{ "cells": [ { "cell_type": "markdown", "source": [ "\n", "loading the model into colab content/\n", "---\n", "\n" ], "metadata": { "id": "ZtH1IyfvDP6M" }, "id": "ZtH1IyfvDP6M" }, { "cell_type": "code", "execution_count": 1, "id": "b83d199f-4bd9-47bd-8be6-f9d0c802c570", "metadata": { "id": "b83d199f-4bd9-47bd-8be6-f9d0c802c570" }, "outputs": [], "source": [ "# URL from which you're downloading the model\n", "#url = \"https://huggingface.co/ssoh/llama-2-7b-mcq_2-GGUF/resolve/main/llama-2-7b-mcq_2.Q5_K_M.gguf\" #new\n", "url = \"https://huggingface.co/ssoh/llama-2-7b-mini-ibased-GGUF/resolve/main/llama-2-7b-mini-ibased.Q5_K_M.gguf\" #old" ] }, { "cell_type": "code", "execution_count": 2, "id": "ab25420c-aef0-48f2-a602-2fe89d6e64b3", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "ab25420c-aef0-48f2-a602-2fe89d6e64b3", "outputId": "91395c3f-078f-4e3d-9ee8-adc9175ac9ed" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "--2024-02-22 07:54:45-- 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https://cdn-lfs-us-1.huggingface.co/repos/a7/16/a716a6f7d3f2fa140d2f0263054d2bc120c1eca46172da4411fa02e97e0236bc/1fad558a8c0c265b3f1ef73559d401fdde00a1945e632c8c7523c066002aac4a?response-content-disposition=attachment%3B+filename*%3DUTF-8%27%27llama-2-7b-mini-ibased.Q5_K_M.gguf%3B+filename%3D%22llama-2-7b-mini-ibased.Q5_K_M.gguf%22%3B&Expires=1708847685&Policy=eyJTdGF0ZW1lbnQiOlt7IkNvbmRpdGlvbiI6eyJEYXRlTGVzc1RoYW4iOnsiQVdTOkVwb2NoVGltZSI6MTcwODg0NzY4NX19LCJSZXNvdXJjZSI6Imh0dHBzOi8vY2RuLWxmcy11cy0xLmh1Z2dpbmdmYWNlLmNvL3JlcG9zL2E3LzE2L2E3MTZhNmY3ZDNmMmZhMTQwZDJmMDI2MzA1NGQyYmMxMjBjMWVjYTQ2MTcyZGE0NDExZmEwMmU5N2UwMjM2YmMvMWZhZDU1OGE4YzBjMjY1YjNmMWVmNzM1NTlkNDAxZmRkZTAwYTE5NDVlNjMyYzhjNzUyM2MwNjYwMDJhYWM0YT9yZXNwb25zZS1jb250ZW50LWRpc3Bvc2l0aW9uPSoifV19&Signature=XwIDL78K38n3cjftylJtmNyj47tcB1jWzoBxOli3ZuGOoQCMfn7AW0kU8x3Ez%7EyytYSsRzI3h5ymQJQtXOM5CfBQ5xRP0EN3u%7E2ZPd8R-OeI-zVeExg6RouzZKwDP-yKSh9DHPvX8hal9mJ1GL376wvLhDFc2z13lvKYzywSj0BVgLa40I7CCbjzsAgSo9Ix5CGD8XjDkwEZEKOMHMLLudGuzakEhQFeBWa8AubPZ2jB%7ELp1A7FPgvvcZmXUIh4s9LCB2FSbsvVftoMNW5Q6hEQHzZ2ZYKHi5vOu4kSK8%7EFjU9cEe2fZYTdI0ABh-SxTnyBLYXEL%7EL00aZu5kAC7oA__&Key-Pair-Id=KCD77M1F0VK2B [following]\n", "--2024-02-22 07:54:45-- 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\u001b[?25l\u001b[?25hdone\n", " Building wheel for pypika (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n" ] } ] }, { "cell_type": "code", "source": [ "#list of librarys for requirement.txt\n", "from langchain.document_loaders import PyPDFLoader\n", "\n", "# Import embeddings module from langchain for vector representations of text\n", "from langchain.embeddings import HuggingFaceEmbeddings\n", "\n", "# Import text splitter for handling large texts\n", "from langchain.text_splitter import CharacterTextSplitter\n", "\n", "# Import vector store for database operations\n", "from langchain.vectorstores import Chroma\n", "\n", "# for loading of llama gguf model\n", "from langchain.llms import LlamaCpp\n", "\n", "from langchain.chains.router.llm_router import LLMRouterChain, RouterOutputParser\n", "from langchain.chains.router.multi_prompt_prompt import MULTI_PROMPT_ROUTER_TEMPLATE\n", "\n", "from langchain.memory import VectorStoreRetrieverMemory #new" ], "metadata": { "id": "vmZabofLs7k8" }, "id": "vmZabofLs7k8", "execution_count": 4, "outputs": [] }, { "cell_type": "code", "source": [ "from langchain.chains.router import MultiPromptChain\n", "from langchain.chains import ConversationChain\n", "from langchain.chains.llm import LLMChain\n", "from langchain.prompts import PromptTemplate\n", "from langchain.memory import ConversationBufferMemory\n", "from langchain.chains import ConversationalRetrievalChain\n", "\n", "from langchain.callbacks.manager import CallbackManager\n", "from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler" ], "metadata": { "id": "HzoRZHljSHal" }, "id": "HzoRZHljSHal", "execution_count": 5, "outputs": [] }, { "cell_type": "code", "source": [ "#Step 1, call this function once to instantiate it, this is to convert pdf into chromadb collection_name has no use for now\n", "def pdf_to_vec(filename):\n", " document = []\n", " loader = PyPDFLoader(filename)\n", " document.extend(loader.load()) #which library is this from?\n", "\n", " # Initialize HuggingFaceEmbeddings with the 'sentence-transformers/all-MiniLM-L6-v2' model for generating text embeddings\n", " embeddings = HuggingFaceEmbeddings(model_name='sentence-transformers/all-MiniLM-L6-v2')\n", "\n", " # Initialize a CharacterTextSplitter to split the loaded documents into smaller chunks\n", " document_splitter = CharacterTextSplitter(separator='\\n', chunk_size=500, chunk_overlap=100)\n", "\n", " # Use the splitter to divide the 'document' content into manageable chunks\n", " document_chunks = document_splitter.split_documents(document) #which library is this from?\n", "\n", " # Create a Chroma vector database from the document chunks with the specified embeddings, and set a directory for persistence\n", " vectordb = Chroma.from_documents(document_chunks, embedding=embeddings, persist_directory='./data') ## change to GUI path\n", "\n", " # Persist the created vector database to disk in the specified directory\n", " vectordb.persist() #this is mandatory?\n", "\n", " return(vectordb)\n", " #return collection # Return the collection as the asset" ], "metadata": { "id": "5Cie9D5ms7r_" }, "id": "5Cie9D5ms7r_", "execution_count": 6, "outputs": [] }, { "cell_type": "code", "source": [ "#step 2 upload the file and return vectordb for langchain\n", "#https://drive.google.com/file/d/1tkX0lNFidzaoK4V1sm1qvkjWkcfZjDuc/view?usp=drive_link\n", "\n", "vectordb = pdf_to_vec(\"DEUSCHLE-SENIORTHESIS-2019.pdf\") #filepath to pdf in application" ], "metadata": { "id": "Cctgk99HvVvu", "colab": { "base_uri": "https://localhost:8080/", "height": 528, "referenced_widgets": [ "56af2aa38eca4f46851274669bda9135", "94df95412a7c421597354857d2a0eb89", "a74b5a51e0304252af1202931d94c9e5", "37273fb7709b4129a34ce84b9c95c4e1", "d7c5e1bd48c645aba81fa10273625db5", "75b729b465774abea5a5b22a1750cb59", "0b9b7411cfc1460596b0575c132d9839", "ddf4d8cea4a449a0ad55933ff339f8ca", "7dc8d5de21c04e61b1d052d9ad181630", "05fe3202124844c09402cce6a88ab458", 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"687dd5bdc953477a8980c48dd6b50c6e", "e4395f7bbcff4c0e82995d85578d066d", "21d061dcaae54fdb955f3dc5159b383a", "03e769503bea495d9d6c3b6371984438", "ea6074f07cab4d9696236d7c26115283", "1043ae5729774f178ac07b6714db0174", "527c155fd38449829e8095e64dede1bd", "c38b07391b9d439ba59892fb9a5007c1", "d133ce95579748a581c15e941b42fdf3", "a63c0c7ebbf04eb6be25aa79a03b72b2", "0c6805b9a106460e88caf4f6e854174c", "7e19408459a24cbdb3f0f34673df8fc5", "fa0fcb6af61840d3a7ae3ae0c7398413", "31f97d1415cf47f8ac4d6ae5161b26d6", "916d4bb9e51a4a559e8e306cc731bf02", "385e146658714ade958348cd00bd983d", "a1298b595ade4b57bdbe1d691a57a005", "32ded7f870b543ef949ccc8298b6366b", "60971c54dd3745de9b030ed1c68ec347", "31d477e7bbf64d5da099a2ddc220b041", "00061d9e9e974da59498e335243bbeb6", "47b7c8cef74948b38e7e680b7ee2b5ba", "5e1d72dd82894042a30bc7f553b2f3c9", "3e6a018670104950bbd81db36004ae7b", "14c0c114e39a44659dd436247f4e2ea6", "905ecc24d2e243ada92a0530089e7750", "1febee6256bf435384c4cfc0e084e600", "b37d3fb184b24f8190f7432c14bc76f9", "8d86a85fe5924ad39a416adf7b803974", "d502cd41b71f4a1c8d031025c4991e2a", "eab09fc7c565478dafe0c29aa6258f4b", "3bc8fbd9c32a4b7a908cadcd353d4f46", "0cebd09286804072b4426759f7e364b6", "bace080952e942fa9cea706e25d5c7fa", "ebe6d3ee30944d5d8369e752aae39783", "88856523500645e89f633eff2b72c8f8", "adc2f01d3c364253b8619bfdbf49ec76", "e926b56418204199b32b548d07798ac0", "2dd023eecfa04ea5b168c4a216c1f375", "90440b84a782492ca4b4b36efa88c8ff", "e913f40cd9d644d3a5163ff688d22c9c", "4f95597d0d444b05b77ce310568afba2", "91766aff8df64774a0ebecee48301667", "8260b76988be49d185c62326bcbdc0f7", "b7690f09eeaa4347ba9e9197dc4743d4", "6cbf652b9e7740dd8f196ee62bb0d0ee", "198fc260e8ae45348710aac8d27c611a", "4af645a282eb4264a23283b3e8f33f22", "8aa6b00a371f48e69356e192cf8d247d", "9cd0ce4bfd2e405b83d68325c3985d8e", "f3b4db4e62bb42a5a34440ae32be470d", "026c4ead28964a51a1de48ce1e7e2f3d", "d20dc23698db40f5bbeb5981b69ada25", "573a0464226c418fb8607fdb320a6d84", "d4473109afa6407e9f58d73c8ce6e715", "84d3b2e43ec543309ef44f62a4a0e2f6", "f3bd31b4d32446ef913a704a37584429", "45f9ea1d9ada4f3989e534575c130814", "d5a4e7b297914911a335c43ab0229966", "29594fbb2d924379936ad571e79739b9", "27b8be4bc6614ed5b1b5fa9e818f879e", "a9ddeaa7d9f14c8c98cd6c093bd58dd1", "8b59e3b750b84837a5adc95b948210a9", "2b818f8f715f4b1e8172e9e7f6ad68c6", "18750f0ae1c045b4b599597bb64a3633", "b2b86743bcf145069cec3310df056abb", "81559a92eabe49b3b6ef508fff7695a4", "967fac20d88340c1bc0d05f751e550ed", "4fb73911ae464a3b840c5d5fe830459a", "ccdc53ba51554509afa78e19240baf5a", "25eb57496e4e4f4ebc491f1e10bd4626", "34f54e0f181248e5911eda0b81cb003d", "e225e7f9a65d46cdb8c255c150ef61fb", "bb6d95c0f7b24ef9b7afc08c565ddd6e", "b0c1b6dd77b844f0b92a0695db8868b9", "1a68194ed30f4fa99c934f36fc6ee9a4", "c410794eb26841febc337c28f80b6ccc", "f1a781cd7a46445c8925f515c9b6e3a8", "2711401df356414e88734d0d4c249c99", "331f83f1c42f4d7eb3c7094d01e80cdb", "ae64ab81e223431fa90ef46bbeb56832", "a1522e4b604544009627e70f66bc13af", "6aaaa994839b43a2ba5f62babb18c88c", "612f2baa2e0f4956a96f4ee96440b281" ] }, "outputId": "ee07c185-cfc1-479b-90c5-5d5a8d74134a" }, "id": "Cctgk99HvVvu", "execution_count": 7, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:88: UserWarning: \n", "The secret `HF_TOKEN` does not exist in your Colab secrets.\n", "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n", "You will be able to reuse this secret in all of your notebooks.\n", "Please note that authentication is recommended but still optional to access public models or datasets.\n", " warnings.warn(\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "modules.json: 0%| | 0.00/349 [00:00\", \"\", \"\", \"<0x00>\", \"<...\n", "llama_model_loader: - kv 14: tokenizer.ggml.scores arr[f32,32000] = [0.000000, 0.000000, 0.000000, 0.0000...\n", "llama_model_loader: - kv 15: tokenizer.ggml.token_type arr[i32,32000] = [2, 3, 3, 6, 6, 6, 6, 6, 6, 6, 6, 6, ...\n", "llama_model_loader: - kv 16: tokenizer.ggml.bos_token_id u32 = 1\n", "llama_model_loader: - kv 17: tokenizer.ggml.eos_token_id u32 = 2\n", "llama_model_loader: - kv 18: tokenizer.ggml.unknown_token_id u32 = 0\n", "llama_model_loader: - kv 19: tokenizer.ggml.padding_token_id u32 = 2\n", "llama_model_loader: - kv 20: tokenizer.ggml.add_bos_token bool = true\n", "llama_model_loader: - kv 21: tokenizer.ggml.add_eos_token bool = false\n", "llama_model_loader: - kv 22: tokenizer.chat_template str = {% if messages[0]['role'] == 'system'...\n", "llama_model_loader: - kv 23: general.quantization_version u32 = 2\n", "llama_model_loader: - type f32: 65 tensors\n", "llama_model_loader: - type q5_K: 193 tensors\n", "llama_model_loader: - type q6_K: 33 tensors\n", "llm_load_vocab: special tokens definition check successful ( 259/32000 ).\n", "llm_load_print_meta: format = GGUF V3 (latest)\n", "llm_load_print_meta: arch = llama\n", "llm_load_print_meta: vocab type = SPM\n", "llm_load_print_meta: n_vocab = 32000\n", "llm_load_print_meta: n_merges = 0\n", "llm_load_print_meta: n_ctx_train = 4096\n", "llm_load_print_meta: n_embd = 4096\n", "llm_load_print_meta: n_head = 32\n", "llm_load_print_meta: n_head_kv = 32\n", "llm_load_print_meta: n_layer = 32\n", "llm_load_print_meta: n_rot = 128\n", "llm_load_print_meta: n_embd_head_k = 128\n", "llm_load_print_meta: n_embd_head_v = 128\n", "llm_load_print_meta: n_gqa = 1\n", "llm_load_print_meta: n_embd_k_gqa = 4096\n", "llm_load_print_meta: n_embd_v_gqa = 4096\n", "llm_load_print_meta: f_norm_eps = 0.0e+00\n", "llm_load_print_meta: f_norm_rms_eps = 1.0e-05\n", "llm_load_print_meta: f_clamp_kqv = 0.0e+00\n", "llm_load_print_meta: f_max_alibi_bias = 0.0e+00\n", "llm_load_print_meta: n_ff = 11008\n", "llm_load_print_meta: n_expert = 0\n", "llm_load_print_meta: n_expert_used = 0\n", "llm_load_print_meta: rope scaling = linear\n", "llm_load_print_meta: freq_base_train = 10000.0\n", "llm_load_print_meta: freq_scale_train = 1\n", "llm_load_print_meta: n_yarn_orig_ctx = 4096\n", "llm_load_print_meta: rope_finetuned = unknown\n", "llm_load_print_meta: model type = 7B\n", "llm_load_print_meta: model ftype = Q5_K - Medium\n", "llm_load_print_meta: model params = 6.74 B\n", "llm_load_print_meta: model size = 4.45 GiB (5.68 BPW) \n", "llm_load_print_meta: general.name = LLaMA v2\n", "llm_load_print_meta: BOS token = 1 ''\n", "llm_load_print_meta: EOS token = 2 ''\n", "llm_load_print_meta: UNK token = 0 ''\n", "llm_load_print_meta: PAD token = 2 ''\n", "llm_load_print_meta: LF token = 13 '<0x0A>'\n", "llm_load_tensors: ggml ctx size = 0.11 MiB\n", "llm_load_tensors: CPU buffer size = 4560.87 MiB\n", "..................................................................................................\n", "llama_new_context_with_model: n_ctx = 512\n", "llama_new_context_with_model: freq_base = 10000.0\n", "llama_new_context_with_model: freq_scale = 1\n", "llama_kv_cache_init: CPU KV buffer size = 256.00 MiB\n", "llama_new_context_with_model: KV self size = 256.00 MiB, K (f16): 128.00 MiB, V (f16): 128.00 MiB\n", "llama_new_context_with_model: CPU input buffer size = 10.01 MiB\n", "llama_new_context_with_model: CPU compute buffer size = 70.50 MiB\n", "llama_new_context_with_model: graph splits (measure): 1\n", "AVX = 1 | AVX_VNNI = 0 | AVX2 = 1 | AVX512 = 1 | AVX512_VBMI = 0 | AVX512_VNNI = 0 | FMA = 1 | NEON = 0 | ARM_FMA = 0 | F16C = 1 | FP16_VA = 0 | WASM_SIMD = 0 | BLAS = 0 | SSE3 = 1 | SSSE3 = 1 | VSX = 0 | MATMUL_INT8 = 0 | \n", "Model metadata: {'tokenizer.chat_template': \"{% if messages[0]['role'] == 'system' %}{% set loop_messages = messages[1:] %}{% set system_message = messages[0]['content'] %}{% else %}{% set loop_messages = messages %}{% set system_message = false %}{% endif %}{% for message in loop_messages %}{% if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}{{ raise_exception('Conversation roles must alternate user/assistant/user/assistant/...') }}{% endif %}{% if loop.index0 == 0 and system_message != false %}{% set content = '<>\\\\n' + system_message + '\\\\n<>\\\\n\\\\n' + message['content'] %}{% else %}{% set content = message['content'] %}{% endif %}{% if message['role'] == 'user' %}{{ bos_token + '[INST] ' + content.strip() + ' [/INST]' }}{% elif message['role'] == 'assistant' %}{{ ' ' + content.strip() + ' ' + eos_token }}{% endif %}{% endfor %}\", 'tokenizer.ggml.add_eos_token': 'false', 'tokenizer.ggml.padding_token_id': '2', 'tokenizer.ggml.unknown_token_id': '0', 'tokenizer.ggml.eos_token_id': '2', 'general.architecture': 'llama', 'llama.rope.freq_base': '10000.000000', 'llama.context_length': '4096', 'general.name': 'LLaMA v2', 'tokenizer.ggml.add_bos_token': 'true', 'llama.embedding_length': '4096', 'llama.feed_forward_length': '11008', 'llama.attention.layer_norm_rms_epsilon': '0.000010', 'llama.rope.dimension_count': '128', 'tokenizer.ggml.bos_token_id': '1', 'llama.attention.head_count': '32', 'llama.block_count': '32', 'llama.attention.head_count_kv': '32', 'general.quantization_version': '2', 'tokenizer.ggml.model': 'llama', 'general.file_type': '17'}\n", "Using chat template: {% if messages[0]['role'] == 'system' %}{% set loop_messages = messages[1:] %}{% set system_message = messages[0]['content'] %}{% else %}{% set loop_messages = messages %}{% set system_message = false %}{% endif %}{% for message in loop_messages %}{% if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}{{ raise_exception('Conversation roles must alternate user/assistant/user/assistant/...') }}{% endif %}{% if loop.index0 == 0 and system_message != false %}{% set content = '<>\\n' + system_message + '\\n<>\\n\\n' + message['content'] %}{% else %}{% set content = message['content'] %}{% endif %}{% if message['role'] == 'user' %}{{ bos_token + '[INST] ' + content.strip() + ' [/INST]' }}{% elif message['role'] == 'assistant' %}{{ ' ' + content.strip() + ' ' + eos_token }}{% endif %}{% endfor %}\n", "Using chat eos_token: \n", "Using chat bos_token: \n" ] } ] }, { "cell_type": "markdown", "source": [ "https://github.com/langchain-ai/langchain/issues/5163" ], "metadata": { "id": "-fKKCWiVp6cH" }, "id": "-fKKCWiVp6cH" }, { "cell_type": "code", "source": [ "#attempt to resolve MULTI_PROMPT_ROUTER_TEMPLATE class as, LLM output for router chain is not predictable. the class is expecting a standardized json output which LLM may not produce 100% of the time\n", "import json\n", "import langchain\n", "from typing import Any, Dict, List, Optional, Type, cast\n", "\n", "class RouterOutputParser_simple(langchain.schema.BaseOutputParser[Dict[str, str]]):\n", " \"\"\"Parser for output of router chain int he multi-prompt chain.\"\"\"\n", "\n", " default_destination: str = \"DEFAULT\"\n", " next_inputs_type: Type = str\n", " next_inputs_inner_key: str = \"input\"\n", "\n", " def parse(self, text: str) -> Dict[str, Any]:\n", " try:\n", " expected_keys = [\"destination\", \"next_inputs\"]\n", " parsed = json.loads(text) ### this line is changed\n", " if not isinstance(parsed[\"destination\"], str):\n", " raise ValueError(\"Expected 'destination' to be a string.\")\n", " if not isinstance(parsed[\"next_inputs\"], self.next_inputs_type):\n", " raise ValueError(\n", " f\"Expected 'next_inputs' to be {self.next_inputs_type}.\"\n", " )\n", " parsed[\"next_inputs\"] = {self.next_inputs_inner_key: parsed[\"next_inputs\"]}\n", " if (\n", " parsed[\"destination\"].strip().lower()\n", " == self.default_destination.lower()\n", " ):\n", " parsed[\"destination\"] = None\n", " else:\n", " parsed[\"destination\"] = parsed[\"destination\"].strip()\n", " return parsed\n", " except Exception as e:\n", "\n", " f\"Parsing text\\n{text}\\n raised following error:\\n{e}\"\n" ], "metadata": { "id": "aeJtZQJApKA8" }, "id": "aeJtZQJApKA8", "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "#step 5, to instantiate once to create default_chain,router_chain,destination_chains into chain and set vectordb. so will not re-create per prompt\n", "def default_chain():\n", "\n", " sum_template = \"\"\"\n", " As a machine learning education specialist, your expertise is pivotal in deepening the comprehension of complex machine learning concepts for both educators and students.\n", "\n", " Your role entails:\n", "\n", " Providing Detailed Explanations: Deliver comprehensive answers to these questions, elucidating the underlying technical principles.\n", " Assisting in Exam Preparation: Support educators in formulating sophisticated exam and quiz questions, including MCQs, accompanied by thorough explanations.\n", " Summarizing Course Material: Distill key information from course materials, articulating complex ideas within the context of advanced machine learning practices.\n", "\n", " Objective: to summarize and explain the key points.\n", " summary:\n", "\n", " Here the question:\n", " {input}\n", " \"\"\"\n", "\n", " mcq_template = \"\"\"\n", " As a machine learning education specialist, your expertise is pivotal in deepening the comprehension of complex machine learning concepts for both educators and students.\n", "\n", " Your role entails:\n", " Crafting Insightful Questions: Develop thought-provoking questions that explore the intricacies of machine learning topics.\n", " Generating MCQs: Create MCQs for each machine learning topic, comprising a question, four choices (A-D), and the correct answer, along with a rationale explaining the answer.\n", "\n", " Objective: to create multiple choice question in this format\n", " question:\n", " options A:\n", " options B:\n", " options C:\n", " options D:\n", " correct_answer:\n", " explanation:\n", "\n", " Here the question:\n", " {input}\n", " \"\"\"\n", "\n", " prompt_infos = [\n", " {\n", " \"name\": \"SUMMARIZE\",\n", " \"description\": \"Good for summarizing and explaination\",\n", " \"prompt_template\": sum_template,\n", " },\n", " {\n", " \"name\": \"MCQ\",\n", " \"description\": \"Good for creating multiple choices questions mcq\",\n", " \"prompt_template\": mcq_template,\n", " },\n", " ]\n", "\n", " destination_chains = {}\n", "\n", " for p_info in prompt_infos:\n", " name = p_info[\"name\"]\n", " prompt_template = p_info[\"prompt_template\"]\n", " prompt = PromptTemplate(template=prompt_template, input_variables=[\"input\"])\n", " embeddings = HuggingFaceEmbeddings(model_name='sentence-transformers/all-MiniLM-L6-v2')\n", " vectordb= Chroma(persist_directory = './data', embedding_function = embeddings)\n", " retriever = vectordb.as_retriever()\n", " memory = VectorStoreRetrieverMemory(retriever=retriever)\n", "\n", " chain = LLMChain(llm=llm, prompt=prompt, verbose=True, memory=memory)\n", " destination_chains[name] = chain\n", "\n", " default_chain = ConversationalRetrievalChain.from_llm(llm=llm,\n", " retriever=vectordb.as_retriever(search_kwargs={'k': 3}),\n", " verbose=True, output_key=\"text\" )\n", "\n", " destinations = [f\"{p['name']}: {p['description']}\" for p in prompt_infos]\n", " destinations_str = \"\\n\".join(destinations)\n", " print(destinations_str)\n", " router_template = MULTI_PROMPT_ROUTER_TEMPLATE.format(destinations=destinations_str)\n", " router_prompt = PromptTemplate(\n", " template=router_template,\n", " input_variables=[\"input\"],\n", " output_parser=RouterOutputParser(),\n", " )\n", " router_chain = LLMRouterChain.from_llm(llm, router_prompt)\n", " print(\"destination_chains:\",destination_chains)\n", " return default_chain,router_chain,destination_chains" ], "metadata": { "id": "6kRG03GLMR4X" }, "id": "6kRG03GLMR4X", "execution_count": 12, "outputs": [] }, { "cell_type": "code", "source": [ "#step 6, to instantiate once to create default_chain,router_chain,destination_chains into chain and set vectordb. so will not re-create per prompt\n", "\n", "default_chain,router_chain,destination_chains = default_chain()" ], "metadata": { "id": "4OU9i10eMlOE", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "b36ed5e3-af3e-481a-9523-29e0918ed005" }, "id": "4OU9i10eMlOE", "execution_count": 13, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "SUMMARIZE: Good for summarizing and explaination\n", "MCQ: Good for creating multiple choices questions mcq\n", "destination_chains: {'SUMMARIZE': LLMChain(memory=VectorStoreRetrieverMemory(retriever=VectorStoreRetriever(tags=['Chroma', 'HuggingFaceEmbeddings'], vectorstore=)), verbose=True, prompt=PromptTemplate(input_variables=['input'], template='\\n As a machine learning education specialist, your expertise is pivotal in deepening the comprehension of complex machine learning concepts for both educators and students.\\n\\n Your role entails:\\n\\n Providing Detailed Explanations: Deliver comprehensive answers to these questions, elucidating the underlying technical principles.\\n Assisting in Exam Preparation: Support educators in formulating sophisticated exam and quiz questions, including MCQs, accompanied by thorough explanations.\\n Summarizing Course Material: Distill key information from course materials, articulating complex ideas within the context of advanced machine learning practices.\\n\\n Objective: to summarize and explain the key points.\\n summary:\\n\\n Here the question:\\n {input}\\n '), llm=LlamaCpp(client=, model_path='/content/llama-2-7b-mini-ibased.Q5_K_M.gguf', n_batch=512, n_gpu_layers=-1, max_tokens=2000, temperature=0.1, top_p=1.0)), 'MCQ': LLMChain(memory=VectorStoreRetrieverMemory(retriever=VectorStoreRetriever(tags=['Chroma', 'HuggingFaceEmbeddings'], vectorstore=)), verbose=True, prompt=PromptTemplate(input_variables=['input'], template='\\n As a machine learning education specialist, your expertise is pivotal in deepening the comprehension of complex machine learning concepts for both educators and students.\\n\\n Your role entails:\\n Crafting Insightful Questions: Develop thought-provoking questions that explore the intricacies of machine learning topics.\\n Generating MCQs: Create MCQs for each machine learning topic, comprising a question, four choices (A-D), and the correct answer, along with a rationale explaining the answer.\\n\\n Objective: to create multiple choice question in this format\\n question:\\n options A:\\n options B:\\n options C:\\n options D:\\n correct_answer:\\n explanation:\\n\\n Here the question:\\n {input}\\n '), llm=LlamaCpp(client=, model_path='/content/llama-2-7b-mini-ibased.Q5_K_M.gguf', n_batch=512, n_gpu_layers=-1, max_tokens=2000, temperature=0.1, top_p=1.0))}\n" ] } ] }, { "cell_type": "code", "source": [ "#step 7 ,instantiate the function once\n", "def llm_infer(default_chain,router_chain,destination_chains,prompt):\n", "\n", " chain = MultiPromptChain(\n", " router_chain=router_chain,\n", " destination_chains=destination_chains,\n", " default_chain=default_chain,\n", " #memory=ConversationBufferMemory(k=2), # memory_key='chat_history', return_messages=True\n", " verbose=True,\n", " )\n", " print(prompt)\n", " response = chain.run(prompt)\n", "\n", " return response" ], "metadata": { "id": "NdlZrvPjLcGf" }, "id": "NdlZrvPjLcGf", "execution_count": 14, "outputs": [] }, { "cell_type": "code", "source": [ "#step 8a call function using this. replace the prompt string with GUI captured prompt\n", "result= llm_infer(default_chain,router_chain,destination_chains,\"please give me a mcq on Hidden Markov Models\") #input GUI variable" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "hG7NJtBiOSdd", "outputId": "d4fb9ff1-2b63-402e-afdf-c83424484f41" }, "id": "hG7NJtBiOSdd", "execution_count": 18, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "please give me a mcq on Hidden Markov Models\n", "\n", "\n", "\u001b[1m> Entering new MultiPromptChain chain...\u001b[0m\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "/usr/local/lib/python3.10/dist-packages/langchain/chains/llm.py:316: UserWarning: The predict_and_parse method is deprecated, instead pass an output parser directly to LLMChain.\n", " warnings.warn(\n", "Llama.generate: prefix-match hit\n", "\n", "llama_print_timings: load time = 170049.91 ms\n", "llama_print_timings: sample time = 114.06 ms / 208 runs ( 0.55 ms per token, 1823.52 tokens per second)\n", "llama_print_timings: prompt eval time = 163286.83 ms / 303 tokens ( 538.90 ms per token, 1.86 tokens per second)\n", "llama_print_timings: eval time = 147224.43 ms / 207 runs ( 711.23 ms per token, 1.41 tokens per second)\n", "llama_print_timings: total time = 311391.08 ms / 510 tokens\n", "Llama.generate: prefix-match hit\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "MCQ: {'input': 'please give me a mcq on Hidden Markov Models'}\n", "\n", "\u001b[1m> Entering new LLMChain chain...\u001b[0m\n", "Prompt after formatting:\n", "\u001b[32;1m\u001b[1;3m\n", " As a machine learning education specialist, your expertise is pivotal in deepening the comprehension of complex machine learning concepts for both educators and students.\n", "\n", " Your role entails:\n", " Crafting Insightful Questions: Develop thought-provoking questions that explore the intricacies of machine learning topics.\n", " Generating MCQs: Create MCQs for each machine learning topic, comprising a question, four choices (A-D), and the correct answer, along with a rationale explaining the answer.\n", "\n", " Objective: to create multiple choice question in this format\n", " question:\n", " options A:\n", " options B:\n", " options C:\n", " options D:\n", " correct_answer:\n", " explanation:\n", "\n", " Here the question:\n", " please give me a mcq on Hidden Markov Models\n", " \u001b[0m\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "\n", "llama_print_timings: load time = 170049.91 ms\n", "llama_print_timings: sample time = 98.14 ms / 159 runs ( 0.62 ms per token, 1620.12 tokens per second)\n", "llama_print_timings: prompt eval time = 100196.94 ms / 189 tokens ( 530.14 ms per token, 1.89 tokens per second)\n", "llama_print_timings: eval time = 109954.49 ms / 158 runs ( 695.91 ms per token, 1.44 tokens per second)\n", "llama_print_timings: total time = 210829.40 ms / 347 tokens\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "\n", "\u001b[1m> Finished chain.\u001b[0m\n", "\n", "\u001b[1m> Finished chain.\u001b[0m\n" ] } ] }, { "cell_type": "code", "source": [ "#for docker to return to GUI\n", "print(result)" ], "metadata": { "id": "85F0SR9NV7sf", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "a601dde1-5af6-42f1-b2ab-b4862b5cfa99" }, "id": "85F0SR9NV7sf", "execution_count": 19, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "\n", " Response:\n", "\n", " question:\n", " What is the primary difference between a Hidden Markov Model (HMM) and a conventional Markov Model?\n", "\n", " options A: HMM has more states than a conventional Markov Model\n", "\n", " options B: HMM has more transitions than a conventional Markov Model\n", "\n", " options C: HMM has more outputs than a conventional Markov Model\n", "\n", " options D: HMM has more inputs than a conventional Markov Model\n", "\n", " correct_answer: B\n", "\n", " explanation:\n", "\n", " A HMM has more transitions than a conventional Markov Model because an HMM can have multiple states simultaneously, whereas a conventional Markov Model can only be in one state at a time.\n" ] } ] }, { "cell_type": "code", "source": [ "#step 8b call function using this. replace the prompt string with GUI captured prompt\n", "result= llm_infer(default_chain,router_chain,destination_chains,\"please give me a summary Hidden Markov Models\") #input GUI variable" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "01yuooVm27Gs", "outputId": "b04a9f78-1ffc-41aa-c52e-8ac2bc81d8b8" }, "id": "01yuooVm27Gs", "execution_count": 20, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "please give me a summary Hidden Markov Models\n", "\n", "\n", "\u001b[1m> Entering new MultiPromptChain chain...\u001b[0m\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "/usr/local/lib/python3.10/dist-packages/langchain/chains/llm.py:316: UserWarning: The predict_and_parse method is deprecated, instead pass an output parser directly to LLMChain.\n", " warnings.warn(\n", "Llama.generate: prefix-match hit\n", "\n", "llama_print_timings: load time = 170049.91 ms\n", "llama_print_timings: sample time = 108.70 ms / 191 runs ( 0.57 ms per token, 1757.08 tokens per second)\n", "llama_print_timings: prompt eval time = 159612.33 ms / 300 tokens ( 532.04 ms per token, 1.88 tokens per second)\n", "llama_print_timings: eval time = 137167.79 ms / 190 runs ( 721.94 ms per token, 1.39 tokens per second)\n", "llama_print_timings: total time = 297613.67 ms / 490 tokens\n", "Llama.generate: prefix-match hit\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "SUMMARIZE: {'input': 'please give me a summary Hidden Markov Models'}\n", "\n", "\u001b[1m> Entering new LLMChain chain...\u001b[0m\n", "Prompt after formatting:\n", "\u001b[32;1m\u001b[1;3m\n", " As a machine learning education specialist, your expertise is pivotal in deepening the comprehension of complex machine learning concepts for both educators and students.\n", "\n", " Your role entails:\n", "\n", " Providing Detailed Explanations: Deliver comprehensive answers to these questions, elucidating the underlying technical principles.\n", " Assisting in Exam Preparation: Support educators in formulating sophisticated exam and quiz questions, including MCQs, accompanied by thorough explanations.\n", " Summarizing Course Material: Distill key information from course materials, articulating complex ideas within the context of advanced machine learning practices.\n", "\n", " Objective: to summarize and explain the key points.\n", " summary:\n", "\n", " Here the question:\n", " please give me a summary Hidden Markov Models\n", " \u001b[0m\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "\n", "llama_print_timings: load time = 170049.91 ms\n", "llama_print_timings: sample time = 199.50 ms / 328 runs ( 0.61 ms per token, 1644.09 tokens per second)\n", "llama_print_timings: prompt eval time = 96984.80 ms / 183 tokens ( 529.97 ms per token, 1.89 tokens per second)\n", "llama_print_timings: eval time = 230166.40 ms / 327 runs ( 703.87 ms per token, 1.42 tokens per second)\n", "llama_print_timings: total time = 328617.54 ms / 510 tokens\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "\n", "\u001b[1m> Finished chain.\u001b[0m\n", "\n", "\u001b[1m> Finished chain.\u001b[0m\n" ] } ] }, { "cell_type": "code", "source": [ "#for docker to return to GUI\n", "print(result)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "Tv5Mh0EF3Afx", "outputId": "3b09eb81-ee0b-4d24-e8f4-5863e1f20b95" }, "id": "Tv5Mh0EF3Afx", "execution_count": 21, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "\n", " Response:\n", "\n", " Hidden Markov Models (HMMs) are a type of probabilistic model that can be used to represent and analyze sequential data. In an HMM, the underlying system is modeled as a Markov process with unobservable (or hidden) states that generate observable outcomes or observations. The basic idea is that the observations are generated by the underlying states, and the goal is to infer the most likely state sequence that generated the observations.\n", "\n", "HMMs have numerous applications in natural language processing, speech recognition, bioinformatics, and other fields where sequential data is prevalent. They can be used for tasks such as speech recognition, natural language processing, and bioinformatics.\n", "\n", "The key components of an HMM include:\n", "\n", "1. Observation sequence: A sequence of observations generated by the underlying states\n", "2. State sequence: A sequence of hidden states that generate the observations\n", "3. Transition matrix: A matrix that specifies the probability of transitioning from one state to another\n", "4. Emission matrix: A matrix that specifies the probability of observing each symbol given each state\n", "5. Initial state distribution: A probability distribution over the initial states\n", "6. Final state distribution: A probability distribution over the final states\n", "\n", "The basic steps involved in using HMMs are:\n", "\n", "1. Modeling the data: This involves defining the HMM architecture, including the number of states, the observation sequence, and the transition and emission matrices\n", "2. Estimating the model parameters: This involves estim\n" ] } ] }, { "cell_type": "markdown", "source": [ "#END for docker app\n", "\n", "---\n", "\n" ], "metadata": { "id": "c7MYr-BdZCWr" }, "id": "c7MYr-BdZCWr" }, { "cell_type": "code", "source": [ "print(chain.run(\"please give me a mcq on Hidden Markov Models with answer and explaination\")) #with old gguf\n" ], "metadata": { "id": "pfxAx9Qk2WEK" }, "id": "pfxAx9Qk2WEK", "execution_count": null, "outputs": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.11.0" }, "colab": { "provenance": [], "gpuType": "T4" }, "accelerator": "GPU", "widgets": { "application/vnd.jupyter.widget-state+json": { "56af2aa38eca4f46851274669bda9135": { "model_module": "@jupyter-widgets/controls", 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