
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     "output_type": "stream",
     "text": [
      "Saving model checkpoint to ft-smollm-135M-instruct-with-simple-cal/checkpoint-6250\n",
      "Configuration saved in ft-smollm-135M-instruct-with-simple-cal/checkpoint-6250/config.json\n",
      "Configuration saved in ft-smollm-135M-instruct-with-simple-cal/checkpoint-6250/generation_config.json\n",
      "Model weights saved in ft-smollm-135M-instruct-with-simple-cal/checkpoint-6250/model.safetensors\n",
      "tokenizer config file saved in ft-smollm-135M-instruct-with-simple-cal/checkpoint-6250/tokenizer_config.json\n",
      "Special tokens file saved in ft-smollm-135M-instruct-with-simple-cal/checkpoint-6250/special_tokens_map.json\n",
      "\n",
      "\n",
      "Training completed. Do not forget to share your model on huggingface.co/models =)\n",
      "\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "TrainOutput(global_step=6250, training_loss=0.2853729229736328, metrics={'train_runtime': 2438.3413, 'train_samples_per_second': 20.506, 'train_steps_per_second': 2.563, 'total_flos': 0.0, 'train_loss': 0.2853729229736328, 'epoch': 1.0})"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from trl import ORPOTrainer, ORPOConfig\n",
    "\n",
    "orpo_config=ORPOConfig(\n",
    "    output_dir=os.getenv(\"WANDB_NAME\"),\n",
    "    #evaluation_strategy=\"steps\",\n",
    "    #do_eval=True,\n",
    "    optim=\"adamw_8bit\",\n",
    "    per_device_train_batch_size=4,\n",
    "    gradient_accumulation_steps=2,\n",
    "    #per_device_eval_batch_size=4,\n",
    "    log_level=\"debug\",\n",
    "    logging_steps=1000,\n",
    "    learning_rate=3e-4,\n",
    "    #eval_steps=100,\n",
    "    save_steps=1000,\n",
    "    save_strategy=\"epoch\",\n",
    "    num_train_epochs=1,\n",
    "    warmup_ratio=0.1,\n",
    "    lr_scheduler_type=\"linear\",\n",
    "    beta=0.1, # beta is ORPO's lambda\n",
    "    max_length=1024,\n",
    "    report_to=\"tensorboard\",\n",
    "    run_name=os.getenv('WANDB_NAME')\n",
    ")\n",
    "\n",
    "trainer = ORPOTrainer(\n",
    "        model=model,\n",
    "        train_dataset=train_ds,\n",
    "        #eval_dataset=eval_ds,\n",
    "        args=orpo_config,\n",
    "        tokenizer=tokenizer,\n",
    ")\n",
    "\n",
    "trainer.train()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "e8ae347a",
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     "exception": false,
     "start_time": "2024-10-29T03:07:15.747103",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "bce81eee1b1843db9daf5f5490055389",
       "version_major": 2,
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      "text/plain": [
       "README.md:   0%|          | 0.00/1.28k [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "tokenizer config file saved in ft-smollm-135M-instruct-with-simple-cal/tokenizer_config.json\n",
      "Special tokens file saved in ft-smollm-135M-instruct-with-simple-cal/special_tokens_map.json\n",
      "Uploading the following files to micost/ft-smollm-135M-instruct-with-simple-cal: tokenizer_config.json,tokenizer.json,vocab.json,special_tokens_map.json,merges.txt,README.md\n",
      "Saving model checkpoint to ft-smollm-135M-instruct-with-simple-cal\n",
      "Configuration saved in ft-smollm-135M-instruct-with-simple-cal/config.json\n",
      "Configuration saved in ft-smollm-135M-instruct-with-simple-cal/generation_config.json\n",
      "Model weights saved in ft-smollm-135M-instruct-with-simple-cal/model.safetensors\n",
      "tokenizer config file saved in ft-smollm-135M-instruct-with-simple-cal/tokenizer_config.json\n",
      "Special tokens file saved in ft-smollm-135M-instruct-with-simple-cal/special_tokens_map.json\n",
      "Dropping the following result as it does not have all the necessary fields:\n",
      "{'task': {'name': 'Causal Language Modeling', 'type': 'text-generation'}}\n"
     ]
    },
    {
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       "events.out.tfevents.1730168797.0fc1bd7735e1.24.0:   0%|          | 0.00/10.6k [00:00<?, ?B/s]"
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    },
    {
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       "version_minor": 0
      },
      "text/plain": [
       "training_args.bin:   0%|          | 0.00/5.30k [00:00<?, ?B/s]"
      ]
     },
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    },
    {
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       "model_id": "034aa36482864ff5830463d36f2e0df5",
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       "version_minor": 0
      },
      "text/plain": [
       "Upload 2 LFS files:   0%|          | 0/2 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "CommitInfo(commit_url='https://huggingface.co/micost/ft-smollm-135M-instruct-with-simple-cal/commit/9f68cbfa4f08173207c592f7e1210ebe9ff79ce5', commit_message='End of training', commit_description='', oid='9f68cbfa4f08173207c592f7e1210ebe9ff79ce5', pr_url=None, pr_revision=None, pr_num=None)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kwargs={\n",
    "    'model_name': os.getenv(\"WANDB_NAME\"),\n",
    "    'finetuned_from': os.getenv('MODEL_NAME'),\n",
    "#     'tasks': 'Text-Generation',\n",
    "#     'dataset_tags':'',\n",
    "    'dataset': os.getenv(\"DATASET\")\n",
    "}\n",
    "\n",
    "tokenizer.push_to_hub(os.getenv(\"WANDB_NAME\"))\n",
    "trainer.push_to_hub(**kwargs)"
   ]
  },
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   "execution_count": 11,
   "id": "f629b0a3",
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "loading configuration file ft-smollm-135M-instruct-with-simple-cal/config.json\n",
      "Model config LlamaConfig {\n",
      "  \"_name_or_path\": \"ft-smollm-135M-instruct-with-simple-cal\",\n",
      "  \"architectures\": [\n",
      "    \"LlamaForCausalLM\"\n",
      "  ],\n",
      "  \"attention_bias\": false,\n",
      "  \"attention_dropout\": 0.0,\n",
      "  \"bos_token_id\": 1,\n",
      "  \"eos_token_id\": 2,\n",
      "  \"hidden_act\": \"silu\",\n",
      "  \"hidden_size\": 576,\n",
      "  \"initializer_range\": 0.02,\n",
      "  \"intermediate_size\": 1536,\n",
      "  \"max_position_embeddings\": 2048,\n",
      "  \"mlp_bias\": false,\n",
      "  \"model_type\": \"llama\",\n",
      "  \"num_attention_heads\": 9,\n",
      "  \"num_hidden_layers\": 30,\n",
      "  \"num_key_value_heads\": 3,\n",
      "  \"pad_token_id\": 2,\n",
      "  \"pretraining_tp\": 1,\n",
      "  \"rms_norm_eps\": 1e-05,\n",
      "  \"rope_scaling\": null,\n",
      "  \"rope_theta\": 10000.0,\n",
      "  \"tie_word_embeddings\": true,\n",
      "  \"torch_dtype\": \"float16\",\n",
      "  \"transformers_version\": \"4.39.3\",\n",
      "  \"use_cache\": true,\n",
      "  \"vocab_size\": 49152\n",
      "}\n",
      "\n",
      "loading weights file ft-smollm-135M-instruct-with-simple-cal/model.safetensors\n",
      "Instantiating LlamaForCausalLM model under default dtype torch.float16.\n",
      "Generate config GenerationConfig {\n",
      "  \"bos_token_id\": 1,\n",
      "  \"eos_token_id\": 2,\n",
      "  \"pad_token_id\": 2\n",
      "}\n",
      "\n",
      "All model checkpoint weights were used when initializing LlamaForCausalLM.\n",
      "\n",
      "All the weights of LlamaForCausalLM were initialized from the model checkpoint at ft-smollm-135M-instruct-with-simple-cal.\n",
      "If your task is similar to the task the model of the checkpoint was trained on, you can already use LlamaForCausalLM for predictions without further training.\n",
      "loading configuration file ft-smollm-135M-instruct-with-simple-cal/generation_config.json\n",
      "Generate config GenerationConfig {\n",
      "  \"bos_token_id\": 1,\n",
      "  \"eos_token_id\": 2,\n",
      "  \"max_new_tokens\": 40,\n",
      "  \"pad_token_id\": 2\n",
      "}\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "user\n",
      "{'A':591,'op':'+','B':78}\n",
      "assistant\n",
      "{'A':591,'op':'+','B':78}\n",
      "user\n",
      "{'A':591,'op':'-','B':78}\n",
      "assistant\n",
      "{'A':58,'op':'-','B':78}\n",
      "user\n",
      "{'A':591,'op':'*','B':78}\n",
      "assistant\n",
      "{'A':591,'op':'*','B':78}\n",
      "user\n",
      "{'A':591,'op':'/','B':78}\n",
      "assistant\n",
      "{'A':591,'op':'/','B':78}\n",
      "user\n",
      "{'A':591,'op':'+','B':785}\n",
      "assistant\n",
      "{'A':591,'op':'+','B':785}\n",
      "user\n",
      "{'A':591,'op':'+','B':784}\n",
      "assistant\n",
      "{'A':591,'op':'+','B':78}\n"
     ]
    }
   ],
   "source": [
    "model=AutoModelForCausalLM.from_pretrained(\n",
    "    os.getenv(\"WANDB_NAME\"), \n",
    "    torch_dtype=torch.float16, \n",
    "    device_map=\"cuda\", \n",
    "    trust_remote_code=True\n",
    ")\n",
    "\n",
    "chat=[\n",
    "    [{\"role\":\"user\",\"content\":\"{'A':591,'op':'+','B':78}\"}],\n",
    "    [{\"role\": \"user\", \"content\": \"{'A':591,'op':'-','B':78}\"}],\n",
    "    [{\"role\": \"user\", \"content\": \"{'A':591,'op':'*','B':78}\"}],\n",
    "    [{\"role\": \"user\", \"content\": \"{'A':591,'op':'/','B':78}\"}],\n",
    "    [{\"role\": \"user\", \"content\": \"{'A':591,'op':'+','B':785}\"}],\n",
    "    [{\"role\": \"user\", \"content\": \"{'A':591,'op':'+','B':784}\"}]\n",
    "]\n",
    "\n",
    "\n",
    "for c in chat:\n",
    "    p=tokenizer.apply_chat_template(c, tokenize=False)\n",
    "    inputs = tokenizer(p, return_tensors=\"pt\").to(\"cuda\")\n",
    "    outputs = model.generate(**inputs, do_sample=True, pad_token_id=tokenizer.eos_token_id, top_p=0.9, max_new_tokens=150)\n",
    "    result = tokenizer.decode(outputs[0], skip_special_tokens=True)\n",
    "    print(result)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b2503da1",
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     "start_time": "2024-10-29T03:07:28.465233",
     "status": "completed"
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    "tags": []
   },
   "source": [
    "# Helpful papers\n",
    "\n",
    "* https://arxiv.org/abs/2403.07691"
   ]
  }
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