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install fire\n!pip install peft\n!pip install accelerate","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"S_li0GiCUsFZ","outputId":"8f0f60d2-dcc1-47e5-b0b3-bf1480f57c20","execution":{"iopub.status.busy":"2024-04-21T19:12:32.849843Z","iopub.execute_input":"2024-04-21T19:12:32.850169Z","iopub.status.idle":"2024-04-21T19:13:14.369292Z","shell.execute_reply.started":"2024-04-21T19:12:32.850144Z","shell.execute_reply":"2024-04-21T19:13:14.368331Z"},"trusted":true},"execution_count":1,"outputs":[{"name":"stdout","text":"Collecting fire\n  Downloading fire-0.6.0.tar.gz (88 kB)\n\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m88.4/88.4 kB\u001b[0m \u001b[31m2.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25ldone\n\u001b[?25hRequirement already satisfied: six in /opt/conda/lib/python3.10/site-packages (from fire) (1.16.0)\nRequirement already satisfied: termcolor in /opt/conda/lib/python3.10/site-packages (from fire) (2.4.0)\nBuilding wheels for collected packages: fire\n  Building wheel for fire (setup.py) ... \u001b[?25ldone\n\u001b[?25h  Created wheel for fire: filename=fire-0.6.0-py2.py3-none-any.whl size=117031 sha256=a0980f7222027121c3d5b85ea22da12fe4c844192173c7e85d556fc632d8004a\n  Stored in directory: /root/.cache/pip/wheels/d6/6d/5d/5b73fa0f46d01a793713f8859201361e9e581ced8c75e5c6a3\nSuccessfully built fire\nInstalling collected packages: fire\nSuccessfully installed fire-0.6.0\nCollecting peft\n  Downloading peft-0.10.0-py3-none-any.whl.metadata (13 kB)\nRequirement already satisfied: numpy>=1.17 in /opt/conda/lib/python3.10/site-packages (from peft) (1.26.4)\nRequirement already satisfied: packaging>=20.0 in /opt/conda/lib/python3.10/site-packages (from peft) (21.3)\nRequirement already satisfied: psutil in /opt/conda/lib/python3.10/site-packages (from peft) (5.9.3)\nRequirement already satisfied: pyyaml in /opt/conda/lib/python3.10/site-packages (from peft) (6.0.1)\nRequirement already satisfied: torch>=1.13.0 in /opt/conda/lib/python3.10/site-packages (from peft) (2.1.2)\nRequirement already satisfied: transformers in /opt/conda/lib/python3.10/site-packages (from peft) (4.39.3)\nRequirement already satisfied: tqdm in /opt/conda/lib/python3.10/site-packages (from peft) (4.66.1)\nRequirement already satisfied: accelerate>=0.21.0 in /opt/conda/lib/python3.10/site-packages (from peft) (0.29.3)\nRequirement already satisfied: safetensors in /opt/conda/lib/python3.10/site-packages (from peft) (0.4.3)\nRequirement already satisfied: huggingface-hub>=0.17.0 in /opt/conda/lib/python3.10/site-packages (from peft) (0.22.2)\nRequirement already satisfied: filelock in /opt/conda/lib/python3.10/site-packages (from huggingface-hub>=0.17.0->peft) (3.13.1)\nRequirement already satisfied: fsspec>=2023.5.0 in /opt/conda/lib/python3.10/site-packages (from huggingface-hub>=0.17.0->peft) (2024.2.0)\nRequirement already satisfied: requests in /opt/conda/lib/python3.10/site-packages (from huggingface-hub>=0.17.0->peft) (2.31.0)\nRequirement already satisfied: typing-extensions>=3.7.4.3 in /opt/conda/lib/python3.10/site-packages (from huggingface-hub>=0.17.0->peft) (4.9.0)\nRequirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /opt/conda/lib/python3.10/site-packages (from packaging>=20.0->peft) (3.1.1)\nRequirement already satisfied: sympy in /opt/conda/lib/python3.10/site-packages (from torch>=1.13.0->peft) (1.12)\nRequirement already satisfied: networkx in /opt/conda/lib/python3.10/site-packages (from torch>=1.13.0->peft) (3.2.1)\nRequirement already satisfied: jinja2 in /opt/conda/lib/python3.10/site-packages (from torch>=1.13.0->peft) (3.1.2)\nRequirement already satisfied: regex!=2019.12.17 in /opt/conda/lib/python3.10/site-packages (from transformers->peft) (2023.12.25)\nRequirement already satisfied: tokenizers<0.19,>=0.14 in /opt/conda/lib/python3.10/site-packages (from transformers->peft) (0.15.2)\nRequirement already satisfied: MarkupSafe>=2.0 in /opt/conda/lib/python3.10/site-packages (from jinja2->torch>=1.13.0->peft) (2.1.3)\nRequirement already satisfied: charset-normalizer<4,>=2 in /opt/conda/lib/python3.10/site-packages (from requests->huggingface-hub>=0.17.0->peft) (3.3.2)\nRequirement already satisfied: idna<4,>=2.5 in /opt/conda/lib/python3.10/site-packages (from requests->huggingface-hub>=0.17.0->peft) (3.6)\nRequirement already satisfied: urllib3<3,>=1.21.1 in /opt/conda/lib/python3.10/site-packages (from requests->huggingface-hub>=0.17.0->peft) (1.26.18)\nRequirement already satisfied: certifi>=2017.4.17 in /opt/conda/lib/python3.10/site-packages (from requests->huggingface-hub>=0.17.0->peft) (2024.2.2)\nRequirement already satisfied: mpmath>=0.19 in /opt/conda/lib/python3.10/site-packages (from sympy->torch>=1.13.0->peft) (1.3.0)\nDownloading peft-0.10.0-py3-none-any.whl (199 kB)\n\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m199.1/199.1 kB\u001b[0m \u001b[31m4.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m00:01\u001b[0m\n\u001b[?25hInstalling collected packages: peft\nSuccessfully installed peft-0.10.0\nRequirement already satisfied: accelerate in /opt/conda/lib/python3.10/site-packages (0.29.3)\nRequirement already satisfied: numpy>=1.17 in /opt/conda/lib/python3.10/site-packages (from accelerate) (1.26.4)\nRequirement already satisfied: packaging>=20.0 in /opt/conda/lib/python3.10/site-packages (from accelerate) (21.3)\nRequirement already satisfied: psutil in /opt/conda/lib/python3.10/site-packages (from accelerate) (5.9.3)\nRequirement already satisfied: pyyaml in /opt/conda/lib/python3.10/site-packages (from accelerate) (6.0.1)\nRequirement already satisfied: torch>=1.10.0 in /opt/conda/lib/python3.10/site-packages (from accelerate) (2.1.2)\nRequirement already satisfied: huggingface-hub in /opt/conda/lib/python3.10/site-packages (from accelerate) (0.22.2)\nRequirement already satisfied: safetensors>=0.3.1 in /opt/conda/lib/python3.10/site-packages (from accelerate) (0.4.3)\nRequirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /opt/conda/lib/python3.10/site-packages (from packaging>=20.0->accelerate) (3.1.1)\nRequirement already satisfied: filelock in /opt/conda/lib/python3.10/site-packages (from torch>=1.10.0->accelerate) (3.13.1)\nRequirement already satisfied: typing-extensions in /opt/conda/lib/python3.10/site-packages (from torch>=1.10.0->accelerate) (4.9.0)\nRequirement already satisfied: sympy in /opt/conda/lib/python3.10/site-packages (from torch>=1.10.0->accelerate) (1.12)\nRequirement already satisfied: networkx in /opt/conda/lib/python3.10/site-packages (from torch>=1.10.0->accelerate) (3.2.1)\nRequirement already satisfied: jinja2 in /opt/conda/lib/python3.10/site-packages (from torch>=1.10.0->accelerate) (3.1.2)\nRequirement already satisfied: fsspec in /opt/conda/lib/python3.10/site-packages (from torch>=1.10.0->accelerate) (2024.2.0)\nRequirement already satisfied: requests in /opt/conda/lib/python3.10/site-packages (from huggingface-hub->accelerate) (2.31.0)\nRequirement already satisfied: tqdm>=4.42.1 in /opt/conda/lib/python3.10/site-packages (from huggingface-hub->accelerate) (4.66.1)\nRequirement already satisfied: MarkupSafe>=2.0 in /opt/conda/lib/python3.10/site-packages (from jinja2->torch>=1.10.0->accelerate) (2.1.3)\nRequirement already satisfied: charset-normalizer<4,>=2 in /opt/conda/lib/python3.10/site-packages (from requests->huggingface-hub->accelerate) (3.3.2)\nRequirement already satisfied: idna<4,>=2.5 in /opt/conda/lib/python3.10/site-packages (from requests->huggingface-hub->accelerate) (3.6)\nRequirement already satisfied: urllib3<3,>=1.21.1 in /opt/conda/lib/python3.10/site-packages (from requests->huggingface-hub->accelerate) (1.26.18)\nRequirement already satisfied: certifi>=2017.4.17 in /opt/conda/lib/python3.10/site-packages (from requests->huggingface-hub->accelerate) (2024.2.2)\nRequirement already satisfied: mpmath>=0.19 in /opt/conda/lib/python3.10/site-packages (from sympy->torch>=1.10.0->accelerate) (1.3.0)\n","output_type":"stream"}]},{"cell_type":"code","source":"import fire\nimport torch\nimport os\nimport sys\nimport time\nimport json\nfrom typing import List\n\nfrom transformers import (\n    LlamaTokenizer,\n    LlamaForCausalLM,\n    AutoModelForCausalLM,\n    AutoTokenizer,\n    LlamaConfig\n)\nfrom peft import PeftModel\nfrom accelerate import disk_offload","metadata":{"id":"wy4vEtLsqnSJ","execution":{"iopub.status.busy":"2024-04-21T19:26:13.684226Z","iopub.execute_input":"2024-04-21T19:26:13.684603Z","iopub.status.idle":"2024-04-21T19:26:17.176789Z","shell.execute_reply.started":"2024-04-21T19:26:13.684573Z","shell.execute_reply":"2024-04-21T19:26:17.175977Z"},"trusted":true},"execution_count":2,"outputs":[]},{"cell_type":"code","source":"BASE_PROMPT = \"\"\"Below is an interaction between a human and an AI fluent in English and Amharic, providing reliable and informative answers.\nThe AI is supposed to answer test questions from the human with short responses saying just the answer and nothing else.\nHuman: {}\nAssistant [Amharic] : \"\"\"","metadata":{"id":"WMM7cK_nUDN0","execution":{"iopub.status.busy":"2024-04-21T19:26:19.062531Z","iopub.execute_input":"2024-04-21T19:26:19.063112Z","iopub.status.idle":"2024-04-21T19:26:19.070805Z","shell.execute_reply.started":"2024-04-21T19:26:19.063076Z","shell.execute_reply":"2024-04-21T19:26:19.069816Z"},"trusted":true},"execution_count":3,"outputs":[]},{"cell_type":"code","source":"# Set the seeds for reproducibility\ntorch.cuda.manual_seed(42)\ntorch.manual_seed(42)","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"543VVe7BD5su","outputId":"a990dc72-7f71-4003-b373-7895961dad9c","execution":{"iopub.status.busy":"2024-04-21T19:26:22.421436Z","iopub.execute_input":"2024-04-21T19:26:22.421834Z","iopub.status.idle":"2024-04-21T19:26:22.429587Z","shell.execute_reply.started":"2024-04-21T19:26:22.421800Z","shell.execute_reply":"2024-04-21T19:26:22.428662Z"},"trusted":true},"execution_count":4,"outputs":[{"execution_count":4,"output_type":"execute_result","data":{"text/plain":"<torch._C.Generator at 0x79b70b229bf0>"},"metadata":{}}]},{"cell_type":"code","source":"!pip install -i https://pypi.org/simple/ bitsandbytes","metadata":{"execution":{"iopub.status.busy":"2024-04-21T19:24:19.353534Z","iopub.execute_input":"2024-04-21T19:24:19.354483Z","iopub.status.idle":"2024-04-21T19:24:35.580005Z","shell.execute_reply.started":"2024-04-21T19:24:19.354440Z","shell.execute_reply":"2024-04-21T19:24:35.578742Z"},"trusted":true},"execution_count":8,"outputs":[{"name":"stdout","text":"Looking in indexes: https://pypi.org/simple/\nCollecting bitsandbytes\n  Downloading bitsandbytes-0.43.1-py3-none-manylinux_2_24_x86_64.whl.metadata (2.2 kB)\nRequirement already satisfied: torch in /opt/conda/lib/python3.10/site-packages (from bitsandbytes) (2.1.2)\nRequirement already satisfied: numpy in /opt/conda/lib/python3.10/site-packages (from bitsandbytes) (1.26.4)\nRequirement already satisfied: filelock in /opt/conda/lib/python3.10/site-packages (from torch->bitsandbytes) (3.13.1)\nRequirement already satisfied: typing-extensions in /opt/conda/lib/python3.10/site-packages (from torch->bitsandbytes) (4.9.0)\nRequirement already satisfied: sympy in /opt/conda/lib/python3.10/site-packages (from torch->bitsandbytes) (1.12)\nRequirement already satisfied: networkx in /opt/conda/lib/python3.10/site-packages (from torch->bitsandbytes) (3.2.1)\nRequirement already satisfied: jinja2 in /opt/conda/lib/python3.10/site-packages (from torch->bitsandbytes) (3.1.2)\nRequirement already satisfied: fsspec in /opt/conda/lib/python3.10/site-packages (from torch->bitsandbytes) (2024.2.0)\nRequirement already satisfied: MarkupSafe>=2.0 in /opt/conda/lib/python3.10/site-packages (from jinja2->torch->bitsandbytes) (2.1.3)\nRequirement already satisfied: mpmath>=0.19 in /opt/conda/lib/python3.10/site-packages (from sympy->torch->bitsandbytes) (1.3.0)\nDownloading bitsandbytes-0.43.1-py3-none-manylinux_2_24_x86_64.whl (119.8 MB)\n\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m119.8/119.8 MB\u001b[0m \u001b[31m13.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m00:01\u001b[0m00:01\u001b[0m\n\u001b[?25hInstalling collected packages: bitsandbytes\nSuccessfully installed bitsandbytes-0.43.1\nNote: you may need to restart the kernel to use updated packages.\n","output_type":"stream"}]},{"cell_type":"markdown","source":"**Don't forget to restart the kernel JO!!! 😡**","metadata":{}},{"cell_type":"code","source":"model = AutoModelForCausalLM.from_pretrained(\n    \"Johntad110/llama-2-7b-amharic-tokenizer\",\n    return_dict=True,\n    load_in_8bit=True,\n    device_map=\"auto\",\n    low_cpu_mem_usage=True,\n    attn_implementation=\"sdpa\",\n    token=\"<HF_TOKEN>\"\n)","metadata":{"id":"l1kmRTIhECXu","colab":{"base_uri":"https://localhost:8080/","height":67,"referenced_widgets":["ce2205aa4f0e46dea6a94ca99984c101","93a32b84c62e4868a572a2a0b9c11002","e190f5bcdde44db88785f4747fd3b156","9b137f68429b4c78b4105ed3587372e3","c2236d8b041e4ab8901e96c539e1259f","76685f83c826487496354262f61b716b","cd389486c3894ea3a9c81fca991b6bde","e7cee9feacf34b3fa4b9ce5fccef99b0","043a336e1e21479bbc94f4d9d8637475","cd2ac578b0ff4c5eb3066913fde976d6","6cdcce135fe443b2a56fbd9b306c90cc"]},"outputId":"d003a5f2-bdfb-4e31-d3e0-38803e177583","execution":{"iopub.status.busy":"2024-04-21T19:26:25.811887Z","iopub.execute_input":"2024-04-21T19:26:25.812265Z","iopub.status.idle":"2024-04-21T19:27:49.082059Z","shell.execute_reply.started":"2024-04-21T19:26:25.812237Z","shell.execute_reply":"2024-04-21T19:27:49.080902Z"},"trusted":true},"execution_count":5,"outputs":[{"name":"stderr","text":"The `load_in_4bit` and `load_in_8bit` arguments are deprecated and will be removed in the future versions. Please, pass a `BitsAndBytesConfig` object in `quantization_config` argument instead.\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"model.safetensors.index.json:   0%|          | 0.00/26.8k [00:00<?, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"61ed084386cf4972acdb8fcf29cbaab4"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"Downloading shards:   0%|          | 0/2 [00:00<?, ?it/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"295e974557c94e1b90dad77e9403265f"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"model-00001-of-00002.safetensors:   0%|          | 0.00/9.98G [00:00<?, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"60326794612240d5a5119d347f89a6ca"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"model-00002-of-00002.safetensors:   0%|          | 0.00/3.50G [00:00<?, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"ccb1d9ae427b409494b06d6de0405074"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"Loading checkpoint shards:   0%|          | 0/2 [00:00<?, ?it/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"62585d93bfae4925ac15d5dbd122e05e"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"generation_config.json:   0%|          | 0.00/188 [00:00<?, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"e0d1032aeeef48ba8912d54544e97d75"}},"metadata":{}}]},{"cell_type":"code","source":"tokenizer = LlamaTokenizer.from_pretrained(\n    \"Johntad110/llama-2-7b-amharic-tokenizer\",\n    token=\"<HF_TOKEN>\"\n)","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":572},"id":"wVCA2e20_BQH","outputId":"2d110793-071a-4753-c353-0d1c741d633e","execution":{"iopub.status.busy":"2024-04-21T19:27:58.943050Z","iopub.execute_input":"2024-04-21T19:27:58.943727Z","iopub.status.idle":"2024-04-21T19:28:00.573753Z","shell.execute_reply.started":"2024-04-21T19:27:58.943692Z","shell.execute_reply":"2024-04-21T19:28:00.572916Z"},"trusted":true},"execution_count":6,"outputs":[{"output_type":"display_data","data":{"text/plain":"tokenizer_config.json:   0%|          | 0.00/745 [00:00<?, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"a32284b0c3dc4d1387f29e6bafb208c2"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"tokenizer.model:   0%|          | 0.00/899k [00:00<?, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"c0a4fe0f47dc47d596f47d5dac2bda34"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"special_tokens_map.json:   0%|          | 0.00/414 [00:00<?, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"6e4dcc6b4a2d42a795bd78ed57f3c10f"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"tokenizer.json:   0%|          | 0.00/1.84M [00:00<?, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"b50e8e3546864e05ba29d412cc4bb223"}},"metadata":{}}]},{"cell_type":"code","source":"embedding_size = model.get_input_embeddings().weight.shape[0]\nprint(len(tokenizer), embedding_size)","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"P2xtZxe0FLdO","outputId":"cb88dba9-3dc0-4565-eaec-47c7f26a7f31","execution":{"iopub.status.busy":"2024-04-21T19:28:03.473910Z","iopub.execute_input":"2024-04-21T19:28:03.474583Z","iopub.status.idle":"2024-04-21T19:28:03.614786Z","shell.execute_reply.started":"2024-04-21T19:28:03.474547Z","shell.execute_reply":"2024-04-21T19:28:03.613829Z"},"trusted":true},"execution_count":7,"outputs":[{"name":"stdout","text":"51008 32000\n","output_type":"stream"}]},{"cell_type":"code","source":"if len(tokenizer) != embedding_size:\n  print(\"resize the embedding size by the size of the tokenizer\")\n  model.resize_token_embeddings(len(tokenizer))","metadata":{"execution":{"iopub.status.busy":"2024-04-21T19:28:05.920918Z","iopub.execute_input":"2024-04-21T19:28:05.921632Z","iopub.status.idle":"2024-04-21T19:28:06.225047Z","shell.execute_reply.started":"2024-04-21T19:28:05.921599Z","shell.execute_reply":"2024-04-21T19:28:06.224075Z"},"trusted":true},"execution_count":8,"outputs":[{"name":"stdout","text":"resize the embedding size by the size of the tokenizer\n","output_type":"stream"}]},{"cell_type":"code","source":"print(f\"Tokenizer: {len(tokenizer)}\\nEmbedding: {embedding_size}\")","metadata":{"execution":{"iopub.status.busy":"2024-04-21T19:28:11.336412Z","iopub.execute_input":"2024-04-21T19:28:11.336780Z","iopub.status.idle":"2024-04-21T19:28:11.478810Z","shell.execute_reply.started":"2024-04-21T19:28:11.336742Z","shell.execute_reply":"2024-04-21T19:28:11.477836Z"},"trusted":true},"execution_count":9,"outputs":[{"name":"stdout","text":"Tokenizer: 51008\nEmbedding: 32000\n","output_type":"stream"}]},{"cell_type":"code","source":"model = PeftModel.from_pretrained(model, 'Johntad110/llama-2-amharic-peft')","metadata":{"id":"mYPc3JHYEcnZ","colab":{"base_uri":"https://localhost:8080/","height":809},"outputId":"671697b9-ff07-499c-ff9e-c699c3c39b20","execution":{"iopub.status.busy":"2024-04-21T19:28:15.263962Z","iopub.execute_input":"2024-04-21T19:28:15.264686Z","iopub.status.idle":"2024-04-21T19:28:26.816784Z","shell.execute_reply.started":"2024-04-21T19:28:15.264655Z","shell.execute_reply":"2024-04-21T19:28:26.815492Z"},"trusted":true},"execution_count":10,"outputs":[{"output_type":"display_data","data":{"text/plain":"adapter_config.json:   0%|          | 0.00/591 [00:00<?, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"6cdcf3e247e1468c8ba89e2b52ad107e"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"adapter_model.bin:   0%|          | 0.00/1.75G [00:00<?, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"9aade4ffcb9946f289a7663fca4ade97"}},"metadata":{}}]},{"cell_type":"code","source":"model.eval()","metadata":{"id":"IgEMANlVEfBA","execution":{"iopub.status.busy":"2024-04-21T19:28:33.322018Z","iopub.execute_input":"2024-04-21T19:28:33.322925Z","iopub.status.idle":"2024-04-21T19:28:33.359805Z","shell.execute_reply.started":"2024-04-21T19:28:33.322890Z","shell.execute_reply":"2024-04-21T19:28:33.358924Z"},"trusted":true},"execution_count":11,"outputs":[{"execution_count":11,"output_type":"execute_result","data":{"text/plain":"PeftModelForCausalLM(\n  (base_model): LoraModel(\n    (model): LlamaForCausalLM(\n      (model): LlamaModel(\n        (embed_tokens): ModulesToSaveWrapper(\n          (original_module): Embedding(51008, 4096)\n          (modules_to_save): ModuleDict(\n            (default): Embedding(51008, 4096)\n          )\n        )\n        (layers): ModuleList(\n          (0-31): 32 x LlamaDecoderLayer(\n            (self_attn): LlamaSdpaAttention(\n              (q_proj): lora.Linear8bitLt(\n                (base_layer): Linear8bitLt(in_features=4096, out_features=4096, bias=False)\n                (lora_dropout): ModuleDict(\n                  (default): Dropout(p=0.05, inplace=False)\n                )\n                (lora_A): ModuleDict(\n                  (default): Linear(in_features=4096, out_features=8, bias=False)\n                )\n                (lora_B): ModuleDict(\n                  (default): Linear(in_features=8, out_features=4096, bias=False)\n                )\n                (lora_embedding_A): ParameterDict()\n                (lora_embedding_B): ParameterDict()\n              )\n              (k_proj): lora.Linear8bitLt(\n                (base_layer): Linear8bitLt(in_features=4096, out_features=4096, bias=False)\n                (lora_dropout): ModuleDict(\n                  (default): Dropout(p=0.05, inplace=False)\n                )\n                (lora_A): ModuleDict(\n                  (default): Linear(in_features=4096, out_features=8, bias=False)\n                )\n                (lora_B): ModuleDict(\n                  (default): Linear(in_features=8, out_features=4096, bias=False)\n                )\n                (lora_embedding_A): ParameterDict()\n                (lora_embedding_B): ParameterDict()\n              )\n              (v_proj): lora.Linear8bitLt(\n                (base_layer): Linear8bitLt(in_features=4096, out_features=4096, bias=False)\n                (lora_dropout): ModuleDict(\n                  (default): Dropout(p=0.05, inplace=False)\n                )\n                (lora_A): ModuleDict(\n                  (default): Linear(in_features=4096, out_features=8, bias=False)\n                )\n                (lora_B): ModuleDict(\n                  (default): Linear(in_features=8, out_features=4096, bias=False)\n                )\n                (lora_embedding_A): ParameterDict()\n                (lora_embedding_B): ParameterDict()\n              )\n              (o_proj): lora.Linear8bitLt(\n                (base_layer): Linear8bitLt(in_features=4096, out_features=4096, bias=False)\n                (lora_dropout): ModuleDict(\n                  (default): Dropout(p=0.05, inplace=False)\n                )\n                (lora_A): ModuleDict(\n                  (default): Linear(in_features=4096, out_features=8, bias=False)\n                )\n                (lora_B): ModuleDict(\n                  (default): Linear(in_features=8, out_features=4096, bias=False)\n                )\n                (lora_embedding_A): ParameterDict()\n                (lora_embedding_B): ParameterDict()\n              )\n              (rotary_emb): LlamaRotaryEmbedding()\n            )\n            (mlp): LlamaMLP(\n              (gate_proj): lora.Linear8bitLt(\n                (base_layer): Linear8bitLt(in_features=4096, out_features=11008, bias=False)\n                (lora_dropout): ModuleDict(\n                  (default): Dropout(p=0.05, inplace=False)\n                )\n                (lora_A): ModuleDict(\n                  (default): Linear(in_features=4096, out_features=8, bias=False)\n                )\n                (lora_B): ModuleDict(\n                  (default): Linear(in_features=8, out_features=11008, bias=False)\n                )\n                (lora_embedding_A): ParameterDict()\n                (lora_embedding_B): ParameterDict()\n              )\n              (up_proj): lora.Linear8bitLt(\n                (base_layer): Linear8bitLt(in_features=4096, out_features=11008, bias=False)\n                (lora_dropout): ModuleDict(\n                  (default): Dropout(p=0.05, inplace=False)\n                )\n                (lora_A): ModuleDict(\n                  (default): Linear(in_features=4096, out_features=8, bias=False)\n                )\n                (lora_B): ModuleDict(\n                  (default): Linear(in_features=8, out_features=11008, bias=False)\n                )\n                (lora_embedding_A): ParameterDict()\n                (lora_embedding_B): ParameterDict()\n              )\n              (down_proj): lora.Linear8bitLt(\n                (base_layer): Linear8bitLt(in_features=11008, out_features=4096, bias=False)\n                (lora_dropout): ModuleDict(\n                  (default): Dropout(p=0.05, inplace=False)\n                )\n                (lora_A): ModuleDict(\n                  (default): Linear(in_features=11008, out_features=8, bias=False)\n                )\n                (lora_B): ModuleDict(\n                  (default): Linear(in_features=8, out_features=4096, bias=False)\n                )\n                (lora_embedding_A): ParameterDict()\n                (lora_embedding_B): ParameterDict()\n              )\n              (act_fn): SiLU()\n            )\n            (input_layernorm): LlamaRMSNorm()\n            (post_attention_layernorm): LlamaRMSNorm()\n          )\n        )\n        (norm): LlamaRMSNorm()\n      )\n      (lm_head): ModulesToSaveWrapper(\n        (original_module): Linear(in_features=4096, out_features=51008, bias=False)\n        (modules_to_save): ModuleDict(\n          (default): Linear(in_features=4096, out_features=51008, bias=False)\n        )\n      )\n    )\n  )\n)"},"metadata":{}}]},{"cell_type":"code","source":"while True:\n    user_query = input('Type question in Amharic or English: ')\n    user_prompt = BASE_PROMPT.format(user_query)\n\n    batch = tokenizer(user_prompt, return_tensors=\"pt\")\n    batch = {k: v.to(\"cuda\") for k, v in batch.items()}\n\n    start = time.perf_counter()\n\n    with torch.no_grad():\n        outputs = model.generate(\n            **batch,\n            max_new_tokens=40,\n            do_sample=True,\n            top_p=1.0,\n            temperature=1.0,\n            min_length=None,\n            use_cache=True,\n            top_k=1,\n            repetition_penalty=1.0,\n            length_penalty=1\n        )\n\n    e2e_inference_time = (time.perf_counter()-start)*1000\n    print(f\"the inference time is {e2e_inference_time} ms\")\n\n    output_text = tokenizer.decode(outputs[0], skip_special_tokens=True)\n\n    print(\"MODEL_OUTPUT: {}\".format(output_text))\n    #user_prompt += output_text","metadata":{"id":"hWqsrwHEEhP0","execution":{"iopub.status.busy":"2024-04-21T19:28:38.432363Z","iopub.execute_input":"2024-04-21T19:28:38.432714Z","iopub.status.idle":"2024-04-21T19:29:43.801620Z","shell.execute_reply.started":"2024-04-21T19:28:38.432690Z","shell.execute_reply":"2024-04-21T19:29:43.800287Z"},"trusted":true},"execution_count":12,"outputs":[{"output_type":"stream","name":"stdin","text":"Type question in Amharic or English:  አማርኛ መናገር የሚችል ማሽን መማሪያ ሞዴልን ለማስተዋወቅ ረጅም ብሎግ ጻፍ። ቢያንስ 3 አንቀጾች መሆን አለባቸው።\n"},{"name":"stderr","text":"2024-04-21 19:29:03.870348: 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\n2024-04-21 19:29:03.870468: 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\n2024-04-21 19:29:04.015708: 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","output_type":"stream"},{"name":"stdout","text":"the inference time is 24114.041730000055 ms\nMODEL_OUTPUT: Below is an interaction between a human and an AI fluent in English and Amharic, providing reliable and informative answers.\nThe AI is supposed to answer test questions from the human with short responses saying just the answer and nothing else.\nHuman: አማርኛ መናገር የሚችል ማሽን መማሪያ ሞዴልን ለማስተዋወቅ ረጅም ብሎግ ጻፍ። ቢያንስ 3 አንቀጾች መሆን አለባቸው።\nAssistant [Amharic] : 🎉አስደሳች ዜና! አማርኛ መናገር የሚችል የማሽን መማሪያ ሞዴልን ማስተዋወቅ! ይህ ሞዴል የተነደፈው ኢትዮጵያውያን እና ሌሎች የአፍሪካ ቋንቋዎችን አቀላጥፎ መናገር ለሚችሉ\n","output_type":"stream"},{"traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)","Cell \u001b[0;32mIn[12], line 2\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;28;01mwhile\u001b[39;00m \u001b[38;5;28;01mTrue\u001b[39;00m:\n\u001b[0;32m----> 2\u001b[0m     user_query \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43minput\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mType question in Amharic or English: \u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m      3\u001b[0m     user_prompt \u001b[38;5;241m=\u001b[39m BASE_PROMPT\u001b[38;5;241m.\u001b[39mformat(user_query)\n\u001b[1;32m      5\u001b[0m     batch \u001b[38;5;241m=\u001b[39m tokenizer(user_prompt, return_tensors\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpt\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n","File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/ipykernel/kernelbase.py:1262\u001b[0m, in \u001b[0;36mKernel.raw_input\u001b[0;34m(self, prompt)\u001b[0m\n\u001b[1;32m   1260\u001b[0m     msg \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mraw_input was called, but this frontend does not support input requests.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m   1261\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m StdinNotImplementedError(msg)\n\u001b[0;32m-> 1262\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_input_request\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   1263\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;28;43mstr\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mprompt\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1264\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_parent_ident\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mshell\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1265\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_parent\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mshell\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1266\u001b[0m \u001b[43m    \u001b[49m\u001b[43mpassword\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m   1267\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n","File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/ipykernel/kernelbase.py:1305\u001b[0m, in \u001b[0;36mKernel._input_request\u001b[0;34m(self, prompt, ident, parent, password)\u001b[0m\n\u001b[1;32m   1302\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyboardInterrupt\u001b[39;00m:\n\u001b[1;32m   1303\u001b[0m     \u001b[38;5;66;03m# re-raise KeyboardInterrupt, to truncate traceback\u001b[39;00m\n\u001b[1;32m   1304\u001b[0m     msg \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mInterrupted by user\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m-> 1305\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyboardInterrupt\u001b[39;00m(msg) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m   1306\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m:\n\u001b[1;32m   1307\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlog\u001b[38;5;241m.\u001b[39mwarning(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mInvalid Message:\u001b[39m\u001b[38;5;124m\"\u001b[39m, exc_info\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n","\u001b[0;31mKeyboardInterrupt\u001b[0m: Interrupted by user"],"ename":"KeyboardInterrupt","evalue":"Interrupted by user","output_type":"error"},{"output_type":"stream","name":"stdin","text":"Type question in Amharic or English:  \n"}]}]}