:...\n",
+ "Name: 41, dtype: object\n"
+ ]
+ }
+ ],
+ "source": [
+ "# check some random messages\n",
+ "\n",
+ "i=41\n",
+ "print(message_df.message_tree_text.iloc[i])\n",
+ "print('\\n')\n",
+ "print(message_df.iloc[i])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "b1gqwFxZUtbo",
+ "outputId": "b8d00339-2b01-49e6-bcc6-0dbd9006c208"
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "Dataset({\n",
+ " features: ['message_tree_id', 'message_tree_text'],\n",
+ " num_rows: 9823\n",
+ "})"
+ ]
+ },
+ "execution_count": 19,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# convert back to HF datasets format\n",
+ "\n",
+ "data = Dataset.from_pandas(message_df)\n",
+ "data"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 88,
+ "referenced_widgets": [
+ "58b9a27175944cf9bab8c7de50059185",
+ "1d3238ce51e2474fbe3607e6e6709c74",
+ "0b9010e55f0445ee8df4f25acfd5b0a2",
+ "1c70df1712c946fc9a436ec1ec112c0f",
+ "9708aecaba7a478f9edecb8a561757dc",
+ "b1c1122ceb83427b897ce77c3749f732",
+ "31d4ce0dc08e493580443ef7af72a203",
+ "9dcde582a1f64e4eacb563ddbb8a9a73",
+ "6b9638ee0c3d4ce691fa4ae7030539f2",
+ "5c28306bcd2b470ea26521464fffef05",
+ "afa9cf9b8b9c4be7b63b1e7f0d6f6e4d"
+ ]
+ },
+ "id": "oaBHh5yWTkQm",
+ "outputId": "764a1bbe-6d2f-44ae-d484-fb15686c1f2b"
+ },
+ "outputs": [
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "58b9a27175944cf9bab8c7de50059185",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "Map: 0%| | 0/9823 [00:00, ? examples/s]"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/plain": [
+ "Dataset({\n",
+ " features: ['message_tree_id', 'message_tree_text', 'input_ids', 'token_type_ids', 'attention_mask'],\n",
+ " num_rows: 9823\n",
+ "})"
+ ]
+ },
+ "execution_count": 20,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "tokenizer.pad_token = tokenizer.eos_token\n",
+ "\n",
+ "data = data.map(lambda samples: tokenizer(samples[\"message_tree_text\"], padding=True, truncation=True,), batched=True)\n",
+ "data"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 1000
+ },
+ "id": "AQ_HCYruWIHU",
+ "outputId": "4acbc736-3602-4084-de58-982aa978d967"
+ },
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "You're using a PreTrainedTokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.\n"
+ ]
+ },
+ {
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+ "╭─────────────────────────────── Traceback (most recent call last) ────────────────────────────────╮\n",
+ "│ in <cell line: 23>:23 │\n",
+ "│ │\n",
+ "│ /usr/local/lib/python3.10/dist-packages/transformers/trainer.py:1696 in train │\n",
+ "│ │\n",
+ "│ 1693 │ │ inner_training_loop = find_executable_batch_size( │\n",
+ "│ 1694 │ │ │ self._inner_training_loop, self._train_batch_size, args.auto_find_batch_size │\n",
+ "│ 1695 │ │ ) │\n",
+ "│ ❱ 1696 │ │ return inner_training_loop( │\n",
+ "│ 1697 │ │ │ args=args, │\n",
+ "│ 1698 │ │ │ resume_from_checkpoint=resume_from_checkpoint, │\n",
+ "│ 1699 │ │ │ trial=trial, │\n",
+ "│ │\n",
+ "│ /usr/local/lib/python3.10/dist-packages/accelerate/utils/memory.py:132 in decorator │\n",
+ "│ │\n",
+ "│ 129 │ │ │ if batch_size == 0: │\n",
+ "│ 130 │ │ │ │ raise RuntimeError(\"No executable batch size found, reached zero.\") │\n",
+ "│ 131 │ │ │ try: │\n",
+ "│ ❱ 132 │ │ │ │ return function(batch_size, *args, **kwargs) │\n",
+ "│ 133 │ │ │ except Exception as e: │\n",
+ "│ 134 │ │ │ │ if should_reduce_batch_size(e): │\n",
+ "│ 135 │ │ │ │ │ gc.collect() │\n",
+ "│ │\n",
+ "│ /usr/local/lib/python3.10/dist-packages/transformers/trainer.py:2052 in _inner_training_loop │\n",
+ "│ │\n",
+ "│ 2049 │ │ │ │ │ self.state.epoch = epoch + (step + 1 + steps_skipped) / steps_in_epo │\n",
+ "│ 2050 │ │ │ │ │ self.control = self.callback_handler.on_step_end(args, self.state, s │\n",
+ "│ 2051 │ │ │ │ │ │\n",
+ "│ ❱ 2052 │ │ │ │ │ self._maybe_log_save_evaluate(tr_loss, model, trial, epoch, ignore_k │\n",
+ "│ 2053 │ │ │ │ else: │\n",
+ "│ 2054 │ │ │ │ │ self.control = self.callback_handler.on_substep_end(args, self.state │\n",
+ "│ 2055 │\n",
+ "│ │\n",
+ "│ /usr/local/lib/python3.10/dist-packages/transformers/trainer.py:2338 in _maybe_log_save_evaluate │\n",
+ "│ │\n",
+ "│ 2335 │ │ │ self._globalstep_last_logged = self.state.global_step │\n",
+ "│ 2336 │ │ │ self.store_flos() │\n",
+ "│ 2337 │ │ │ │\n",
+ "│ ❱ 2338 │ │ │ self.log(logs) │\n",
+ "│ 2339 │ │ │\n",
+ "│ 2340 │ │ metrics = None │\n",
+ "│ 2341 │ │ if self.control.should_evaluate: │\n",
+ "│ │\n",
+ "│ /usr/local/lib/python3.10/dist-packages/transformers/trainer.py:2700 in log │\n",
+ "│ │\n",
+ "│ 2697 │ │ │\n",
+ "│ 2698 │ │ output = {**logs, **{\"step\": self.state.global_step}} │\n",
+ "│ 2699 │ │ self.state.log_history.append(output) │\n",
+ "│ ❱ 2700 │ │ self.control = self.callback_handler.on_log(self.args, self.state, self.control, │\n",
+ "│ 2701 │ │\n",
+ "│ 2702 │ def _prepare_input(self, data: Union[torch.Tensor, Any]) -> Union[torch.Tensor, Any] │\n",
+ "│ 2703 │ │ \"\"\" │\n",
+ "│ │\n",
+ "│ /usr/local/lib/python3.10/dist-packages/transformers/trainer_callback.py:390 in on_log │\n",
+ "│ │\n",
+ "│ 387 │ │\n",
+ "│ 388 │ def on_log(self, args: TrainingArguments, state: TrainerState, control: TrainerContr │\n",
+ "│ 389 │ │ control.should_log = False │\n",
+ "│ ❱ 390 │ │ return self.call_event(\"on_log\", args, state, control, logs=logs) │\n",
+ "│ 391 │ │\n",
+ "│ 392 │ def on_prediction_step(self, args: TrainingArguments, state: TrainerState, control: │\n",
+ "│ 393 │ │ return self.call_event(\"on_prediction_step\", args, state, control) │\n",
+ "│ │\n",
+ "│ /usr/local/lib/python3.10/dist-packages/transformers/trainer_callback.py:397 in call_event │\n",
+ "│ │\n",
+ "│ 394 │ │\n",
+ "│ 395 │ def call_event(self, event, args, state, control, **kwargs): │\n",
+ "│ 396 │ │ for callback in self.callbacks: │\n",
+ "│ ❱ 397 │ │ │ result = getattr(callback, event)( │\n",
+ "│ 398 │ │ │ │ args, │\n",
+ "│ 399 │ │ │ │ state, │\n",
+ "│ 400 │ │ │ │ control, │\n",
+ "│ │\n",
+ "│ /usr/local/lib/python3.10/dist-packages/transformers/integrations.py:655 in on_log │\n",
+ "│ │\n",
+ "│ 652 │ │ │ │ │ │ \"This invocation of Tensorboard's writer.add_scalar() \" │\n",
+ "│ 653 │ │ │ │ │ │ \"is incorrect so we dropped this attribute.\" │\n",
+ "│ 654 │ │ │ │ │ ) │\n",
+ "│ ❱ 655 │ │ │ self.tb_writer.flush() │\n",
+ "│ 656 │ │\n",
+ "│ 657 │ def on_train_end(self, args, state, control, **kwargs): │\n",
+ "│ 658 │ │ if self.tb_writer: │\n",
+ "│ │\n",
+ "│ /usr/local/lib/python3.10/dist-packages/torch/utils/tensorboard/writer.py:1200 in flush │\n",
+ "│ │\n",
+ "│ 1197 │ │ if self.all_writers is None: │\n",
+ "│ 1198 │ │ │ return │\n",
+ "│ 1199 │ │ for writer in self.all_writers.values(): │\n",
+ "│ ❱ 1200 │ │ │ writer.flush() │\n",
+ "│ 1201 │ │\n",
+ "│ 1202 │ def close(self): │\n",
+ "│ 1203 │ │ if self.all_writers is None: │\n",
+ "│ │\n",
+ "│ /usr/local/lib/python3.10/dist-packages/torch/utils/tensorboard/writer.py:150 in flush │\n",
+ "│ │\n",
+ "│ 147 │ │ Call this method to make sure that all pending events have been written to │\n",
+ "│ 148 │ │ disk. │\n",
+ "│ 149 │ │ \"\"\" │\n",
+ "│ ❱ 150 │ │ self.event_writer.flush() │\n",
+ "│ 151 │ │\n",
+ "│ 152 │ def close(self): │\n",
+ "│ 153 │ │ \"\"\"Flushes the event file to disk and close the file. │\n",
+ "│ │\n",
+ "│ /usr/local/lib/python3.10/dist-packages/tensorboard/summary/writer/event_file_writer.py:125 in │\n",
+ "│ flush │\n",
+ "│ │\n",
+ "│ 122 │ │ Call this method to make sure that all pending events have been │\n",
+ "│ 123 │ │ written to disk. │\n",
+ "│ 124 │ │ \"\"\" │\n",
+ "│ ❱ 125 │ │ self._async_writer.flush() │\n",
+ "│ 126 │ │\n",
+ "│ 127 │ def close(self): │\n",
+ "│ 128 │ │ \"\"\"Performs a final flush of the event file to disk, stops the │\n",
+ "│ │\n",
+ "│ /usr/local/lib/python3.10/dist-packages/tensorboard/summary/writer/event_file_writer.py:190 in │\n",
+ "│ flush │\n",
+ "│ │\n",
+ "│ 187 │ │ │ if self._closed: │\n",
+ "│ 188 │ │ │ │ raise IOError(\"Writer is closed\") │\n",
+ "│ 189 │ │ │ self._byte_queue.join() │\n",
+ "│ ❱ 190 │ │ │ self._writer.flush() │\n",
+ "│ 191 │ │ │ # Check the status again in case the background worker thread has │\n",
+ "│ 192 │ │ │ # failed in the meantime to avoid waiting until the next call to │\n",
+ "│ 193 │ │ │ # surface the error. │\n",
+ "│ │\n",
+ "│ /usr/local/lib/python3.10/dist-packages/tensorboard/summary/writer/record_writer.py:43 in flush │\n",
+ "│ │\n",
+ "│ 40 │ │ self._writer.write(header + header_crc + data + footer_crc) │\n",
+ "│ 41 │ │\n",
+ "│ 42 │ def flush(self): │\n",
+ "│ ❱ 43 │ │ self._writer.flush() │\n",
+ "│ 44 │ │\n",
+ "│ 45 │ def close(self): │\n",
+ "│ 46 │ │ self._writer.close() │\n",
+ "│ │\n",
+ "│ /usr/local/lib/python3.10/dist-packages/tensorflow/python/lib/io/file_io.py:221 in flush │\n",
+ "│ │\n",
+ "│ 218 │ data would survive an application crash but not necessarily an OS crash. │\n",
+ "│ 219 │ \"\"\" │\n",
+ "│ 220 │ if self._writable_file: │\n",
+ "│ ❱ 221 │ self._writable_file.flush() │\n",
+ "│ 222 │\n",
+ "│ 223 def close(self): │\n",
+ "│ 224 │ r\"\"\"Closes the file. │\n",
+ "╰──────────────────────────────────────────────────────────────────────────────────────────────────╯\n",
+ "FailedPreconditionError: /content/drive/MyDrive/Colab \n",
+ "Files/WM/falcon-chat-40b/runs/May30_05-06-25_3e61552a1fc1/events.out.tfevents.1685423187.3e61552a1fc1.231.0; \n",
+ "Transport endpoint is not connected\n",
+ "
\n"
+ ],
+ "text/plain": [
+ "\u001b[31m╭─\u001b[0m\u001b[31m──────────────────────────────\u001b[0m\u001b[31m \u001b[0m\u001b[1;31mTraceback \u001b[0m\u001b[1;2;31m(most recent call last)\u001b[0m\u001b[31m \u001b[0m\u001b[31m───────────────────────────────\u001b[0m\u001b[31m─╮\u001b[0m\n",
+ "\u001b[31m│\u001b[0m in \u001b[92m\u001b[0m:\u001b[94m23\u001b[0m \u001b[31m│\u001b[0m\n",
+ "\u001b[31m│\u001b[0m \u001b[31m│\u001b[0m\n",
+ "\u001b[31m│\u001b[0m \u001b[2;33m/usr/local/lib/python3.10/dist-packages/transformers/\u001b[0m\u001b[1;33mtrainer.py\u001b[0m:\u001b[94m1696\u001b[0m in \u001b[92mtrain\u001b[0m \u001b[31m│\u001b[0m\n",
+ "\u001b[31m│\u001b[0m \u001b[31m│\u001b[0m\n",
+ "\u001b[31m│\u001b[0m \u001b[2m1693 \u001b[0m\u001b[2m│ │ \u001b[0minner_training_loop = find_executable_batch_size( \u001b[31m│\u001b[0m\n",
+ "\u001b[31m│\u001b[0m \u001b[2m1694 \u001b[0m\u001b[2m│ │ │ \u001b[0m\u001b[96mself\u001b[0m._inner_training_loop, \u001b[96mself\u001b[0m._train_batch_size, args.auto_find_batch_size \u001b[31m│\u001b[0m\n",
+ "\u001b[31m│\u001b[0m \u001b[2m1695 \u001b[0m\u001b[2m│ │ \u001b[0m) \u001b[31m│\u001b[0m\n",
+ "\u001b[31m│\u001b[0m \u001b[31m❱ \u001b[0m1696 \u001b[2m│ │ \u001b[0m\u001b[94mreturn\u001b[0m inner_training_loop( \u001b[31m│\u001b[0m\n",
+ "\u001b[31m│\u001b[0m \u001b[2m1697 \u001b[0m\u001b[2m│ │ │ \u001b[0margs=args, \u001b[31m│\u001b[0m\n",
+ "\u001b[31m│\u001b[0m \u001b[2m1698 \u001b[0m\u001b[2m│ │ │ \u001b[0mresume_from_checkpoint=resume_from_checkpoint, \u001b[31m│\u001b[0m\n",
+ "\u001b[31m│\u001b[0m \u001b[2m1699 \u001b[0m\u001b[2m│ │ │ \u001b[0mtrial=trial, \u001b[31m│\u001b[0m\n",
+ "\u001b[31m│\u001b[0m \u001b[31m│\u001b[0m\n",
+ "\u001b[31m│\u001b[0m \u001b[2;33m/usr/local/lib/python3.10/dist-packages/accelerate/utils/\u001b[0m\u001b[1;33mmemory.py\u001b[0m:\u001b[94m132\u001b[0m in \u001b[92mdecorator\u001b[0m \u001b[31m│\u001b[0m\n",
+ "\u001b[31m│\u001b[0m \u001b[31m│\u001b[0m\n",
+ "\u001b[31m│\u001b[0m \u001b[2m129 \u001b[0m\u001b[2m│ │ │ \u001b[0m\u001b[94mif\u001b[0m batch_size == \u001b[94m0\u001b[0m: \u001b[31m│\u001b[0m\n",
+ "\u001b[31m│\u001b[0m \u001b[2m130 \u001b[0m\u001b[2m│ │ │ │ \u001b[0m\u001b[94mraise\u001b[0m \u001b[96mRuntimeError\u001b[0m(\u001b[33m\"\u001b[0m\u001b[33mNo executable batch size found, reached zero.\u001b[0m\u001b[33m\"\u001b[0m) \u001b[31m│\u001b[0m\n",
+ "\u001b[31m│\u001b[0m \u001b[2m131 \u001b[0m\u001b[2m│ │ │ \u001b[0m\u001b[94mtry\u001b[0m: \u001b[31m│\u001b[0m\n",
+ "\u001b[31m│\u001b[0m \u001b[31m❱ \u001b[0m132 \u001b[2m│ │ │ │ \u001b[0m\u001b[94mreturn\u001b[0m function(batch_size, *args, **kwargs) \u001b[31m│\u001b[0m\n",
+ "\u001b[31m│\u001b[0m \u001b[2m133 \u001b[0m\u001b[2m│ │ │ \u001b[0m\u001b[94mexcept\u001b[0m \u001b[96mException\u001b[0m \u001b[94mas\u001b[0m e: \u001b[31m│\u001b[0m\n",
+ "\u001b[31m│\u001b[0m \u001b[2m134 \u001b[0m\u001b[2m│ │ │ │ \u001b[0m\u001b[94mif\u001b[0m should_reduce_batch_size(e): \u001b[31m│\u001b[0m\n",
+ "\u001b[31m│\u001b[0m \u001b[2m135 \u001b[0m\u001b[2m│ │ │ │ │ \u001b[0mgc.collect() \u001b[31m│\u001b[0m\n",
+ "\u001b[31m│\u001b[0m \u001b[31m│\u001b[0m\n",
+ "\u001b[31m│\u001b[0m \u001b[2;33m/usr/local/lib/python3.10/dist-packages/transformers/\u001b[0m\u001b[1;33mtrainer.py\u001b[0m:\u001b[94m2052\u001b[0m in \u001b[92m_inner_training_loop\u001b[0m \u001b[31m│\u001b[0m\n",
+ "\u001b[31m│\u001b[0m \u001b[31m│\u001b[0m\n",
+ "\u001b[31m│\u001b[0m \u001b[2m2049 \u001b[0m\u001b[2m│ │ │ │ │ \u001b[0m\u001b[96mself\u001b[0m.state.epoch = epoch + (step + \u001b[94m1\u001b[0m + steps_skipped) / steps_in_epo \u001b[31m│\u001b[0m\n",
+ "\u001b[31m│\u001b[0m \u001b[2m2050 \u001b[0m\u001b[2m│ │ │ │ │ \u001b[0m\u001b[96mself\u001b[0m.control = \u001b[96mself\u001b[0m.callback_handler.on_step_end(args, \u001b[96mself\u001b[0m.state, \u001b[96ms\u001b[0m \u001b[31m│\u001b[0m\n",
+ "\u001b[31m│\u001b[0m \u001b[2m2051 \u001b[0m\u001b[2m│ │ │ │ │ \u001b[0m \u001b[31m│\u001b[0m\n",
+ "\u001b[31m│\u001b[0m \u001b[31m❱ \u001b[0m2052 \u001b[2m│ │ │ │ │ \u001b[0m\u001b[96mself\u001b[0m._maybe_log_save_evaluate(tr_loss, model, trial, epoch, ignore_k \u001b[31m│\u001b[0m\n",
+ "\u001b[31m│\u001b[0m \u001b[2m2053 \u001b[0m\u001b[2m│ │ │ │ \u001b[0m\u001b[94melse\u001b[0m: \u001b[31m│\u001b[0m\n",
+ "\u001b[31m│\u001b[0m \u001b[2m2054 \u001b[0m\u001b[2m│ │ │ │ │ \u001b[0m\u001b[96mself\u001b[0m.control = \u001b[96mself\u001b[0m.callback_handler.on_substep_end(args, \u001b[96mself\u001b[0m.state \u001b[31m│\u001b[0m\n",
+ "\u001b[31m│\u001b[0m \u001b[2m2055 \u001b[0m \u001b[31m│\u001b[0m\n",
+ "\u001b[31m│\u001b[0m \u001b[31m│\u001b[0m\n",
+ "\u001b[31m│\u001b[0m \u001b[2;33m/usr/local/lib/python3.10/dist-packages/transformers/\u001b[0m\u001b[1;33mtrainer.py\u001b[0m:\u001b[94m2338\u001b[0m in \u001b[92m_maybe_log_save_evaluate\u001b[0m \u001b[31m│\u001b[0m\n",
+ "\u001b[31m│\u001b[0m \u001b[31m│\u001b[0m\n",
+ "\u001b[31m│\u001b[0m \u001b[2m2335 \u001b[0m\u001b[2m│ │ │ \u001b[0m\u001b[96mself\u001b[0m._globalstep_last_logged = \u001b[96mself\u001b[0m.state.global_step \u001b[31m│\u001b[0m\n",
+ "\u001b[31m│\u001b[0m \u001b[2m2336 \u001b[0m\u001b[2m│ │ │ \u001b[0m\u001b[96mself\u001b[0m.store_flos() \u001b[31m│\u001b[0m\n",
+ "\u001b[31m│\u001b[0m \u001b[2m2337 \u001b[0m\u001b[2m│ │ │ \u001b[0m \u001b[31m│\u001b[0m\n",
+ "\u001b[31m│\u001b[0m \u001b[31m❱ \u001b[0m2338 \u001b[2m│ │ │ \u001b[0m\u001b[96mself\u001b[0m.log(logs) \u001b[31m│\u001b[0m\n",
+ "\u001b[31m│\u001b[0m \u001b[2m2339 \u001b[0m\u001b[2m│ │ \u001b[0m \u001b[31m│\u001b[0m\n",
+ "\u001b[31m│\u001b[0m \u001b[2m2340 \u001b[0m\u001b[2m│ │ \u001b[0mmetrics = \u001b[94mNone\u001b[0m \u001b[31m│\u001b[0m\n",
+ "\u001b[31m│\u001b[0m \u001b[2m2341 \u001b[0m\u001b[2m│ │ \u001b[0m\u001b[94mif\u001b[0m \u001b[96mself\u001b[0m.control.should_evaluate: \u001b[31m│\u001b[0m\n",
+ "\u001b[31m│\u001b[0m \u001b[31m│\u001b[0m\n",
+ "\u001b[31m│\u001b[0m \u001b[2;33m/usr/local/lib/python3.10/dist-packages/transformers/\u001b[0m\u001b[1;33mtrainer.py\u001b[0m:\u001b[94m2700\u001b[0m in \u001b[92mlog\u001b[0m \u001b[31m│\u001b[0m\n",
+ "\u001b[31m│\u001b[0m \u001b[31m│\u001b[0m\n",
+ "\u001b[31m│\u001b[0m \u001b[2m2697 \u001b[0m\u001b[2m│ │ \u001b[0m \u001b[31m│\u001b[0m\n",
+ "\u001b[31m│\u001b[0m \u001b[2m2698 \u001b[0m\u001b[2m│ │ \u001b[0moutput = {**logs, **{\u001b[33m\"\u001b[0m\u001b[33mstep\u001b[0m\u001b[33m\"\u001b[0m: \u001b[96mself\u001b[0m.state.global_step}} \u001b[31m│\u001b[0m\n",
+ "\u001b[31m│\u001b[0m \u001b[2m2699 \u001b[0m\u001b[2m│ │ \u001b[0m\u001b[96mself\u001b[0m.state.log_history.append(output) \u001b[31m│\u001b[0m\n",
+ "\u001b[31m│\u001b[0m \u001b[31m❱ \u001b[0m2700 \u001b[2m│ │ \u001b[0m\u001b[96mself\u001b[0m.control = \u001b[96mself\u001b[0m.callback_handler.on_log(\u001b[96mself\u001b[0m.args, \u001b[96mself\u001b[0m.state, \u001b[96mself\u001b[0m.control, \u001b[31m│\u001b[0m\n",
+ "\u001b[31m│\u001b[0m \u001b[2m2701 \u001b[0m\u001b[2m│ \u001b[0m \u001b[31m│\u001b[0m\n",
+ "\u001b[31m│\u001b[0m \u001b[2m2702 \u001b[0m\u001b[2m│ \u001b[0m\u001b[94mdef\u001b[0m \u001b[92m_prepare_input\u001b[0m(\u001b[96mself\u001b[0m, data: Union[torch.Tensor, Any]) -> Union[torch.Tensor, Any] \u001b[31m│\u001b[0m\n",
+ "\u001b[31m│\u001b[0m \u001b[2m2703 \u001b[0m\u001b[2;90m│ │ \u001b[0m\u001b[33m\"\"\"\u001b[0m \u001b[31m│\u001b[0m\n",
+ "\u001b[31m│\u001b[0m \u001b[31m│\u001b[0m\n",
+ "\u001b[31m│\u001b[0m \u001b[2;33m/usr/local/lib/python3.10/dist-packages/transformers/\u001b[0m\u001b[1;33mtrainer_callback.py\u001b[0m:\u001b[94m390\u001b[0m in \u001b[92mon_log\u001b[0m \u001b[31m│\u001b[0m\n",
+ "\u001b[31m│\u001b[0m \u001b[31m│\u001b[0m\n",
+ "\u001b[31m│\u001b[0m \u001b[2m387 \u001b[0m\u001b[2m│ \u001b[0m \u001b[31m│\u001b[0m\n",
+ "\u001b[31m│\u001b[0m \u001b[2m388 \u001b[0m\u001b[2m│ \u001b[0m\u001b[94mdef\u001b[0m \u001b[92mon_log\u001b[0m(\u001b[96mself\u001b[0m, args: TrainingArguments, state: TrainerState, control: TrainerContr \u001b[31m│\u001b[0m\n",
+ "\u001b[31m│\u001b[0m \u001b[2m389 \u001b[0m\u001b[2m│ │ \u001b[0mcontrol.should_log = \u001b[94mFalse\u001b[0m \u001b[31m│\u001b[0m\n",
+ "\u001b[31m│\u001b[0m \u001b[31m❱ \u001b[0m390 \u001b[2m│ │ \u001b[0m\u001b[94mreturn\u001b[0m \u001b[96mself\u001b[0m.call_event(\u001b[33m\"\u001b[0m\u001b[33mon_log\u001b[0m\u001b[33m\"\u001b[0m, args, state, control, logs=logs) \u001b[31m│\u001b[0m\n",
+ "\u001b[31m│\u001b[0m \u001b[2m391 \u001b[0m\u001b[2m│ \u001b[0m \u001b[31m│\u001b[0m\n",
+ "\u001b[31m│\u001b[0m \u001b[2m392 \u001b[0m\u001b[2m│ \u001b[0m\u001b[94mdef\u001b[0m \u001b[92mon_prediction_step\u001b[0m(\u001b[96mself\u001b[0m, args: TrainingArguments, state: TrainerState, control: \u001b[31m│\u001b[0m\n",
+ "\u001b[31m│\u001b[0m \u001b[2m393 \u001b[0m\u001b[2m│ │ \u001b[0m\u001b[94mreturn\u001b[0m \u001b[96mself\u001b[0m.call_event(\u001b[33m\"\u001b[0m\u001b[33mon_prediction_step\u001b[0m\u001b[33m\"\u001b[0m, args, state, control) \u001b[31m│\u001b[0m\n",
+ "\u001b[31m│\u001b[0m \u001b[31m│\u001b[0m\n",
+ "\u001b[31m│\u001b[0m \u001b[2;33m/usr/local/lib/python3.10/dist-packages/transformers/\u001b[0m\u001b[1;33mtrainer_callback.py\u001b[0m:\u001b[94m397\u001b[0m in \u001b[92mcall_event\u001b[0m \u001b[31m│\u001b[0m\n",
+ "\u001b[31m│\u001b[0m \u001b[31m│\u001b[0m\n",
+ "\u001b[31m│\u001b[0m \u001b[2m394 \u001b[0m\u001b[2m│ \u001b[0m \u001b[31m│\u001b[0m\n",
+ "\u001b[31m│\u001b[0m \u001b[2m395 \u001b[0m\u001b[2m│ \u001b[0m\u001b[94mdef\u001b[0m \u001b[92mcall_event\u001b[0m(\u001b[96mself\u001b[0m, event, args, state, control, **kwargs): \u001b[31m│\u001b[0m\n",
+ "\u001b[31m│\u001b[0m \u001b[2m396 \u001b[0m\u001b[2m│ │ \u001b[0m\u001b[94mfor\u001b[0m callback \u001b[95min\u001b[0m \u001b[96mself\u001b[0m.callbacks: \u001b[31m│\u001b[0m\n",
+ "\u001b[31m│\u001b[0m \u001b[31m❱ \u001b[0m397 \u001b[2m│ │ │ \u001b[0mresult = \u001b[96mgetattr\u001b[0m(callback, event)( \u001b[31m│\u001b[0m\n",
+ "\u001b[31m│\u001b[0m \u001b[2m398 \u001b[0m\u001b[2m│ │ │ │ \u001b[0margs, \u001b[31m│\u001b[0m\n",
+ "\u001b[31m│\u001b[0m \u001b[2m399 \u001b[0m\u001b[2m│ │ │ │ \u001b[0mstate, \u001b[31m│\u001b[0m\n",
+ "\u001b[31m│\u001b[0m \u001b[2m400 \u001b[0m\u001b[2m│ │ │ │ \u001b[0mcontrol, \u001b[31m│\u001b[0m\n",
+ "\u001b[31m│\u001b[0m \u001b[31m│\u001b[0m\n",
+ "\u001b[31m│\u001b[0m \u001b[2;33m/usr/local/lib/python3.10/dist-packages/transformers/\u001b[0m\u001b[1;33mintegrations.py\u001b[0m:\u001b[94m655\u001b[0m in \u001b[92mon_log\u001b[0m \u001b[31m│\u001b[0m\n",
+ "\u001b[31m│\u001b[0m \u001b[31m│\u001b[0m\n",
+ "\u001b[31m│\u001b[0m \u001b[2m 652 \u001b[0m\u001b[2m│ │ │ │ │ │ \u001b[0m\u001b[33m\"\u001b[0m\u001b[33mThis invocation of Tensorboard\u001b[0m\u001b[33m'\u001b[0m\u001b[33ms writer.add_scalar() \u001b[0m\u001b[33m\"\u001b[0m \u001b[31m│\u001b[0m\n",
+ "\u001b[31m│\u001b[0m \u001b[2m 653 \u001b[0m\u001b[2m│ │ │ │ │ │ \u001b[0m\u001b[33m\"\u001b[0m\u001b[33mis incorrect so we dropped this attribute.\u001b[0m\u001b[33m\"\u001b[0m \u001b[31m│\u001b[0m\n",
+ "\u001b[31m│\u001b[0m \u001b[2m 654 \u001b[0m\u001b[2m│ │ │ │ │ \u001b[0m) \u001b[31m│\u001b[0m\n",
+ "\u001b[31m│\u001b[0m \u001b[31m❱ \u001b[0m 655 \u001b[2m│ │ │ \u001b[0m\u001b[96mself\u001b[0m.tb_writer.flush() \u001b[31m│\u001b[0m\n",
+ "\u001b[31m│\u001b[0m \u001b[2m 656 \u001b[0m\u001b[2m│ \u001b[0m \u001b[31m│\u001b[0m\n",
+ "\u001b[31m│\u001b[0m \u001b[2m 657 \u001b[0m\u001b[2m│ \u001b[0m\u001b[94mdef\u001b[0m \u001b[92mon_train_end\u001b[0m(\u001b[96mself\u001b[0m, args, state, control, **kwargs): \u001b[31m│\u001b[0m\n",
+ "\u001b[31m│\u001b[0m \u001b[2m 658 \u001b[0m\u001b[2m│ │ \u001b[0m\u001b[94mif\u001b[0m \u001b[96mself\u001b[0m.tb_writer: \u001b[31m│\u001b[0m\n",
+ "\u001b[31m│\u001b[0m \u001b[31m│\u001b[0m\n",
+ "\u001b[31m│\u001b[0m \u001b[2;33m/usr/local/lib/python3.10/dist-packages/torch/utils/tensorboard/\u001b[0m\u001b[1;33mwriter.py\u001b[0m:\u001b[94m1200\u001b[0m in \u001b[92mflush\u001b[0m \u001b[31m│\u001b[0m\n",
+ "\u001b[31m│\u001b[0m \u001b[31m│\u001b[0m\n",
+ "\u001b[31m│\u001b[0m \u001b[2m1197 \u001b[0m\u001b[2m│ │ \u001b[0m\u001b[94mif\u001b[0m \u001b[96mself\u001b[0m.all_writers \u001b[95mis\u001b[0m \u001b[94mNone\u001b[0m: \u001b[31m│\u001b[0m\n",
+ "\u001b[31m│\u001b[0m \u001b[2m1198 \u001b[0m\u001b[2m│ │ │ \u001b[0m\u001b[94mreturn\u001b[0m \u001b[31m│\u001b[0m\n",
+ "\u001b[31m│\u001b[0m \u001b[2m1199 \u001b[0m\u001b[2m│ │ \u001b[0m\u001b[94mfor\u001b[0m writer \u001b[95min\u001b[0m \u001b[96mself\u001b[0m.all_writers.values(): \u001b[31m│\u001b[0m\n",
+ "\u001b[31m│\u001b[0m \u001b[31m❱ \u001b[0m1200 \u001b[2m│ │ │ \u001b[0mwriter.flush() \u001b[31m│\u001b[0m\n",
+ "\u001b[31m│\u001b[0m \u001b[2m1201 \u001b[0m\u001b[2m│ \u001b[0m \u001b[31m│\u001b[0m\n",
+ "\u001b[31m│\u001b[0m \u001b[2m1202 \u001b[0m\u001b[2m│ \u001b[0m\u001b[94mdef\u001b[0m \u001b[92mclose\u001b[0m(\u001b[96mself\u001b[0m): \u001b[31m│\u001b[0m\n",
+ "\u001b[31m│\u001b[0m \u001b[2m1203 \u001b[0m\u001b[2m│ │ \u001b[0m\u001b[94mif\u001b[0m \u001b[96mself\u001b[0m.all_writers \u001b[95mis\u001b[0m \u001b[94mNone\u001b[0m: \u001b[31m│\u001b[0m\n",
+ "\u001b[31m│\u001b[0m \u001b[31m│\u001b[0m\n",
+ "\u001b[31m│\u001b[0m \u001b[2;33m/usr/local/lib/python3.10/dist-packages/torch/utils/tensorboard/\u001b[0m\u001b[1;33mwriter.py\u001b[0m:\u001b[94m150\u001b[0m in \u001b[92mflush\u001b[0m \u001b[31m│\u001b[0m\n",
+ "\u001b[31m│\u001b[0m \u001b[31m│\u001b[0m\n",
+ "\u001b[31m│\u001b[0m \u001b[2m 147 \u001b[0m\u001b[2;33m│ │ \u001b[0m\u001b[33mCall this method to make sure that all pending events have been written to\u001b[0m \u001b[31m│\u001b[0m\n",
+ "\u001b[31m│\u001b[0m \u001b[2m 148 \u001b[0m\u001b[2;33m│ │ \u001b[0m\u001b[33mdisk.\u001b[0m \u001b[31m│\u001b[0m\n",
+ "\u001b[31m│\u001b[0m \u001b[2m 149 \u001b[0m\u001b[2;33m│ │ \u001b[0m\u001b[33m\"\"\"\u001b[0m \u001b[31m│\u001b[0m\n",
+ "\u001b[31m│\u001b[0m \u001b[31m❱ \u001b[0m 150 \u001b[2m│ │ \u001b[0m\u001b[96mself\u001b[0m.event_writer.flush() \u001b[31m│\u001b[0m\n",
+ "\u001b[31m│\u001b[0m \u001b[2m 151 \u001b[0m\u001b[2m│ \u001b[0m \u001b[31m│\u001b[0m\n",
+ "\u001b[31m│\u001b[0m \u001b[2m 152 \u001b[0m\u001b[2m│ \u001b[0m\u001b[94mdef\u001b[0m \u001b[92mclose\u001b[0m(\u001b[96mself\u001b[0m): \u001b[31m│\u001b[0m\n",
+ "\u001b[31m│\u001b[0m \u001b[2m 153 \u001b[0m\u001b[2;90m│ │ \u001b[0m\u001b[33m\"\"\"Flushes the event file to disk and close the file.\u001b[0m \u001b[31m│\u001b[0m\n",
+ "\u001b[31m│\u001b[0m \u001b[31m│\u001b[0m\n",
+ "\u001b[31m│\u001b[0m \u001b[2;33m/usr/local/lib/python3.10/dist-packages/tensorboard/summary/writer/\u001b[0m\u001b[1;33mevent_file_writer.py\u001b[0m:\u001b[94m125\u001b[0m in \u001b[31m│\u001b[0m\n",
+ "\u001b[31m│\u001b[0m \u001b[92mflush\u001b[0m \u001b[31m│\u001b[0m\n",
+ "\u001b[31m│\u001b[0m \u001b[31m│\u001b[0m\n",
+ "\u001b[31m│\u001b[0m \u001b[2m122 \u001b[0m\u001b[2;33m│ │ \u001b[0m\u001b[33mCall this method to make sure that all pending events have been\u001b[0m \u001b[31m│\u001b[0m\n",
+ "\u001b[31m│\u001b[0m \u001b[2m123 \u001b[0m\u001b[2;33m│ │ \u001b[0m\u001b[33mwritten to disk.\u001b[0m \u001b[31m│\u001b[0m\n",
+ "\u001b[31m│\u001b[0m \u001b[2m124 \u001b[0m\u001b[2;33m│ │ \u001b[0m\u001b[33m\"\"\"\u001b[0m \u001b[31m│\u001b[0m\n",
+ "\u001b[31m│\u001b[0m \u001b[31m❱ \u001b[0m125 \u001b[2m│ │ \u001b[0m\u001b[96mself\u001b[0m._async_writer.flush() \u001b[31m│\u001b[0m\n",
+ "\u001b[31m│\u001b[0m \u001b[2m126 \u001b[0m\u001b[2m│ \u001b[0m \u001b[31m│\u001b[0m\n",
+ "\u001b[31m│\u001b[0m \u001b[2m127 \u001b[0m\u001b[2m│ \u001b[0m\u001b[94mdef\u001b[0m \u001b[92mclose\u001b[0m(\u001b[96mself\u001b[0m): \u001b[31m│\u001b[0m\n",
+ "\u001b[31m│\u001b[0m \u001b[2m128 \u001b[0m\u001b[2;90m│ │ \u001b[0m\u001b[33m\"\"\"Performs a final flush of the event file to disk, stops the\u001b[0m \u001b[31m│\u001b[0m\n",
+ "\u001b[31m│\u001b[0m \u001b[31m│\u001b[0m\n",
+ "\u001b[31m│\u001b[0m \u001b[2;33m/usr/local/lib/python3.10/dist-packages/tensorboard/summary/writer/\u001b[0m\u001b[1;33mevent_file_writer.py\u001b[0m:\u001b[94m190\u001b[0m in \u001b[31m│\u001b[0m\n",
+ "\u001b[31m│\u001b[0m \u001b[92mflush\u001b[0m \u001b[31m│\u001b[0m\n",
+ "\u001b[31m│\u001b[0m \u001b[31m│\u001b[0m\n",
+ "\u001b[31m│\u001b[0m \u001b[2m187 \u001b[0m\u001b[2m│ │ │ \u001b[0m\u001b[94mif\u001b[0m \u001b[96mself\u001b[0m._closed: \u001b[31m│\u001b[0m\n",
+ "\u001b[31m│\u001b[0m \u001b[2m188 \u001b[0m\u001b[2m│ │ │ │ \u001b[0m\u001b[94mraise\u001b[0m \u001b[96mIOError\u001b[0m(\u001b[33m\"\u001b[0m\u001b[33mWriter is closed\u001b[0m\u001b[33m\"\u001b[0m) \u001b[31m│\u001b[0m\n",
+ "\u001b[31m│\u001b[0m \u001b[2m189 \u001b[0m\u001b[2m│ │ │ \u001b[0m\u001b[96mself\u001b[0m._byte_queue.join() \u001b[31m│\u001b[0m\n",
+ "\u001b[31m│\u001b[0m \u001b[31m❱ \u001b[0m190 \u001b[2m│ │ │ \u001b[0m\u001b[96mself\u001b[0m._writer.flush() \u001b[31m│\u001b[0m\n",
+ "\u001b[31m│\u001b[0m \u001b[2m191 \u001b[0m\u001b[2m│ │ │ \u001b[0m\u001b[2m# Check the status again in case the background worker thread has\u001b[0m \u001b[31m│\u001b[0m\n",
+ "\u001b[31m│\u001b[0m \u001b[2m192 \u001b[0m\u001b[2m│ │ │ \u001b[0m\u001b[2m# failed in the meantime to avoid waiting until the next call to\u001b[0m \u001b[31m│\u001b[0m\n",
+ "\u001b[31m│\u001b[0m \u001b[2m193 \u001b[0m\u001b[2m│ │ │ \u001b[0m\u001b[2m# surface the error.\u001b[0m \u001b[31m│\u001b[0m\n",
+ "\u001b[31m│\u001b[0m \u001b[31m│\u001b[0m\n",
+ "\u001b[31m│\u001b[0m \u001b[2;33m/usr/local/lib/python3.10/dist-packages/tensorboard/summary/writer/\u001b[0m\u001b[1;33mrecord_writer.py\u001b[0m:\u001b[94m43\u001b[0m in \u001b[92mflush\u001b[0m \u001b[31m│\u001b[0m\n",
+ "\u001b[31m│\u001b[0m \u001b[31m│\u001b[0m\n",
+ "\u001b[31m│\u001b[0m \u001b[2m40 \u001b[0m\u001b[2m│ │ \u001b[0m\u001b[96mself\u001b[0m._writer.write(header + header_crc + data + footer_crc) \u001b[31m│\u001b[0m\n",
+ "\u001b[31m│\u001b[0m \u001b[2m41 \u001b[0m\u001b[2m│ \u001b[0m \u001b[31m│\u001b[0m\n",
+ "\u001b[31m│\u001b[0m \u001b[2m42 \u001b[0m\u001b[2m│ \u001b[0m\u001b[94mdef\u001b[0m \u001b[92mflush\u001b[0m(\u001b[96mself\u001b[0m): \u001b[31m│\u001b[0m\n",
+ "\u001b[31m│\u001b[0m \u001b[31m❱ \u001b[0m43 \u001b[2m│ │ \u001b[0m\u001b[96mself\u001b[0m._writer.flush() \u001b[31m│\u001b[0m\n",
+ "\u001b[31m│\u001b[0m \u001b[2m44 \u001b[0m\u001b[2m│ \u001b[0m \u001b[31m│\u001b[0m\n",
+ "\u001b[31m│\u001b[0m \u001b[2m45 \u001b[0m\u001b[2m│ \u001b[0m\u001b[94mdef\u001b[0m \u001b[92mclose\u001b[0m(\u001b[96mself\u001b[0m): \u001b[31m│\u001b[0m\n",
+ "\u001b[31m│\u001b[0m \u001b[2m46 \u001b[0m\u001b[2m│ │ \u001b[0m\u001b[96mself\u001b[0m._writer.close() \u001b[31m│\u001b[0m\n",
+ "\u001b[31m│\u001b[0m \u001b[31m│\u001b[0m\n",
+ "\u001b[31m│\u001b[0m \u001b[2;33m/usr/local/lib/python3.10/dist-packages/tensorflow/python/lib/io/\u001b[0m\u001b[1;33mfile_io.py\u001b[0m:\u001b[94m221\u001b[0m in \u001b[92mflush\u001b[0m \u001b[31m│\u001b[0m\n",
+ "\u001b[31m│\u001b[0m \u001b[31m│\u001b[0m\n",
+ "\u001b[31m│\u001b[0m \u001b[2m 218 \u001b[0m\u001b[2;33m│ \u001b[0m\u001b[33mdata would survive an application crash but not necessarily an OS crash.\u001b[0m \u001b[31m│\u001b[0m\n",
+ "\u001b[31m│\u001b[0m \u001b[2m 219 \u001b[0m\u001b[2;33m│ \u001b[0m\u001b[33m\"\"\"\u001b[0m \u001b[31m│\u001b[0m\n",
+ "\u001b[31m│\u001b[0m \u001b[2m 220 \u001b[0m\u001b[2m│ \u001b[0m\u001b[94mif\u001b[0m \u001b[96mself\u001b[0m._writable_file: \u001b[31m│\u001b[0m\n",
+ "\u001b[31m│\u001b[0m \u001b[31m❱ \u001b[0m 221 \u001b[2m│ \u001b[0m\u001b[96mself\u001b[0m._writable_file.flush() \u001b[31m│\u001b[0m\n",
+ "\u001b[31m│\u001b[0m \u001b[2m 222 \u001b[0m\u001b[2m \u001b[0m \u001b[31m│\u001b[0m\n",
+ "\u001b[31m│\u001b[0m \u001b[2m 223 \u001b[0m\u001b[2m \u001b[0m\u001b[94mdef\u001b[0m \u001b[92mclose\u001b[0m(\u001b[96mself\u001b[0m): \u001b[31m│\u001b[0m\n",
+ "\u001b[31m│\u001b[0m \u001b[2m 224 \u001b[0m\u001b[2;90m│ \u001b[0m\u001b[33mr\u001b[0m\u001b[33m\"\"\"Closes the file.\u001b[0m \u001b[31m│\u001b[0m\n",
+ "\u001b[31m╰──────────────────────────────────────────────────────────────────────────────────────────────────╯\u001b[0m\n",
+ "\u001b[1;91mFailedPreconditionError: \u001b[0m\u001b[35m/content/drive/MyDrive/\u001b[0m\u001b[95mColab\u001b[0m \n",
+ "Files/WM/falcon-chat-40b/runs/May30_05-\u001b[1;36m06\u001b[0m-25_3e61552a1fc1/events.out.tfevents.\u001b[1;36m1685423187.\u001b[0m3e61552a1fc1.\u001b[1;36m231.0\u001b[0m; \n",
+ "Transport endpoint is not connected\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "training_args = transformers.TrainingArguments(\n",
+ " auto_find_batch_size=True,\n",
+ " gradient_accumulation_steps=4,\n",
+ " num_train_epochs=1,\n",
+ " learning_rate=2e-5,\n",
+ " fp16=True,\n",
+ " save_total_limit=4,\n",
+ " logging_steps=25,\n",
+ " output_dir=\"/content/drive/MyDrive/Colab Files/WM/falcon-chat-40b\",\n",
+ " save_strategy='epoch',\n",
+ " optim=\"paged_adamw_8bit\",\n",
+ " lr_scheduler_type = 'cosine',\n",
+ " warmup_ratio = 0.05,\n",
+ ")\n",
+ "\n",
+ "trainer = transformers.Trainer(\n",
+ " model=model,\n",
+ " train_dataset=data,\n",
+ " args=training_args,\n",
+ " data_collator=transformers.DataCollatorForLanguageModeling(tokenizer, mlm=False),\n",
+ ")\n",
+ "model.config.use_cache = False # silence the warnings. Please re-enable for inference!\n",
+ "trainer.train()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "Duak7T_B3VpJ"
+ },
+ "source": [
+ "## Share adapters on the 🤗 Hub"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 22,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 135,
+ "referenced_widgets": [
+ "ca22fdc5543b40a695476c352965e587",
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+ "c17abfbff6644ecf9ec2f21d10495588",
+ "bd60144a6ba2489a95e30d6e3f2aab61",
+ "2fc0d85d5f194613bb6cb238afeefb57",
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+ "f95b824d2f08420fb907de8b2c529c1a",
+ "bcd8c57594f44936975c07f7848aa078",
+ "c5d0534c10364104b89b98e70ef1346a",
+ "86dfd30f10554f8aa12c7076860fc361",
+ "5400eb206c5f49c4aa4e57c612d8fde4",
+ "087a82e337554cfa8642dbdd97624f30",
+ "e20dcca2167048f4a41275680d4e9d51",
+ "1b834967bc8e4779ad58687fbcbd527b"
+ ]
+ },
+ "id": "VxB6UV5XAvvP",
+ "outputId": "72fcdaad-8183-4544-9025-3973324ea427"
+ },
+ "outputs": [
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "ca22fdc5543b40a695476c352965e587",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "Upload 1 LFS files: 0%| | 0/1 [00:00, ?it/s]"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "873e2d07dd6b4e1d86d9fd560948e8b3",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "adapter_model.bin: 0%| | 0.00/33.5M [00:00, ?B/s]"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/plain": [
+ "CommitInfo(commit_url='https://huggingface.co/dfurman/falcon-40b-chat-oasst1/commit/ac771b1cd698d5d2c7ce270eb15924cd2ea366aa', commit_message='Upload model', commit_description='', oid='ac771b1cd698d5d2c7ce270eb15924cd2ea366aa', pr_url=None, pr_revision=None, pr_num=None)"
+ ]
+ },
+ "execution_count": 22,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "model.push_to_hub(\"dfurman/falcon-40b-chat-oasst1\", use_auth_token=True)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "S65GcxNGA9kz"
+ },
+ "source": [
+ "## Load adapters from the Hub\n",
+ "\n",
+ "You can also directly load adapters from the Hub using the commands below:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 637,
+ "referenced_widgets": [
+ "af8ed9f488774e158860da31a070ebdb",
+ "012fd769dabc4b6ab285cf0e838632a4",
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+ "78398c61b7fd43e7844673da022352b4",
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+ "c4257b19ccee4aa4b6986e7e2ea366a3",
+ "8284aee324944895b7da43554615dfc4",
+ "3450ab02b1714805a9b54b27e93d2e0d",
+ "34f7e1a0f48e4c488d60a75ddf321d63",
+ "711dfc9060594030935dbe58ad688d9d",
+ "fe9c5af5775646f39f9ad1b3cfc7bbec",
+ "8cb14ad1398847b090456990ea35e5ca",
+ "f6cf360f5caf4ec782aa7461ce6bf1c1",
+ "f76f28be57274973acb9ea7a9cea5d2a",
+ "e8d11ba7bcb449a3a31da63551938957"
+ ]
+ },
+ "id": "hsD1VKqeA62Z",
+ "outputId": "43c39595-6f30-451e-9ff1-9e8f4afc5c63"
+ },
+ "outputs": [],
+ "source": [
+ "import torch\n",
+ "from peft import PeftModel, PeftConfig\n",
+ "from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig\n",
+ "\n",
+ "peft_model_id = \"dfurman/falcon-40b-chat-oasst1\"\n",
+ "config = PeftConfig.from_pretrained(peft_model_id)\n",
+ "\n",
+ "bnb_config = BitsAndBytesConfig(\n",
+ " load_in_4bit=True,\n",
+ " bnb_4bit_use_double_quant=True,\n",
+ " bnb_4bit_quant_type=\"nf4\",\n",
+ " bnb_4bit_compute_dtype=torch.bfloat16\n",
+ ")\n",
+ "\n",
+ "model = AutoModelForCausalLM.from_pretrained(\n",
+ " config.base_model_name_or_path,\n",
+ " quantization_config=bnb_config,\n",
+ " device_map={\"\":0},\n",
+ " trust_remote_code=True,\n",
+ ")\n",
+ "tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path)\n",
+ "tokenizer.pad_token = tokenizer.eos_token\n",
+ "\n",
+ "# Load the Lora model\n",
+ "model = PeftModel.from_pretrained(model, peft_model_id)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "J_xz1Cc-Kx4c"
+ },
+ "source": [
+ "## Inference\n",
+ "\n",
+ "You can then directly use the trained model or the model that you have loaded from the 🤗 Hub for inference as you would do it usually in `transformers`."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 53
+ },
+ "id": "jlpAYdyVGoyo",
+ "outputId": "6fa1758f-4ffa-4a0d-8c8d-0b3d7f497641"
+ },
+ "outputs": [
+ {
+ "data": {
+ "application/vnd.google.colaboratory.intrinsic+json": {
+ "type": "string"
+ },
+ "text/plain": [
+ "': My name is Daniel. Write a short email to my closest friends inviting them to come to my home on Friday for a dinner party, I will make the food but tell them to BYOB. \\n:'"
+ ]
+ },
+ "execution_count": 3,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "prompt = \"\"\": My name is Daniel. Write a short email to my closest friends inviting them to come to my home on Friday for a dinner party, I will make the food but tell them to BYOB. \n",
+ ":\"\"\"\n",
+ "\n",
+ "prompt"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "5UpKIshHJQmm",
+ "outputId": "c5bcd426-f94f-4ecc-b99c-3d7b74617d5c"
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "{'input_ids': tensor([[ 39, 15564, 48190, 1814, 1536, 304, 8156, 25, 14687, 241,\n",
+ " 1866, 2572, 271, 491, 14710, 2153, 19549, 612, 271, 1239,\n",
+ " 271, 491, 1081, 313, 4201, 312, 241, 5947, 3054, 23,\n",
+ " 295, 451, 717, 248, 1655, 480, 1705, 612, 271, 15528,\n",
+ " 18791, 25, 4610, 39, 13359, 48190]], device='cuda:0'), 'token_type_ids': tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
+ " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]],\n",
+ " device='cuda:0'), 'attention_mask': tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
+ " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]],\n",
+ " device='cuda:0')}"
+ ]
+ },
+ "execution_count": 4,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "batch = tokenizer(\n",
+ " prompt,\n",
+ " padding=True,\n",
+ " truncation=True,\n",
+ " return_tensors='pt'\n",
+ ")\n",
+ "batch = batch.to('cuda:0')\n",
+ "batch"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "FuUMu4tzKx4e",
+ "outputId": "66da0ee0-48e9-441d-a6fb-09ea4529103c"
+ },
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/usr/local/lib/python3.10/dist-packages/transformers/generation/utils.py:1255: UserWarning: You have modified the pretrained model configuration to control generation. This is a deprecated strategy to control generation and will be removed soon, in a future version. Please use a generation configuration file (see https://huggingface.co/docs/transformers/main_classes/text_generation)\n",
+ " warnings.warn(\n"
+ ]
+ }
+ ],
+ "source": [
+ "with torch.cuda.amp.autocast():\n",
+ " output_tokens = model.generate(\n",
+ " input_ids = batch.input_ids, \n",
+ " max_new_tokens=200,\n",
+ " temperature=0.7,\n",
+ " top_p=0.7,\n",
+ " num_return_sequences=1,\n",
+ " pad_token_id=tokenizer.eos_token_id,\n",
+ " eos_token_id=tokenizer.eos_token_id,\n",
+ " )"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "RCe7ly02JVis",
+ "outputId": "e5a2633a-2ea5-494f-9376-93e555383f77"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Dear Friends,\n",
+ "\n",
+ "I'm excited to invite you to a dinner party at my home on Friday, [date]. I will be making the food, but please bring your own beverages. I'm looking forward to seeing you and catching up!\n",
+ "\n",
+ "Sincerely,\n",
+ "Daniel\n",
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "generated_text = tokenizer.decode(output_tokens[0], skip_special_tokens=True)\n",
+ "# Inspect message response in the outputs\n",
+ "print(generated_text.split(\": \")[1].split(\": \")[-1])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 35
+ },
+ "id": "ErwLWPPMCjmg",
+ "outputId": "e5f56cfa-e55d-4504-e814-03bcd1f487ab"
+ },
+ "outputs": [
+ {
+ "data": {
+ "application/vnd.google.colaboratory.intrinsic+json": {
+ "type": "string"
+ },
+ "text/plain": [
+ "': Create a list of four things to do in San Francisco. \\n:'"
+ ]
+ },
+ "execution_count": 7,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "prompt = \"\"\": Create a list of four things to do in San Francisco. \n",
+ ":\"\"\"\n",
+ "\n",
+ "prompt"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "gTfZmK37Cjmg",
+ "outputId": "40f240cf-d9f3-47c7-ec76-aa092a020ae6"
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "{'input_ids': tensor([[ 39, 15564, 48190, 8715, 241, 1175, 275, 1855, 1209, 271,\n",
+ " 441, 272, 3787, 8863, 25, 4610, 39, 13359, 48190]],\n",
+ " device='cuda:0'), 'token_type_ids': tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]],\n",
+ " device='cuda:0'), 'attention_mask': tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]],\n",
+ " device='cuda:0')}"
+ ]
+ },
+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "batch = tokenizer(\n",
+ " prompt,\n",
+ " padding=True,\n",
+ " truncation=True,\n",
+ " return_tensors='pt'\n",
+ ")\n",
+ "batch = batch.to('cuda:0')\n",
+ "batch"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {
+ "id": "3j55z1AhCjmh"
+ },
+ "outputs": [],
+ "source": [
+ "with torch.cuda.amp.autocast():\n",
+ " output_tokens = model.generate(\n",
+ " input_ids = batch.input_ids, \n",
+ " max_new_tokens=300,\n",
+ " temperature=0.7,\n",
+ " top_p=0.7,\n",
+ " num_return_sequences=1,\n",
+ " pad_token_id=tokenizer.eos_token_id,\n",
+ " eos_token_id=tokenizer.eos_token_id,\n",
+ " )"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "6oTaI6m6Cjmh",
+ "outputId": "1a55f5fe-3dda-4121-9076-c0496416d0dd"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Here are four things to do in San Francisco:\n",
+ "\n",
+ "1. Visit the Golden Gate Bridge: The Golden Gate Bridge is one of the most iconic landmarks in San Francisco and a must-see for any visitor. You can walk or bike across the bridge, or take a ferry to get a closer look.\n",
+ "\n",
+ "2. Explore the Fisherman's Wharf: The Fisherman's Wharf is a popular tourist destination with a variety of restaurants, shops, and attractions. You can try some fresh seafood, take a boat tour, or visit the Aquarium of the Bay.\n",
+ "\n",
+ "3. Visit the Alcatraz Island: Alcatraz Island is a former federal prison that is now a national park and a popular tourist destination. You can take a ferry to the island and explore the prison, learn about its history, and enjoy the beautiful views of the city.\n",
+ "\n",
+ "4. Take a cable car ride: San Francisco's cable cars are a unique and fun way to get around the city. You can ride the cable cars up and down the city's steep hills and enjoy the views along the way.\n",
+ "\n",
+ "These are just a few of the many things to do in San Francisco. There are also museums, parks, and other attractions to explore.\n",
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "generated_text = tokenizer.decode(output_tokens[0], skip_special_tokens=True)\n",
+ "# Inspect message response in the outputs\n",
+ "print(generated_text.split(\": \")[1].split(\": \")[-1])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "aNX2bTr3NZCM"
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+ "_model_name": "HTMLModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/controls",
+ "_view_module_version": "1.5.0",
+ "_view_name": "HTMLView",
+ "description": "",
+ "description_tooltip": null,
+ "layout": "IPY_MODEL_2e00d4270baf47bdb48985326d13df6e",
+ "placeholder": "",
+ "style": "IPY_MODEL_eabfc0efe41242c99ec08be98c675b3d",
+ "value": "\nPro Tip: If you don't already have one, you can create a dedicated\n'notebooks' token with 'write' access, that you can then easily reuse for all\nnotebooks. |