├── LICENSE ├── README.md ├── .gitignore ├── Function-Calling-Mistral-7B (using mistral API Key).py ├── function_calling_open_source.py └── Function_Calling_Open_Source.ipynb /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2024 AI Anytime 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. 22 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | ## Function Calling Mistral 7B Integration 2 | 3 | This project demonstrates two different approaches to utilizing the MistralAI ChatMistralAI API for generating responses based on user prompts. 4 | 5 | 1. The first approach, implemented in [Function-Calling-Open-Source.ipynb](https://colab.research.google.com/drive/1CgRaeM0RxO1DFNldHMF1ZgzKXpRCnEKX), uses an open-source language model (`teknium/OpenHermes-2.5-Mistral-7B`). It demonstrates how to define Pydantic models for different types of responses, such as book recommendations, jokes, and song recommendations. It also includes functions for loading the model, generating responses based on prompts, and extracting function calls from the generated responses. 6 | 7 | 2. The second approach, implemented in [Function-Calling-Mistral-7B (using mistral API Key).py](#), demonstrates integration with MistralAI API key using langchain ChatMistralAI API. This integration allows the system to interact with MistralAI to generate responses based on user prompts. Similar to the local model approach, Pydantic models are defined for different types of responses, and functions are provided for loading the MistralAI model, generating responses, and extracting function calls from the responses. This approach uses an API key for authentication. 8 | 9 | ### Features 10 | 11 | - **Pydantic Models**: Both approaches use Pydantic models to define the structure of the input prompts and the expected response formats. This allows for easy validation of input data and generation of response objects. 12 | 13 | - **Function Call Extraction**: Both approaches include functions for extracting function calls from the generated responses. This functionality allows the system to identify specific actions or functions requested by the user in the prompts. 14 | 15 | - **API Key Handling**: The MistralAI integration includes a mechanism for loading the API key from a `.env` file, ensuring that the API key is kept secure and not exposed in the code. 16 | 17 | - **Response Generation**: Both approaches demonstrate how to generate responses based on user prompts using the respective language models. The responses include text as well as function calls that can be executed based on the user's request. 18 | 19 | ### Usage 20 | 21 | 1. **Open-Source Approach**: In `Function-Calling-Open-Source.ipynb`, you can use the defined Pydantic models to create instances of requests (such as a book recommendation request or a joke request) and generate responses based on these requests. 22 | 23 | 2. **MistralAI API Key Approach**: In `Function-Calling-Mistral-7B (using mistral API Key).py`, the integration with MistralAI API allows for more sophisticated responses based on the MistralAI model's capabilities. Requests can be made to the MistralAI model, and responses containing text and function calls can be generated. 24 | 25 | Both approaches demonstrate how language models can be used to create interactive systems that can understand user requests and provide relevant responses. 26 | -------------------------------------------------------------------------------- /.gitignore: -------------------------------------------------------------------------------- 1 | # Byte-compiled / optimized / DLL files 2 | __pycache__/ 3 | *.py[cod] 4 | *$py.class 5 | 6 | # C extensions 7 | *.so 8 | 9 | # Distribution / packaging 10 | .Python 11 | build/ 12 | develop-eggs/ 13 | dist/ 14 | downloads/ 15 | eggs/ 16 | .eggs/ 17 | lib/ 18 | lib64/ 19 | parts/ 20 | sdist/ 21 | var/ 22 | wheels/ 23 | share/python-wheels/ 24 | *.egg-info/ 25 | .installed.cfg 26 | *.egg 27 | MANIFEST 28 | 29 | # PyInstaller 30 | # Usually these files are written by a python script from a template 31 | # before PyInstaller builds the exe, so as to inject date/other infos into it. 32 | *.manifest 33 | *.spec 34 | 35 | # Installer logs 36 | pip-log.txt 37 | pip-delete-this-directory.txt 38 | 39 | # Unit test / coverage reports 40 | htmlcov/ 41 | .tox/ 42 | .nox/ 43 | .coverage 44 | .coverage.* 45 | .cache 46 | nosetests.xml 47 | coverage.xml 48 | *.cover 49 | *.py,cover 50 | .hypothesis/ 51 | .pytest_cache/ 52 | cover/ 53 | 54 | # Translations 55 | *.mo 56 | *.pot 57 | 58 | # Django stuff: 59 | *.log 60 | local_settings.py 61 | db.sqlite3 62 | db.sqlite3-journal 63 | 64 | # Flask stuff: 65 | instance/ 66 | .webassets-cache 67 | 68 | # Scrapy stuff: 69 | .scrapy 70 | 71 | # Sphinx documentation 72 | docs/_build/ 73 | 74 | # PyBuilder 75 | .pybuilder/ 76 | target/ 77 | 78 | # Jupyter Notebook 79 | .ipynb_checkpoints 80 | 81 | # IPython 82 | profile_default/ 83 | ipython_config.py 84 | 85 | # pyenv 86 | # For a library or package, you might want to ignore these files since the code is 87 | # intended to run in multiple environments; otherwise, check them in: 88 | # .python-version 89 | 90 | # pipenv 91 | # According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. 92 | # However, in case of collaboration, if having platform-specific dependencies or dependencies 93 | # having no cross-platform support, pipenv may install dependencies that don't work, or not 94 | # install all needed dependencies. 95 | #Pipfile.lock 96 | 97 | # poetry 98 | # Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control. 99 | # This is especially recommended for binary packages to ensure reproducibility, and is more 100 | # commonly ignored for libraries. 101 | # https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control 102 | #poetry.lock 103 | 104 | # pdm 105 | # Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control. 106 | #pdm.lock 107 | # pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it 108 | # in version control. 109 | # https://pdm.fming.dev/#use-with-ide 110 | .pdm.toml 111 | 112 | # PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm 113 | __pypackages__/ 114 | 115 | # Celery stuff 116 | celerybeat-schedule 117 | celerybeat.pid 118 | 119 | # SageMath parsed files 120 | *.sage.py 121 | 122 | # Environments 123 | .env 124 | .venv 125 | env/ 126 | venv/ 127 | ENV/ 128 | env.bak/ 129 | venv.bak/ 130 | 131 | # Spyder project settings 132 | .spyderproject 133 | .spyproject 134 | 135 | # Rope project settings 136 | .ropeproject 137 | 138 | # mkdocs documentation 139 | /site 140 | 141 | # mypy 142 | .mypy_cache/ 143 | .dmypy.json 144 | dmypy.json 145 | 146 | # Pyre type checker 147 | .pyre/ 148 | 149 | # pytype static type analyzer 150 | .pytype/ 151 | 152 | # Cython debug symbols 153 | cython_debug/ 154 | 155 | # PyCharm 156 | # JetBrains specific template is maintained in a separate JetBrains.gitignore that can 157 | # be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore 158 | # and can be added to the global gitignore or merged into this file. For a more nuclear 159 | # option (not recommended) you can uncomment the following to ignore the entire idea folder. 160 | #.idea/ 161 | -------------------------------------------------------------------------------- /Function-Calling-Mistral-7B (using mistral API Key).py: -------------------------------------------------------------------------------- 1 | import locale 2 | import gc, json, re 3 | import xml.etree.ElementTree as ET 4 | from functools import partial 5 | import os 6 | from dotenv import load_dotenv 7 | 8 | import transformers 9 | import torch 10 | 11 | from langchain.utils.openai_functions import convert_pydantic_to_openai_function 12 | from langchain.pydantic_v1 import BaseModel, Field, validator 13 | from langchain_mistralai.chat_models import ChatMistralAI 14 | 15 | # Load API key from .env file 16 | load_dotenv() 17 | mistral_api_key = os.getenv("MISTRAL_API_KEY") 18 | 19 | if mistral_api_key is None: 20 | raise ValueError("MISTRAL_API_KEY not found in .env file") 21 | 22 | tokenizer = transformers.AutoTokenizer.from_pretrained("teknium/OpenHermes-2.5-Mistral-7B") 23 | model = ChatMistralAI(mistral_api_key=mistral_api_key) 24 | 25 | def delete_model(*args): 26 | for var in args: 27 | if var in globals(): 28 | del globals()[var] 29 | 30 | gc.collect() 31 | torch.cuda.empty_cache() 32 | 33 | class BookRecommendation(BaseModel): 34 | """Provides book recommendations based on specified interest.""" 35 | interest: str = Field(description="question of user interest about a book.") 36 | recommended_book: str = Field(description="answer to recommend a book") 37 | 38 | @validator("interest") 39 | def interests_must_not_be_empty(cls, field): 40 | if not field: 41 | raise ValueError("Interest cannot be empty.") 42 | return field 43 | 44 | class Joke(BaseModel): 45 | """Get a joke that includes the setup and punchline""" 46 | setup: str = Field(description="question to set up a joke") 47 | punchline: str = Field(description="answer to resolve the joke") 48 | 49 | # You can add custom validation logic easily with Pydantic. 50 | @validator("setup") 51 | def question_ends_with_question_mark(cls, field): 52 | if field[-1] != "?": 53 | raise ValueError("Badly formed question!") 54 | return field 55 | 56 | class SongRecommendation(BaseModel): 57 | """Provides song recommendations based on specified genre.""" 58 | genre: str = Field(description="question to recommend a song.") 59 | song: str = Field(description="answer to recommend a song") 60 | 61 | @validator("genre") 62 | def genre_must_not_be_empty(cls, field): 63 | if not field: 64 | raise ValueError("genre cannot be empty.") 65 | return field 66 | 67 | convert_pydantic_to_openai_function(SongRecommendation) 68 | 69 | def extract_function_calls(completion): 70 | if isinstance(completion, str): 71 | content = completion 72 | else: 73 | content = completion.content 74 | 75 | pattern = r"(.*?)" 76 | match = re.search(pattern, content, re.DOTALL) 77 | if not match: 78 | return None 79 | 80 | multiplefn = match.group(1) 81 | functions = [] 82 | for fn_match in re.finditer(r"(.*?)", multiplefn, re.DOTALL): 83 | fn_text = fn_match.group(1) 84 | try: 85 | functions.append(json.loads(fn_text)) 86 | except json.JSONDecodeError: 87 | pass # Ignore invalid JSON 88 | 89 | return functions 90 | 91 | def generate_hermes(prompt, model, tokenizer, generation_config_overrides={}): 92 | fn = """{"name": "function_name", "arguments": {"arg_1": "value_1", "arg_2": value_2, ...}}""" 93 | prompt = f"""system 94 | You are a helpful assistant with access to the following functions: 95 | 96 | {convert_pydantic_to_openai_function(Joke)} 97 | 98 | {convert_pydantic_to_openai_function(BookRecommendation)} 99 | 100 | {convert_pydantic_to_openai_function(SongRecommendation)} 101 | 102 | To use these functions respond with: 103 | 104 | {fn} 105 | {fn} 106 | ... 107 | 108 | 109 | Edge cases you must handle: 110 | - If there are no functions that match the user request, you will respond politely that you cannot help. 111 | user 112 | {prompt} 113 | assistant""" 114 | 115 | with torch.inference_mode(): 116 | completion = model.invoke([{"role": "user", "content": prompt}]) 117 | 118 | if isinstance(completion, str): 119 | # Handle the case where completion is a string 120 | content = completion.strip() 121 | else: 122 | # Handle the case where completion is an AIMessage object 123 | content = completion.content.strip() 124 | 125 | functions = extract_function_calls(content) 126 | 127 | if functions: 128 | print(functions) 129 | else: 130 | print(content) 131 | print("="*100) 132 | 133 | generation_func = partial(generate_hermes, model=model, tokenizer=tokenizer) 134 | 135 | prompts = [ 136 | "Tell me a joke about kenyan athletes", 137 | "Song for working out", 138 | "Recommend me a book on singularity." 139 | ] 140 | 141 | for prompt in prompts: 142 | generation_func(prompt) 143 | -------------------------------------------------------------------------------- /function_calling_open_source.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | """Function Calling Open Source.ipynb 3 | 4 | Automatically generated by Colaboratory. 5 | 6 | Original file is located at 7 | https://colab.research.google.com/drive/1CgRaeM0RxO1DFNldHMF1ZgzKXpRCnEKX 8 | """ 9 | 10 | import locale 11 | locale.getpreferredencoding = lambda: "UTF-8" 12 | 13 | !pip install -q langchain 14 | 15 | import gc, inspect, json, re 16 | import xml.etree.ElementTree as ET 17 | from functools import partial 18 | from typing import get_type_hints 19 | 20 | import transformers 21 | import torch 22 | 23 | from langchain.chains.openai_functions import convert_to_openai_function 24 | from langchain.utils.openai_functions import convert_pydantic_to_openai_function 25 | from langchain.pydantic_v1 import BaseModel, Field, validator 26 | 27 | model_name = "teknium/OpenHermes-2.5-Mistral-7B" 28 | 29 | def load_model(model_name: str): 30 | tokenizer = transformers.AutoTokenizer.from_pretrained(model_name) 31 | 32 | with torch.device("cuda:0"): 33 | model = transformers.AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.bfloat16).eval() 34 | 35 | return tokenizer, model 36 | 37 | tokenizer, model = load_model(model_name) 38 | 39 | def delete_model(*args): 40 | for var in args: 41 | if var in globals(): 42 | del globals()[var] 43 | 44 | gc.collect() 45 | torch.cuda.empty_cache() 46 | 47 | class BookRecommendation(BaseModel): 48 | """Provides book recommendations based on specified interest.""" 49 | interest: str = Field(description="question of user interest about a book.") 50 | recommended_book: str = Field(description="answer to recommend a book") 51 | 52 | @validator("interest") 53 | def interests_must_not_be_empty(cls, field): 54 | if not field: 55 | raise ValueError("Interest cannot be empty.") 56 | return field 57 | 58 | class Joke(BaseModel): 59 | """Get a joke that includes the setup and punchline""" 60 | setup: str = Field(description="question to set up a joke") 61 | punchline: str = Field(description="answer to resolve the joke") 62 | 63 | # You can add custom validation logic easily with Pydantic. 64 | @validator("setup") 65 | def question_ends_with_question_mark(cls, field): 66 | if field[-1] != "?": 67 | raise ValueError("Badly formed question!") 68 | return field 69 | 70 | class SongRecommendation(BaseModel): 71 | """Provides song recommendations based on specified genre.""" 72 | genre: str = Field(description="question to recommend a song.") 73 | song: str = Field(description="answer to recommend a song") 74 | 75 | @validator("genre") 76 | def genre_must_not_be_empty(cls, field): 77 | if not field: 78 | raise ValueError("genre cannot be empty.") 79 | return field 80 | 81 | convert_pydantic_to_openai_function(SongRecommendation) 82 | 83 | def extract_function_calls(completion): 84 | completion = completion.strip() 85 | pattern = r"((.*?))" 86 | match = re.search(pattern, completion, re.DOTALL) 87 | if not match: 88 | return None 89 | 90 | multiplefn = match.group(1) 91 | root = ET.fromstring(multiplefn) 92 | functions = root.findall("functioncall") 93 | return [json.loads(fn.text) for fn in functions] 94 | 95 | def generate_hermes(prompt, model, tokenizer, generation_config_overrides={}): 96 | fn = """{"name": "function_name", "arguments": {"arg_1": "value_1", "arg_2": value_2, ...}}""" 97 | prompt = f"""<|im_start|>system 98 | You are a helpful assistant with access to the following functions: 99 | 100 | {convert_pydantic_to_openai_function(Joke)} 101 | 102 | {convert_pydantic_to_openai_function(BookRecommendation)} 103 | 104 | {convert_pydantic_to_openai_function(SongRecommendation)} 105 | 106 | To use these functions respond with: 107 | 108 | {fn} 109 | {fn} 110 | ... 111 | 112 | 113 | Edge cases you must handle: 114 | - If there are no functions that match the user request, you will respond politely that you cannot help.<|im_end|> 115 | <|im_start|>user 116 | {prompt}<|im_end|> 117 | <|im_start|>assistant""" 118 | 119 | generation_config = model.generation_config 120 | generation_config.update( 121 | **{ 122 | **{ 123 | "use_cache": True, 124 | "do_sample": True, 125 | "temperature": 0.2, 126 | "top_p": 1.0, 127 | "top_k": 0, 128 | "max_new_tokens": 512, 129 | "eos_token_id": tokenizer.eos_token_id, 130 | "pad_token_id": tokenizer.eos_token_id, 131 | }, 132 | **generation_config_overrides, 133 | } 134 | ) 135 | 136 | model = model.eval() 137 | inputs = tokenizer(prompt, return_tensors="pt").to(model.device) 138 | n_tokens = inputs.input_ids.numel() 139 | 140 | with torch.inference_mode(): 141 | generated_tokens = model.generate(**inputs, generation_config=generation_config) 142 | 143 | return tokenizer.decode( 144 | generated_tokens.squeeze()[n_tokens:], skip_special_tokens=False 145 | ) 146 | 147 | # Commented out IPython magic to ensure Python compatibility. 148 | # %%time 149 | # 150 | # generation_func = partial(generate_hermes, model=model, tokenizer=tokenizer) 151 | # 152 | # prompts = [ 153 | # "Tell me a joke", 154 | # "Song for inspiration.", 155 | # "Recommend me a book on Crime Thriller." 156 | # ] 157 | # 158 | # for prompt in prompts: 159 | # completion = generation_func(prompt) 160 | # functions = extract_function_calls(completion) 161 | # 162 | # if functions: 163 | # print(functions) 164 | # else: 165 | # print(completion.strip()) 166 | # print("="*100) 167 | 168 | -------------------------------------------------------------------------------- /Function_Calling_Open_Source.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "nbformat": 4, 3 | "nbformat_minor": 0, 4 | "metadata": { 5 | "colab": { 6 | "provenance": [], 7 | "machine_shape": "hm", 8 | "gpuType": "A100" 9 | }, 10 | "kernelspec": { 11 | "name": "python3", 12 | "display_name": "Python 3" 13 | }, 14 | "language_info": { 15 | "name": "python" 16 | }, 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"_model_module_version": "1.5.0", 3774 | "_model_name": "DescriptionStyleModel", 3775 | "_view_count": null, 3776 | "_view_module": "@jupyter-widgets/base", 3777 | "_view_module_version": "1.2.0", 3778 | "_view_name": "StyleView", 3779 | "description_width": "" 3780 | } 3781 | } 3782 | } 3783 | } 3784 | }, 3785 | "cells": [ 3786 | { 3787 | "cell_type": "code", 3788 | "source": [ 3789 | "import locale\n", 3790 | "locale.getpreferredencoding = lambda: \"UTF-8\"" 3791 | ], 3792 | "metadata": { 3793 | "id": "_FqaRJZnFQC5" 3794 | }, 3795 | "execution_count": 52, 3796 | "outputs": [] 3797 | }, 3798 | { 3799 | "cell_type": "code", 3800 | "source": [ 3801 | "!pip install -q langchain" 3802 | ], 3803 | "metadata": { 3804 | "id": "Nk8QnHZTf5fI" 3805 | }, 3806 | "execution_count": 53, 3807 | "outputs": [] 3808 | }, 3809 | { 3810 | "cell_type": "code", 3811 | "execution_count": 54, 3812 | "metadata": { 3813 | "id": "HDoROQAUfuic" 3814 | }, 3815 | "outputs": [], 3816 | "source": [ 3817 | "import gc, inspect, json, re\n", 3818 | "import xml.etree.ElementTree as ET\n", 3819 | "from functools import partial\n", 3820 | "from typing import get_type_hints\n", 3821 | "\n", 3822 | "import transformers\n", 3823 | "import torch\n", 3824 | "\n", 3825 | "from langchain.chains.openai_functions import convert_to_openai_function\n", 3826 | "from langchain.utils.openai_functions import convert_pydantic_to_openai_function\n", 3827 | "from langchain.pydantic_v1 import BaseModel, Field, validator" 3828 | ] 3829 | }, 3830 | { 3831 | "cell_type": "code", 3832 | "source": [ 3833 | "model_name = \"teknium/OpenHermes-2.5-Mistral-7B\"" 3834 | ], 3835 | "metadata": { 3836 | "id": "B31E5qJPgbvK" 3837 | }, 3838 | "execution_count": 3, 3839 | "outputs": [] 3840 | }, 3841 | { 3842 | "cell_type": "code", 3843 | "source": [ 3844 | "def load_model(model_name: str):\n", 3845 | " tokenizer = transformers.AutoTokenizer.from_pretrained(model_name)\n", 3846 | "\n", 3847 | " with torch.device(\"cuda:0\"):\n", 3848 | " model = transformers.AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.bfloat16).eval()\n", 3849 | "\n", 3850 | " return tokenizer, model" 3851 | ], 3852 | "metadata": { 3853 | "id": "0SoC4ia7geZK" 3854 | }, 3855 | "execution_count": 4, 3856 | "outputs": [] 3857 | }, 3858 | { 3859 | "cell_type": "code", 3860 | "source": [ 3861 | "tokenizer, model = load_model(model_name)" 3862 | ], 3863 | "metadata": { 3864 | "colab": { 3865 | "base_uri": "https://localhost:8080/", 3866 | "height": 508, 3867 | "referenced_widgets": [ 3868 | "f22fb66b3f3c49819e9a4966f53632d0", 3869 | "d1bb621750fd41358a42db3d549489aa", 3870 | "1f8420db44dc4650af5875a7e3fc10a1", 3871 | "15b95b9b93594aedaffce8932faaaff1", 3872 | "b8c11de2dc8947b78012cf25c7b6caae", 3873 | "8e07aaaf50274e44be9e08e9882a1672", 3874 | "15aa4732da6d4a169ca6bdee36ed4937", 3875 | "8dc8a1027aa7408196bfcd87562c7ddc", 3876 | "4ed637987ee348829ce571dd96abcf41", 3877 | "c57add69d8b54d999094894208b9f3e2", 3878 | "67674756f4d5407d96ad212a9216f3a5", 3879 | "e2abfa57974e44d5973aae620a77d389", 3880 | "7df59bd797c44a9aa1680b82ab6e5d57", 3881 | "97d46f22e6d242259e11bc226a0733f4", 3882 | "08c23316b4ec452ea5cfbafd69617727", 3883 | "8e8221d5d3a441d7a7e6b4afc4691057", 3884 | "b1a8840f6ec641dba90798954599537a", 3885 | "15c5931b132a4a348c3d542d1d1d6ba4", 3886 | "abaf0d27d5004d6092c9a5f315d5cf13", 3887 | "4aae6ca1e1ad4fcaae85530a2b098e60", 3888 | "9f19966872764d8d86c58888184978c1", 3889 | "e4564d47e7fb4d508b4b94f010d4c2bb", 3890 | "3570d850cbe24810bc439f3369644c63", 3891 | "01dde30b5c974ee99e9b2c6ae7072c04", 3892 | "1b1113b422214f549201dce9c43f172f", 3893 | "cec835da32f14a9ab3d47b9a12a03894", 3894 | "8fd59418797341a08a6f3da249bb6e05", 3895 | "8847c9a8564741028adbec02fb6450e2", 3896 | "c0469801f7e44800afa192ca1d915557", 3897 | "4f33f9e804ff491590250938034991b2", 3898 | "9e4d18a8c70b4c46b8cedb9e30be0e8a", 3899 | "abce2a4d52064b958ac9d521f2a976f1", 3900 | "5f247e9d46cf4d2aac5d72a6e4b07aa3", 3901 | 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"aad7a2f0c0454045867c667a1cd3af1f", 3925 | "c914f8c23adf404797a33442ef6feb91", 3926 | "0e0c748af2104bf0ad12454279d16c0e", 3927 | "f6262e28000c4e0188ec16939bc46bbb", 3928 | "a2600d8786e34dc9872eaa8f5fa730fa", 3929 | "e7287eb6394a49a08778419826af3a48", 3930 | "a256a5db7e7145b7b795331d69233ca3", 3931 | "632fe13cb829410bbab4c468a2ba5949", 3932 | "b422c90f9cec411c8fd28d33c5a9ec4a", 3933 | "a318cf2a2c5f4b6f911db8e8a2dca064", 3934 | "7dd686dc58314f65a4ce49424c02117e", 3935 | "85321100928b498cad1466b67b547f52", 3936 | "be038f49b2084cc1840e4e6e9cc5a973", 3937 | "c914731e55c943649a28be83a696276b", 3938 | "d924f03fe4f042f58ed86059c0d089f9", 3939 | "c6291c76fda34bf4bd2286153ce4cc49", 3940 | "4c7135baf8634facb8931b71d9eb64f3", 3941 | "4ba920b8950f403da0454e42e6ffaccf", 3942 | "c24a42cbfb27434a9e36372e6ea87fa5", 3943 | "0227c02ea2194edd82edf58c36032ed0", 3944 | "4f2d890ce63a499ab9ccecf8e14d6eae", 3945 | "755559e9d8f844059ed80838ee3755e3", 3946 | "0b2cf5e1aedf42f7acd17291599cbecb", 3947 | "94652a48ac854a3d85c35acd9c991a45", 3948 | "cdc4a202c6194e93ad9ea2745e1c34fb", 3949 | "98adfe6514944df6a571e08673b34822", 3950 | "89c332e758b640d3b78329359d1e4484", 3951 | "2a824c5f03c94205be565add66400d3e", 3952 | "e99a47facfeb442da5619b06a6c2d3b9", 3953 | "1dd9486263f8450c873689c4ea95896b", 3954 | "de32acc261274649afb8bd99abc2e5c9", 3955 | "0fed2751754640768399127a090a3cf8", 3956 | "c652f48d1ed34acc9b2e85f88d45e92c", 3957 | "ab25b65bb29c4ff0a18dc18afbd2fbac", 3958 | "6995f6e0021f49b09e6a35f6cd875dc1", 3959 | "f9138702e2644047ab596cb9e7c2e7c0", 3960 | "038ace2d808c4d4897206aa616afb24d", 3961 | "0d24ab9f0e644b8e8bb6fadc12184f65", 3962 | "5f63c0db2dfd47e9be039f2a2d593970", 3963 | "b55cfa3bdb734ac0a6f7227931fd7579", 3964 | "bb93ef3ccabb4951930a1dff95f269f8", 3965 | "0110e7113a7e4cb5961fd671e40cd4f3", 3966 | "df13376837c74775b52e2415074eabab", 3967 | "cb2b77331930478f816693db5d142981", 3968 | "322ef168731a43f0a05f17ceb9123554", 3969 | "d8b84f97cfa943b6936b1802313e1a7a", 3970 | "db331bb718334b1ea221d6b54a700cd7", 3971 | "177ab45e7c1242f09fb54b352ff44f01", 3972 | "860709a1b09d426b8d34fa86bcec46d8", 3973 | "7ffd1bd1212d44548b1a8ab71eb33d4b", 3974 | "2108d7e2341147d3afc03e64eaab84b5", 3975 | "cbfc5cbd6e624db19d372c20d745f9be", 3976 | "d2fbcf4b808b439e8c57655872679433", 3977 | "fc3367441fa8437681a7b0fcf67c4830", 3978 | "ae5cc6fc8f994d3982f1f94a4118b0d7", 3979 | "36ebb20e5ba14223aee5a1e597694771", 3980 | "fa3bf6e51ac7424c8489ec122c04219d", 3981 | "36c7ffcddba246ab9c4d68fcfba0f5f2", 3982 | "2059389d20b245c79d7239985b25d2a6", 3983 | "3de00db7cd8a44b58186e31921508125", 3984 | "051645455bb04ca394eadb2c4fd0a838", 3985 | "a7b308a959794184b4e15ceec7544ff3", 3986 | "baab159e9ccd4cf7be35c618d2e8d7cd", 3987 | "2a2831ab5c3d410ebd79384f704c1d75", 3988 | "dba9a5e39c0d4639a031b6ade9311bc7" 3989 | ] 3990 | }, 3991 | "id": "VwfuuMTdgedI", 3992 | "outputId": "31e7680f-46bf-4337-856c-6d8159fc782d" 3993 | }, 3994 | "execution_count": 5, 3995 | "outputs": [ 3996 | { 3997 | "output_type": "stream", 3998 | "name": "stderr", 3999 | "text": [ 4000 | "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:88: UserWarning: \n", 4001 | "The secret `HF_TOKEN` does not exist in your Colab secrets.\n", 4002 | "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", 4003 | "You will be able to reuse this secret in all of your notebooks.\n", 4004 | "Please note that authentication is recommended but still optional to access public models or datasets.\n", 4005 | " warnings.warn(\n" 4006 | ] 4007 | }, 4008 | { 4009 | "output_type": "display_data", 4010 | "data": { 4011 | "text/plain": [ 4012 | "tokenizer_config.json: 0%| | 0.00/1.60k [00:00(.*?))\"\n", 4288 | " match = re.search(pattern, completion, re.DOTALL)\n", 4289 | " if not match:\n", 4290 | " return None\n", 4291 | "\n", 4292 | " multiplefn = match.group(1)\n", 4293 | " root = ET.fromstring(multiplefn)\n", 4294 | " functions = root.findall(\"functioncall\")\n", 4295 | " return [json.loads(fn.text) for fn in functions]" 4296 | ], 4297 | "metadata": { 4298 | "id": "rbZK81ck_O0z" 4299 | }, 4300 | "execution_count": 74, 4301 | "outputs": [] 4302 | }, 4303 | { 4304 | "cell_type": "code", 4305 | "source": [ 4306 | "def generate_hermes(prompt, model, tokenizer, generation_config_overrides={}):\n", 4307 | " fn = \"\"\"{\"name\": \"function_name\", \"arguments\": {\"arg_1\": \"value_1\", \"arg_2\": value_2, ...}}\"\"\"\n", 4308 | " prompt = f\"\"\"<|im_start|>system\n", 4309 | "You are a helpful assistant with access to the following functions:\n", 4310 | "\n", 4311 | "{convert_pydantic_to_openai_function(Joke)}\n", 4312 | "\n", 4313 | "{convert_pydantic_to_openai_function(BookRecommendation)}\n", 4314 | "\n", 4315 | "{convert_pydantic_to_openai_function(SongRecommendation)}\n", 4316 | "\n", 4317 | "To use these functions respond with:\n", 4318 | "\n", 4319 | " {fn} \n", 4320 | " {fn} \n", 4321 | " ...\n", 4322 | "\n", 4323 | "\n", 4324 | "Edge cases you must handle:\n", 4325 | "- If there are no functions that match the user request, you will respond politely that you cannot help.<|im_end|>\n", 4326 | "<|im_start|>user\n", 4327 | "{prompt}<|im_end|>\n", 4328 | "<|im_start|>assistant\"\"\"\n", 4329 | "\n", 4330 | " generation_config = model.generation_config\n", 4331 | " generation_config.update(\n", 4332 | " **{\n", 4333 | " **{\n", 4334 | " \"use_cache\": True,\n", 4335 | " \"do_sample\": True,\n", 4336 | " \"temperature\": 0.2,\n", 4337 | " \"top_p\": 1.0,\n", 4338 | " \"top_k\": 0,\n", 4339 | " \"max_new_tokens\": 512,\n", 4340 | " \"eos_token_id\": tokenizer.eos_token_id,\n", 4341 | " \"pad_token_id\": tokenizer.eos_token_id,\n", 4342 | " },\n", 4343 | " **generation_config_overrides,\n", 4344 | " }\n", 4345 | " )\n", 4346 | "\n", 4347 | " model = model.eval()\n", 4348 | " inputs = tokenizer(prompt, return_tensors=\"pt\").to(model.device)\n", 4349 | " n_tokens = inputs.input_ids.numel()\n", 4350 | "\n", 4351 | " with torch.inference_mode():\n", 4352 | " generated_tokens = model.generate(**inputs, generation_config=generation_config)\n", 4353 | "\n", 4354 | " return tokenizer.decode(\n", 4355 | " generated_tokens.squeeze()[n_tokens:], skip_special_tokens=False\n", 4356 | " )" 4357 | ], 4358 | "metadata": { 4359 | "id": "PWLPVZDl_VTl" 4360 | }, 4361 | "execution_count": 75, 4362 | "outputs": [] 4363 | }, 4364 | { 4365 | "cell_type": "code", 4366 | "source": [ 4367 | "%%time\n", 4368 | "\n", 4369 | "generation_func = partial(generate_hermes, model=model, tokenizer=tokenizer)\n", 4370 | "\n", 4371 | "prompts = [\n", 4372 | " \"Tell me a joke\",\n", 4373 | " \"Song for inspiration.\",\n", 4374 | " \"Recommend me a book on Crime Thriller.\"\n", 4375 | "]\n", 4376 | "\n", 4377 | "for prompt in prompts:\n", 4378 | " completion = generation_func(prompt)\n", 4379 | " functions = extract_function_calls(completion)\n", 4380 | "\n", 4381 | " if functions:\n", 4382 | " print(functions)\n", 4383 | " else:\n", 4384 | " print(completion.strip())\n", 4385 | " print(\"=\"*100)" 4386 | ], 4387 | "metadata": { 4388 | "colab": { 4389 | "base_uri": "https://localhost:8080/" 4390 | }, 4391 | "id": "cgktideV_wJX", 4392 | "outputId": "9b335835-b14e-4e32-c3cd-5443b85692e8" 4393 | }, 4394 | "execution_count": 76, 4395 | "outputs": [ 4396 | { 4397 | "output_type": "stream", 4398 | "name": "stdout", 4399 | "text": [ 4400 | "[{'name': 'Joke', 'arguments': {'setup': \"Why don't scientists trust atoms?\", 'punchline': 'Because they make up everything!'}}]\n", 4401 | "====================================================================================================\n", 4402 | "[{'name': 'SongRecommendation', 'arguments': {'genre': 'inspiration', 'song': '\"Eye of the Tiger\" by Survivor'}}]\n", 4403 | "====================================================================================================\n", 4404 | "[{'name': 'BookRecommendation', 'arguments': {'interest': 'Crime Thriller', 'recommended_book': 'The Silence of the Lambs'}}]\n", 4405 | "====================================================================================================\n", 4406 | "CPU times: user 8.15 s, sys: 0 ns, total: 8.15 s\n", 4407 | "Wall time: 8.13 s\n" 4408 | ] 4409 | } 4410 | ] 4411 | }, 4412 | { 4413 | "cell_type": "code", 4414 | "source": [], 4415 | "metadata": { 4416 | "id": "RGlzDf_zALDb" 4417 | }, 4418 | "execution_count": null, 4419 | "outputs": [] 4420 | } 4421 | ] 4422 | } --------------------------------------------------------------------------------