{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "ebc05db3-a28f-4f6c-8ebc-649d9b3012ca", "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "1b61a5c74bbf4d45b2b1c469682586e5", "version_major": 2, "version_minor": 0 }, "text/plain": [ "tokenizer_config.json: 0%| | 0.00/50.8k [00:00 666\u001b[0m prev_sig_handler \u001b[38;5;241m=\u001b[39m signal\u001b[38;5;241m.\u001b[39msignal(signal\u001b[38;5;241m.\u001b[39mSIGALRM, _raise_timeout_error)\n\u001b[0;32m 667\u001b[0m signal\u001b[38;5;241m.\u001b[39malarm(TIME_OUT_REMOTE_CODE)\n", "\u001b[1;31mAttributeError\u001b[0m: module 'signal' has no attribute 'SIGALRM'", "\nDuring handling of the above exception, another exception occurred:\n", "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", "Cell \u001b[1;32mIn[1], line 8\u001b[0m\n\u001b[0;32m 6\u001b[0m \u001b[38;5;66;03m# Load tokenizer and model\u001b[39;00m\n\u001b[0;32m 7\u001b[0m tokenizer \u001b[38;5;241m=\u001b[39m AutoTokenizer\u001b[38;5;241m.\u001b[39mfrom_pretrained(model_id)\n\u001b[1;32m----> 8\u001b[0m model \u001b[38;5;241m=\u001b[39m AutoModelForCausalLM\u001b[38;5;241m.\u001b[39mfrom_pretrained(\n\u001b[0;32m 9\u001b[0m model_id,\n\u001b[0;32m 10\u001b[0m torch_dtype\u001b[38;5;241m=\u001b[39mtorch\u001b[38;5;241m.\u001b[39mbfloat16\n\u001b[0;32m 11\u001b[0m )\n\u001b[0;32m 13\u001b[0m \u001b[38;5;66;03m# Apply the chat template\u001b[39;00m\n\u001b[0;32m 14\u001b[0m messages \u001b[38;5;241m=\u001b[39m [\n\u001b[0;32m 15\u001b[0m {\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrole\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msystem\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcontent\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mYou are a helpful AI assistant.\u001b[39m\u001b[38;5;124m\"\u001b[39m},\n\u001b[0;32m 16\u001b[0m {\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrole\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124muser\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcontent\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mHow are you?\u001b[39m\u001b[38;5;124m\"\u001b[39m},\n\u001b[0;32m 17\u001b[0m ]\n", "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\transformers\\models\\auto\\auto_factory.py:531\u001b[0m, in \u001b[0;36m_BaseAutoModelClass.from_pretrained\u001b[1;34m(cls, pretrained_model_name_or_path, *model_args, **kwargs)\u001b[0m\n\u001b[0;32m 528\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m kwargs\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mquantization_config\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m) \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m 529\u001b[0m _ \u001b[38;5;241m=\u001b[39m kwargs\u001b[38;5;241m.\u001b[39mpop(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mquantization_config\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m--> 531\u001b[0m config, kwargs \u001b[38;5;241m=\u001b[39m AutoConfig\u001b[38;5;241m.\u001b[39mfrom_pretrained(\n\u001b[0;32m 532\u001b[0m pretrained_model_name_or_path,\n\u001b[0;32m 533\u001b[0m return_unused_kwargs\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m,\n\u001b[0;32m 534\u001b[0m trust_remote_code\u001b[38;5;241m=\u001b[39mtrust_remote_code,\n\u001b[0;32m 535\u001b[0m code_revision\u001b[38;5;241m=\u001b[39mcode_revision,\n\u001b[0;32m 536\u001b[0m _commit_hash\u001b[38;5;241m=\u001b[39mcommit_hash,\n\u001b[0;32m 537\u001b[0m \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mhub_kwargs,\n\u001b[0;32m 538\u001b[0m \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs,\n\u001b[0;32m 539\u001b[0m )\n\u001b[0;32m 541\u001b[0m \u001b[38;5;66;03m# if torch_dtype=auto was passed here, ensure to pass it on\u001b[39;00m\n\u001b[0;32m 542\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m kwargs_orig\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtorch_dtype\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m) \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mauto\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n", "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\transformers\\models\\auto\\configuration_auto.py:1117\u001b[0m, in \u001b[0;36mAutoConfig.from_pretrained\u001b[1;34m(cls, pretrained_model_name_or_path, **kwargs)\u001b[0m\n\u001b[0;32m 1115\u001b[0m has_remote_code \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mauto_map\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01min\u001b[39;00m config_dict \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mAutoConfig\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01min\u001b[39;00m config_dict[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mauto_map\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[0;32m 1116\u001b[0m has_local_code \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmodel_type\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01min\u001b[39;00m config_dict \u001b[38;5;129;01mand\u001b[39;00m config_dict[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmodel_type\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;129;01min\u001b[39;00m CONFIG_MAPPING\n\u001b[1;32m-> 1117\u001b[0m trust_remote_code \u001b[38;5;241m=\u001b[39m resolve_trust_remote_code(\n\u001b[0;32m 1118\u001b[0m trust_remote_code, pretrained_model_name_or_path, has_local_code, has_remote_code\n\u001b[0;32m 1119\u001b[0m )\n\u001b[0;32m 1121\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m has_remote_code \u001b[38;5;129;01mand\u001b[39;00m trust_remote_code:\n\u001b[0;32m 1122\u001b[0m class_ref \u001b[38;5;241m=\u001b[39m config_dict[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mauto_map\u001b[39m\u001b[38;5;124m\"\u001b[39m][\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mAutoConfig\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n", "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\transformers\\dynamic_module_utils.py:682\u001b[0m, in \u001b[0;36mresolve_trust_remote_code\u001b[1;34m(trust_remote_code, model_name, has_local_code, has_remote_code)\u001b[0m\n\u001b[0;32m 679\u001b[0m signal\u001b[38;5;241m.\u001b[39malarm(\u001b[38;5;241m0\u001b[39m)\n\u001b[0;32m 680\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m:\n\u001b[0;32m 681\u001b[0m \u001b[38;5;66;03m# OS which does not support signal.SIGALRM\u001b[39;00m\n\u001b[1;32m--> 682\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[0;32m 683\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mThe repository for \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mmodel_name\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m contains custom code which must be executed to correctly \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 684\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mload the model. You can inspect the repository content at https://hf.co/\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mmodel_name\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m.\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 685\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mPlease pass the argument `trust_remote_code=True` to allow custom code to be run.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 686\u001b[0m )\n\u001b[0;32m 687\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[0;32m 688\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m prev_sig_handler \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n", "\u001b[1;31mValueError\u001b[0m: The repository for microsoft/bitnet-b1.58-2B-4T contains custom code which must be executed to correctly load the model. You can inspect the repository content at https://hf.co/microsoft/bitnet-b1.58-2B-4T.\nPlease pass the argument `trust_remote_code=True` to allow custom code to be run." ] } ], "source": [ "import torch\n", "from transformers import AutoModelForCausalLM, AutoTokenizer\n", "\n", "model_id = \"microsoft/bitnet-b1.58-2B-4T\"\n", "\n", "# Load tokenizer and model\n", "tokenizer = AutoTokenizer.from_pretrained(model_id)\n", "model = AutoModelForCausalLM.from_pretrained(\n", " model_id,\n", " torch_dtype=torch.bfloat16\n", ")\n", "\n", "# Apply the chat template\n", "messages = [\n", " {\"role\": \"system\", \"content\": \"You are a helpful AI assistant.\"},\n", " {\"role\": \"user\", \"content\": \"How are you?\"},\n", "]\n", "prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)\n", "chat_input = tokenizer(prompt, return_tensors=\"pt\").to(model.device)\n", "\n", "# Generate response\n", "chat_outputs = model.generate(**chat_input, max_new_tokens=50)\n", "response = tokenizer.decode(chat_outputs[0][chat_input['input_ids'].shape[-1]:], skip_special_tokens=True) # Decode only the response part\n", "print(\"\\nAssistant Response:\", response)\n" ] }, { "cell_type": "code", "execution_count": null, "id": "2b6c0add-3a67-47df-be9d-ed78bbd08a16", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python [conda env:base] *", "language": "python", "name": "conda-base-py" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.12.7" } }, "nbformat": 4, "nbformat_minor": 5 }