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llama.cpp/examples/notebooks/OpenHermesFunctionCalling.ipynb
Andrei 153a0049d9
feat: Generic chatml Function Calling (#957) 
* Add demo notebook

* Add initial chat handler

* Update OpenAI types

* Add generic chatml function calling (wip)

* Update chatml generic function calling.

* Progress on auto-tool calls

* fix streaming functions

* Remove print statements

* fix: Suppress output from llama.cpp init and grammar creation

* Add OpenAI v1 python api compatible chat completion function

* Support non-streaming multi-tool calls

* Format

* Include function_call in response.
2024-02-12 15:56:07 -05:00

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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{\n",
" \"name\": \"get_article_details\",\n",
" \"description\": \"Get article details from unstructured article text.\\ndate_published: formatted as \\\"MM/DD/YYYY\\\"\",\n",
" \"parameters\": {\n",
" \"type\": \"object\",\n",
" \"properties\": {\n",
" \"title\": {\n",
" \"type\": \"str\"\n",
" },\n",
" \"authors\": {\n",
" \"type\": \"list[str]\"\n",
" },\n",
" \"short_summary\": {\n",
" \"type\": \"str\"\n",
" },\n",
" \"date_published\": {\n",
" \"type\": \"str\"\n",
" },\n",
" \"tags\": {\n",
" \"type\": \"list[str]\"\n",
" }\n",
" }\n",
" },\n",
" \"returns\": \"Article\"\n",
"}\n"
]
}
],
"source": [
"import json\n",
"import inspect\n",
"from typing import get_type_hints\n",
"\n",
"class Article:\n",
" pass\n",
"\n",
"class Weather:\n",
" pass\n",
"\n",
"class Directions:\n",
" pass\n",
"\n",
"def calculate_mortgage_payment(loan_amount: int, interest_rate: float, loan_term: int) -> float:\n",
" \"\"\"Get the monthly mortgage payment given an interest rate percentage.\"\"\"\n",
" \n",
" # TODO: you must implement this to actually call it later\n",
" pass\n",
"\n",
"def get_article_details(title: str, authors: list[str], short_summary: str, date_published: str, tags: list[str]) -> Article:\n",
" '''Get article details from unstructured article text.\n",
"date_published: formatted as \"MM/DD/YYYY\"'''\n",
" \n",
" # TODO: you must implement this to actually call it later\n",
" pass\n",
"\n",
"def get_weather(zip_code: str) -> Weather:\n",
" \"\"\"Get the current weather given a zip code.\"\"\"\n",
" \n",
" # TODO: you must implement this to actually call it later\n",
" pass\n",
"\n",
"def get_directions(start: str, destination: str) -> Directions:\n",
" \"\"\"Get directions from Google Directions API.\n",
"start: start address as a string including zipcode (if any)\n",
"destination: end address as a string including zipcode (if any)\"\"\"\n",
" \n",
" # TODO: you must implement this to actually call it later\n",
" pass\n",
"\n",
"def get_type_name(t):\n",
" name = str(t)\n",
" if \"list\" in name or \"dict\" in name:\n",
" return name\n",
" else:\n",
" return t.__name__\n",
"\n",
"def serialize_function_to_json(func):\n",
" signature = inspect.signature(func)\n",
" type_hints = get_type_hints(func)\n",
"\n",
" function_info = {\n",
" \"name\": func.__name__,\n",
" \"description\": func.__doc__,\n",
" \"parameters\": {\n",
" \"type\": \"object\",\n",
" \"properties\": {}\n",
" },\n",
" \"returns\": type_hints.get('return', 'void').__name__\n",
" }\n",
"\n",
" for name, _ in signature.parameters.items():\n",
" param_type = get_type_name(type_hints.get(name, type(None)))\n",
" function_info[\"parameters\"][\"properties\"][name] = {\"type\": param_type}\n",
"\n",
" return json.dumps(function_info, indent=2)\n",
"\n",
"print(serialize_function_to_json(get_article_details))"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"import xml.etree.ElementTree as ET\n",
"import re\n",
"\n",
"def extract_function_calls(completion):\n",
" completion = completion.strip()\n",
" pattern = r\"(<multiplefunctions>(.*?)</multiplefunctions>)\"\n",
" match = re.search(pattern, completion, re.DOTALL)\n",
" if not match:\n",
" return None\n",
" \n",
" multiplefn = match.group(1)\n",
" root = ET.fromstring(multiplefn)\n",
" functions = root.findall(\"functioncall\")\n",
" return [json.loads(fn.text) for fn in functions]"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"def generate_hermes_prompt(prompt, functions):\n",
" functions = \"\\n\\n\".join([serialize_function_to_json(fn) for fn in functions])\n",
" prompt = f\"\"\"<|im_start|>system\n",
"You are a helpful assistant with access to the following functions:\n",
"\n",
"{functions}\n",
"\n",
"To use these functions respond with:\n",
"<multiplefunctions>\n",
" <functioncall> {{\"name\": \"function_name\", \"arguments\": {{\"arg_1\": \"value_1\", \"arg_2\": value_2, ...}}}} </functioncall>\n",
" <functioncall> {{\"name\": \"function_name\", \"arguments\": {{\"arg_1\": \"value_1\", \"arg_2\": value_2, ...}}}} </functioncall>\n",
" ...\n",
"</multiplefunctions>\n",
"\n",
"Edge cases you must handle:\n",
"- If there are no functions that match the user request, you will respond politely that you cannot help.<|im_end|>\n",
"<|im_start|>user\n",
"{prompt}<|im_end|>\n",
"<|im_start|>assistant\"\"\"\n",
" return prompt"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<|im_start|>system\n",
"You are a helpful assistant with access to the following functions:\n",
"\n",
"{\n",
" \"name\": \"get_weather\",\n",
" \"description\": \"Get the current weather given a zip code.\",\n",
" \"parameters\": {\n",
" \"type\": \"object\",\n",
" \"properties\": {\n",
" \"zip_code\": {\n",
" \"type\": \"str\"\n",
" }\n",
" }\n",
" },\n",
" \"returns\": \"Weather\"\n",
"}\n",
"\n",
"{\n",
" \"name\": \"calculate_mortgage_payment\",\n",
" \"description\": \"Get the monthly mortgage payment given an interest rate percentage.\",\n",
" \"parameters\": {\n",
" \"type\": \"object\",\n",
" \"properties\": {\n",
" \"loan_amount\": {\n",
" \"type\": \"int\"\n",
" },\n",
" \"interest_rate\": {\n",
" \"type\": \"float\"\n",
" },\n",
" \"loan_term\": {\n",
" \"type\": \"int\"\n",
" }\n",
" }\n",
" },\n",
" \"returns\": \"float\"\n",
"}\n",
"\n",
"{\n",
" \"name\": \"get_article_details\",\n",
" \"description\": \"Get article details from unstructured article text.\\ndate_published: formatted as \\\"MM/DD/YYYY\\\"\",\n",
" \"parameters\": {\n",
" \"type\": \"object\",\n",
" \"properties\": {\n",
" \"title\": {\n",
" \"type\": \"str\"\n",
" },\n",
" \"authors\": {\n",
" \"type\": \"list[str]\"\n",
" },\n",
" \"short_summary\": {\n",
" \"type\": \"str\"\n",
" },\n",
" \"date_published\": {\n",
" \"type\": \"str\"\n",
" },\n",
" \"tags\": {\n",
" \"type\": \"list[str]\"\n",
" }\n",
" }\n",
" },\n",
" \"returns\": \"Article\"\n",
"}\n",
"\n",
"To use these functions respond with:\n",
"<multiplefunctions>\n",
" <functioncall> {\"name\": \"function_name\", \"arguments\": {\"arg_1\": \"value_1\", \"arg_2\": value_2, ...}} </functioncall>\n",
" <functioncall> {\"name\": \"function_name\", \"arguments\": {\"arg_1\": \"value_1\", \"arg_2\": value_2, ...}} </functioncall>\n",
" ...\n",
"</multiplefunctions>\n",
"\n",
"Edge cases you must handle:\n",
"- If there are no functions that match the user request, you will respond politely that you cannot help.<|im_end|>\n",
"<|im_start|>user\n",
"What's the weather in 10001?<|im_end|>\n",
"<|im_start|>assistant\n",
"<|im_start|>system\n",
"You are a helpful assistant with access to the following functions:\n",
"\n",
"{\n",
" \"name\": \"get_weather\",\n",
" \"description\": \"Get the current weather given a zip code.\",\n",
" \"parameters\": {\n",
" \"type\": \"object\",\n",
" \"properties\": {\n",
" \"zip_code\": {\n",
" \"type\": \"str\"\n",
" }\n",
" }\n",
" },\n",
" \"returns\": \"Weather\"\n",
"}\n",
"\n",
"{\n",
" \"name\": \"calculate_mortgage_payment\",\n",
" \"description\": \"Get the monthly mortgage payment given an interest rate percentage.\",\n",
" \"parameters\": {\n",
" \"type\": \"object\",\n",
" \"properties\": {\n",
" \"loan_amount\": {\n",
" \"type\": \"int\"\n",
" },\n",
" \"interest_rate\": {\n",
" \"type\": \"float\"\n",
" },\n",
" \"loan_term\": {\n",
" \"type\": \"int\"\n",
" }\n",
" }\n",
" },\n",
" \"returns\": \"float\"\n",
"}\n",
"\n",
"{\n",
" \"name\": \"get_article_details\",\n",
" \"description\": \"Get article details from unstructured article text.\\ndate_published: formatted as \\\"MM/DD/YYYY\\\"\",\n",
" \"parameters\": {\n",
" \"type\": \"object\",\n",
" \"properties\": {\n",
" \"title\": {\n",
" \"type\": \"str\"\n",
" },\n",
" \"authors\": {\n",
" \"type\": \"list[str]\"\n",
" },\n",
" \"short_summary\": {\n",
" \"type\": \"str\"\n",
" },\n",
" \"date_published\": {\n",
" \"type\": \"str\"\n",
" },\n",
" \"tags\": {\n",
" \"type\": \"list[str]\"\n",
" }\n",
" }\n",
" },\n",
" \"returns\": \"Article\"\n",
"}\n",
"\n",
"To use these functions respond with:\n",
"<multiplefunctions>\n",
" <functioncall> {\"name\": \"function_name\", \"arguments\": {\"arg_1\": \"value_1\", \"arg_2\": value_2, ...}} </functioncall>\n",
" <functioncall> {\"name\": \"function_name\", \"arguments\": {\"arg_1\": \"value_1\", \"arg_2\": value_2, ...}} </functioncall>\n",
" ...\n",
"</multiplefunctions>\n",
"\n",
"Edge cases you must handle:\n",
"- If there are no functions that match the user request, you will respond politely that you cannot help.<|im_end|>\n",
"<|im_start|>user\n",
"Determine the monthly mortgage payment for a loan amount of $200,000, an interest rate of 4%, and a loan term of 30 years.<|im_end|>\n",
"<|im_start|>assistant\n",
"<|im_start|>system\n",
"You are a helpful assistant with access to the following functions:\n",
"\n",
"{\n",
" \"name\": \"get_weather\",\n",
" \"description\": \"Get the current weather given a zip code.\",\n",
" \"parameters\": {\n",
" \"type\": \"object\",\n",
" \"properties\": {\n",
" \"zip_code\": {\n",
" \"type\": \"str\"\n",
" }\n",
" }\n",
" },\n",
" \"returns\": \"Weather\"\n",
"}\n",
"\n",
"{\n",
" \"name\": \"calculate_mortgage_payment\",\n",
" \"description\": \"Get the monthly mortgage payment given an interest rate percentage.\",\n",
" \"parameters\": {\n",
" \"type\": \"object\",\n",
" \"properties\": {\n",
" \"loan_amount\": {\n",
" \"type\": \"int\"\n",
" },\n",
" \"interest_rate\": {\n",
" \"type\": \"float\"\n",
" },\n",
" \"loan_term\": {\n",
" \"type\": \"int\"\n",
" }\n",
" }\n",
" },\n",
" \"returns\": \"float\"\n",
"}\n",
"\n",
"{\n",
" \"name\": \"get_article_details\",\n",
" \"description\": \"Get article details from unstructured article text.\\ndate_published: formatted as \\\"MM/DD/YYYY\\\"\",\n",
" \"parameters\": {\n",
" \"type\": \"object\",\n",
" \"properties\": {\n",
" \"title\": {\n",
" \"type\": \"str\"\n",
" },\n",
" \"authors\": {\n",
" \"type\": \"list[str]\"\n",
" },\n",
" \"short_summary\": {\n",
" \"type\": \"str\"\n",
" },\n",
" \"date_published\": {\n",
" \"type\": \"str\"\n",
" },\n",
" \"tags\": {\n",
" \"type\": \"list[str]\"\n",
" }\n",
" }\n",
" },\n",
" \"returns\": \"Article\"\n",
"}\n",
"\n",
"To use these functions respond with:\n",
"<multiplefunctions>\n",
" <functioncall> {\"name\": \"function_name\", \"arguments\": {\"arg_1\": \"value_1\", \"arg_2\": value_2, ...}} </functioncall>\n",
" <functioncall> {\"name\": \"function_name\", \"arguments\": {\"arg_1\": \"value_1\", \"arg_2\": value_2, ...}} </functioncall>\n",
" ...\n",
"</multiplefunctions>\n",
"\n",
"Edge cases you must handle:\n",
"- If there are no functions that match the user request, you will respond politely that you cannot help.<|im_end|>\n",
"<|im_start|>user\n",
"What's the current exchange rate for USD to EUR?<|im_end|>\n",
"<|im_start|>assistant\n"
]
}
],
"source": [
"prompts = [\n",
" \"What's the weather in 10001?\",\n",
" \"Determine the monthly mortgage payment for a loan amount of $200,000, an interest rate of 4%, and a loan term of 30 years.\",\n",
" \"What's the current exchange rate for USD to EUR?\"\n",
"]\n",
"functions = [get_weather, calculate_mortgage_payment, get_article_details]\n",
"\n",
"for prompt in prompts:\n",
" print(generate_hermes_prompt(prompt, functions))"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"ggml_init_cublas: GGML_CUDA_FORCE_MMQ: no\n",
"ggml_init_cublas: CUDA_USE_TENSOR_CORES: yes\n",
"ggml_init_cublas: found 1 CUDA devices:\n",
" Device 0: NVIDIA GeForce RTX 2060, compute capability 7.5\n",
"llama_model_loader: loaded meta data with 20 key-value pairs and 291 tensors from ../../models/OpenHermes-2.5-Mistral-7B-GGUF/openhermes-2.5-mistral-7b.Q4_K_M.gguf (version GGUF V3 (latest))\n",
"llama_model_loader: - tensor 0: token_embd.weight q4_K [ 4096, 32002, 1, 1 ]\n",
"llama_model_loader: - tensor 1: blk.0.attn_q.weight q4_K [ 4096, 4096, 1, 1 ]\n",
"llama_model_loader: - tensor 2: blk.0.attn_k.weight q4_K [ 4096, 1024, 1, 1 ]\n",
"llama_model_loader: - tensor 3: blk.0.attn_v.weight q6_K [ 4096, 1024, 1, 1 ]\n",
"llama_model_loader: - tensor 4: blk.0.attn_output.weight q4_K [ 4096, 4096, 1, 1 ]\n",
"llama_model_loader: - tensor 5: blk.0.ffn_gate.weight q4_K [ 4096, 14336, 1, 1 ]\n",
"llama_model_loader: - tensor 6: blk.0.ffn_up.weight q4_K [ 4096, 14336, 1, 1 ]\n",
"llama_model_loader: - tensor 7: blk.0.ffn_down.weight q6_K [ 14336, 4096, 1, 1 ]\n",
"llama_model_loader: - tensor 8: blk.0.attn_norm.weight f32 [ 4096, 1, 1, 1 ]\n",
"llama_model_loader: - tensor 9: blk.0.ffn_norm.weight f32 [ 4096, 1, 1, 1 ]\n",
"llama_model_loader: - tensor 10: blk.1.attn_q.weight q4_K [ 4096, 4096, 1, 1 ]\n",
"llama_model_loader: - tensor 11: blk.1.attn_k.weight q4_K [ 4096, 1024, 1, 1 ]\n",
"llama_model_loader: - tensor 12: blk.1.attn_v.weight q6_K [ 4096, 1024, 1, 1 ]\n",
"llama_model_loader: - tensor 13: blk.1.attn_output.weight q4_K [ 4096, 4096, 1, 1 ]\n",
"llama_model_loader: - tensor 14: blk.1.ffn_gate.weight q4_K [ 4096, 14336, 1, 1 ]\n",
"llama_model_loader: - tensor 15: blk.1.ffn_up.weight q4_K [ 4096, 14336, 1, 1 ]\n",
"llama_model_loader: - tensor 16: blk.1.ffn_down.weight q6_K [ 14336, 4096, 1, 1 ]\n",
"llama_model_loader: - tensor 17: blk.1.attn_norm.weight f32 [ 4096, 1, 1, 1 ]\n",
"llama_model_loader: - tensor 18: blk.1.ffn_norm.weight f32 [ 4096, 1, 1, 1 ]\n",
"llama_model_loader: - tensor 19: blk.2.attn_q.weight q4_K [ 4096, 4096, 1, 1 ]\n",
"llama_model_loader: - tensor 20: blk.2.attn_k.weight q4_K [ 4096, 1024, 1, 1 ]\n",
"llama_model_loader: - tensor 21: blk.2.attn_v.weight q6_K [ 4096, 1024, 1, 1 ]\n",
"llama_model_loader: - tensor 22: blk.2.attn_output.weight q4_K [ 4096, 4096, 1, 1 ]\n",
"llama_model_loader: - tensor 23: blk.2.ffn_gate.weight q4_K [ 4096, 14336, 1, 1 ]\n",
"llama_model_loader: - tensor 24: blk.2.ffn_up.weight q4_K [ 4096, 14336, 1, 1 ]\n",
"llama_model_loader: - tensor 25: blk.2.ffn_down.weight q6_K [ 14336, 4096, 1, 1 ]\n",
"llama_model_loader: - tensor 26: blk.2.attn_norm.weight f32 [ 4096, 1, 1, 1 ]\n",
"llama_model_loader: - tensor 27: blk.2.ffn_norm.weight f32 [ 4096, 1, 1, 1 ]\n",
"llama_model_loader: - tensor 28: blk.3.attn_q.weight q4_K [ 4096, 4096, 1, 1 ]\n",
"llama_model_loader: - tensor 29: blk.3.attn_k.weight q4_K [ 4096, 1024, 1, 1 ]\n",
"llama_model_loader: - tensor 30: blk.3.attn_v.weight q6_K [ 4096, 1024, 1, 1 ]\n",
"llama_model_loader: - tensor 31: blk.3.attn_output.weight q4_K [ 4096, 4096, 1, 1 ]\n",
"llama_model_loader: - tensor 32: blk.3.ffn_gate.weight q4_K [ 4096, 14336, 1, 1 ]\n",
"llama_model_loader: - tensor 33: blk.3.ffn_up.weight q4_K [ 4096, 14336, 1, 1 ]\n",
"llama_model_loader: - tensor 34: blk.3.ffn_down.weight q6_K [ 14336, 4096, 1, 1 ]\n",
"llama_model_loader: - tensor 35: blk.3.attn_norm.weight f32 [ 4096, 1, 1, 1 ]\n",
"llama_model_loader: - tensor 36: blk.3.ffn_norm.weight f32 [ 4096, 1, 1, 1 ]\n",
"llama_model_loader: - tensor 37: blk.4.attn_q.weight q4_K [ 4096, 4096, 1, 1 ]\n",
"llama_model_loader: - tensor 38: blk.4.attn_k.weight q4_K [ 4096, 1024, 1, 1 ]\n",
"llama_model_loader: - tensor 39: blk.4.attn_v.weight q4_K [ 4096, 1024, 1, 1 ]\n",
"llama_model_loader: - tensor 40: blk.4.attn_output.weight q4_K [ 4096, 4096, 1, 1 ]\n",
"llama_model_loader: - tensor 41: blk.4.ffn_gate.weight q4_K [ 4096, 14336, 1, 1 ]\n",
"llama_model_loader: - tensor 42: blk.4.ffn_up.weight q4_K [ 4096, 14336, 1, 1 ]\n",
"llama_model_loader: - tensor 43: blk.4.ffn_down.weight q4_K [ 14336, 4096, 1, 1 ]\n",
"llama_model_loader: - tensor 44: blk.4.attn_norm.weight f32 [ 4096, 1, 1, 1 ]\n",
"llama_model_loader: - tensor 45: blk.4.ffn_norm.weight f32 [ 4096, 1, 1, 1 ]\n",
"llama_model_loader: - tensor 46: blk.5.attn_q.weight q4_K [ 4096, 4096, 1, 1 ]\n",
"llama_model_loader: - tensor 47: blk.5.attn_k.weight q4_K [ 4096, 1024, 1, 1 ]\n",
"llama_model_loader: - tensor 48: blk.5.attn_v.weight q4_K [ 4096, 1024, 1, 1 ]\n",
"llama_model_loader: - tensor 49: blk.5.attn_output.weight q4_K [ 4096, 4096, 1, 1 ]\n",
"llama_model_loader: - tensor 50: blk.5.ffn_gate.weight q4_K [ 4096, 14336, 1, 1 ]\n",
"llama_model_loader: - tensor 51: blk.5.ffn_up.weight q4_K [ 4096, 14336, 1, 1 ]\n",
"llama_model_loader: - tensor 52: blk.5.ffn_down.weight q4_K [ 14336, 4096, 1, 1 ]\n",
"llama_model_loader: - tensor 53: blk.5.attn_norm.weight f32 [ 4096, 1, 1, 1 ]\n",
"llama_model_loader: - tensor 54: blk.5.ffn_norm.weight f32 [ 4096, 1, 1, 1 ]\n",
"llama_model_loader: - tensor 55: blk.6.attn_q.weight q4_K [ 4096, 4096, 1, 1 ]\n",
"llama_model_loader: - tensor 56: blk.6.attn_k.weight q4_K [ 4096, 1024, 1, 1 ]\n",
"llama_model_loader: - tensor 57: blk.6.attn_v.weight q6_K [ 4096, 1024, 1, 1 ]\n",
"llama_model_loader: - tensor 58: blk.6.attn_output.weight q4_K [ 4096, 4096, 1, 1 ]\n",
"llama_model_loader: - tensor 59: blk.6.ffn_gate.weight q4_K [ 4096, 14336, 1, 1 ]\n",
"llama_model_loader: - tensor 60: blk.6.ffn_up.weight q4_K [ 4096, 14336, 1, 1 ]\n",
"llama_model_loader: - tensor 61: blk.6.ffn_down.weight q6_K [ 14336, 4096, 1, 1 ]\n",
"llama_model_loader: - tensor 62: blk.6.attn_norm.weight f32 [ 4096, 1, 1, 1 ]\n",
"llama_model_loader: - tensor 63: blk.6.ffn_norm.weight f32 [ 4096, 1, 1, 1 ]\n",
"llama_model_loader: - tensor 64: blk.7.attn_q.weight q4_K [ 4096, 4096, 1, 1 ]\n",
"llama_model_loader: - tensor 65: blk.7.attn_k.weight q4_K [ 4096, 1024, 1, 1 ]\n",
"llama_model_loader: - tensor 66: blk.7.attn_v.weight q4_K [ 4096, 1024, 1, 1 ]\n",
"llama_model_loader: - tensor 67: blk.7.attn_output.weight q4_K [ 4096, 4096, 1, 1 ]\n",
"llama_model_loader: - tensor 68: blk.7.ffn_gate.weight q4_K [ 4096, 14336, 1, 1 ]\n",
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"llama_model_loader: - tensor 261: blk.28.ffn_norm.weight f32 [ 4096, 1, 1, 1 ]\n",
"llama_model_loader: - tensor 262: blk.29.attn_q.weight q4_K [ 4096, 4096, 1, 1 ]\n",
"llama_model_loader: - tensor 263: blk.29.attn_k.weight q4_K [ 4096, 1024, 1, 1 ]\n",
"llama_model_loader: - tensor 264: blk.29.attn_v.weight q6_K [ 4096, 1024, 1, 1 ]\n",
"llama_model_loader: - tensor 265: blk.29.attn_output.weight q4_K [ 4096, 4096, 1, 1 ]\n",
"llama_model_loader: - tensor 266: blk.29.ffn_gate.weight q4_K [ 4096, 14336, 1, 1 ]\n",
"llama_model_loader: - tensor 267: blk.29.ffn_up.weight q4_K [ 4096, 14336, 1, 1 ]\n",
"llama_model_loader: - tensor 268: blk.29.ffn_down.weight q6_K [ 14336, 4096, 1, 1 ]\n",
"llama_model_loader: - tensor 269: blk.29.attn_norm.weight f32 [ 4096, 1, 1, 1 ]\n",
"llama_model_loader: - tensor 270: blk.29.ffn_norm.weight f32 [ 4096, 1, 1, 1 ]\n",
"llama_model_loader: - tensor 271: blk.30.attn_q.weight q4_K [ 4096, 4096, 1, 1 ]\n",
"llama_model_loader: - tensor 272: blk.30.attn_k.weight q4_K [ 4096, 1024, 1, 1 ]\n",
"llama_model_loader: - tensor 273: blk.30.attn_v.weight q6_K [ 4096, 1024, 1, 1 ]\n",
"llama_model_loader: - tensor 274: blk.30.attn_output.weight q4_K [ 4096, 4096, 1, 1 ]\n",
"llama_model_loader: - tensor 275: blk.30.ffn_gate.weight q4_K [ 4096, 14336, 1, 1 ]\n",
"llama_model_loader: - tensor 276: blk.30.ffn_up.weight q4_K [ 4096, 14336, 1, 1 ]\n",
"llama_model_loader: - tensor 277: blk.30.ffn_down.weight q6_K [ 14336, 4096, 1, 1 ]\n",
"llama_model_loader: - tensor 278: blk.30.attn_norm.weight f32 [ 4096, 1, 1, 1 ]\n",
"llama_model_loader: - tensor 279: blk.30.ffn_norm.weight f32 [ 4096, 1, 1, 1 ]\n",
"llama_model_loader: - tensor 280: blk.31.attn_q.weight q4_K [ 4096, 4096, 1, 1 ]\n",
"llama_model_loader: - tensor 281: blk.31.attn_k.weight q4_K [ 4096, 1024, 1, 1 ]\n",
"llama_model_loader: - tensor 282: blk.31.attn_v.weight q6_K [ 4096, 1024, 1, 1 ]\n",
"llama_model_loader: - tensor 283: blk.31.attn_output.weight q4_K [ 4096, 4096, 1, 1 ]\n",
"llama_model_loader: - tensor 284: blk.31.ffn_gate.weight q4_K [ 4096, 14336, 1, 1 ]\n",
"llama_model_loader: - tensor 285: blk.31.ffn_up.weight q4_K [ 4096, 14336, 1, 1 ]\n",
"llama_model_loader: - tensor 286: blk.31.ffn_down.weight q6_K [ 14336, 4096, 1, 1 ]\n",
"llama_model_loader: - tensor 287: blk.31.attn_norm.weight f32 [ 4096, 1, 1, 1 ]\n",
"llama_model_loader: - tensor 288: blk.31.ffn_norm.weight f32 [ 4096, 1, 1, 1 ]\n",
"llama_model_loader: - tensor 289: output_norm.weight f32 [ 4096, 1, 1, 1 ]\n",
"llama_model_loader: - tensor 290: output.weight q6_K [ 4096, 32002, 1, 1 ]\n",
"llama_model_loader: - kv 0: general.architecture str = llama\n",
"llama_model_loader: - kv 1: general.name str = teknium_openhermes-2.5-mistral-7b\n",
"llama_model_loader: - kv 2: llama.context_length u32 = 32768\n",
"llama_model_loader: - kv 3: llama.embedding_length u32 = 4096\n",
"llama_model_loader: - kv 4: llama.block_count u32 = 32\n",
"llama_model_loader: - kv 5: llama.feed_forward_length u32 = 14336\n",
"llama_model_loader: - kv 6: llama.rope.dimension_count u32 = 128\n",
"llama_model_loader: - kv 7: llama.attention.head_count u32 = 32\n",
"llama_model_loader: - kv 8: llama.attention.head_count_kv u32 = 8\n",
"llama_model_loader: - kv 9: llama.attention.layer_norm_rms_epsilon f32 = 0.000010\n",
"llama_model_loader: - kv 10: llama.rope.freq_base f32 = 10000.000000\n",
"llama_model_loader: - kv 11: general.file_type u32 = 15\n",
"llama_model_loader: - kv 12: tokenizer.ggml.model str = llama\n",
"llama_model_loader: - kv 13: tokenizer.ggml.tokens arr[str,32002] = [\"<unk>\", \"<s>\", \"</s>\", \"<0x00>\", \"<...\n",
"llama_model_loader: - kv 14: tokenizer.ggml.scores arr[f32,32002] = [0.000000, 0.000000, 0.000000, 0.0000...\n",
"llama_model_loader: - kv 15: tokenizer.ggml.token_type arr[i32,32002] = [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 = 32000\n",
"llama_model_loader: - kv 18: tokenizer.ggml.padding_token_id u32 = 0\n",
"llama_model_loader: - kv 19: general.quantization_version u32 = 2\n",
"llama_model_loader: - type f32: 65 tensors\n",
"llama_model_loader: - type q4_K: 193 tensors\n",
"llama_model_loader: - type q6_K: 33 tensors\n",
"llm_load_vocab: special tokens definition check successful ( 261/32002 ).\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 = 32002\n",
"llm_load_print_meta: n_merges = 0\n",
"llm_load_print_meta: n_ctx_train = 32768\n",
"llm_load_print_meta: n_embd = 4096\n",
"llm_load_print_meta: n_head = 32\n",
"llm_load_print_meta: n_head_kv = 8\n",
"llm_load_print_meta: n_layer = 32\n",
"llm_load_print_meta: n_rot = 128\n",
"llm_load_print_meta: n_gqa = 4\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 = 14336\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 = 32768\n",
"llm_load_print_meta: rope_finetuned = unknown\n",
"llm_load_print_meta: model type = 7B\n",
"llm_load_print_meta: model ftype = mostly Q4_K - Medium\n",
"llm_load_print_meta: model params = 7.24 B\n",
"llm_load_print_meta: model size = 4.07 GiB (4.83 BPW) \n",
"llm_load_print_meta: general.name = teknium_openhermes-2.5-mistral-7b\n",
"llm_load_print_meta: BOS token = 1 '<s>'\n",
"llm_load_print_meta: EOS token = 32000 '<|im_end|>'\n",
"llm_load_print_meta: UNK token = 0 '<unk>'\n",
"llm_load_print_meta: PAD token = 0 '<unk>'\n",
"llm_load_print_meta: LF token = 13 '<0x0A>'\n",
"llm_load_tensors: ggml ctx size = 0.11 MiB\n",
"llm_load_tensors: using CUDA for GPU acceleration\n",
"llm_load_tensors: mem required = 70.42 MiB\n",
"llm_load_tensors: offloading 32 repeating layers to GPU\n",
"llm_load_tensors: offloading non-repeating layers to GPU\n",
"llm_load_tensors: offloaded 35/35 layers to GPU\n",
"llm_load_tensors: VRAM used: 4095.06 MiB\n",
"...............................................................................................\n",
"llama_new_context_with_model: n_ctx = 2048\n",
"llama_new_context_with_model: freq_base = 10000.0\n",
"llama_new_context_with_model: freq_scale = 1\n",
"llama_kv_cache_init: offloading v cache to GPU\n",
"llama_kv_cache_init: offloading k cache to GPU\n",
"llama_kv_cache_init: VRAM kv self = 256.00 MiB\n",
"llama_new_context_with_model: kv self size = 256.00 MiB\n",
"llama_build_graph: non-view tensors processed: 740/740\n",
"llama_new_context_with_model: compute buffer total size = 159.07 MiB\n",
"llama_new_context_with_model: VRAM scratch buffer: 156.00 MiB\n",
"llama_new_context_with_model: total VRAM used: 4507.07 MiB (model: 4095.06 MiB, context: 412.00 MiB)\n"
]
}
],
"source": [
"import llama_cpp\n",
"\n",
"llama = llama_cpp.Llama(model_path=\"../../models/OpenHermes-2.5-Mistral-7B-GGUF/openhermes-2.5-mistral-7b.Q4_K_M.gguf\", n_gpu_layers=-1, n_ctx=2048, verbose=False)"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[{'name': 'get_weather', 'arguments': {'zip_code': '10001'}}]\n",
"====================================================================================================\n",
"[{'name': 'calculate_mortgage_payment', 'arguments': {'loan_amount': 200000, 'interest_rate': 0.04, 'loan_term': 30}}]\n",
"====================================================================================================\n",
"Unfortunately, I do not have a built-in function to check currency exchange rates. However, you can use third-party APIs or websites like Google Finance or XE to get this information.\n",
"====================================================================================================\n"
]
}
],
"source": [
"prompts = [\n",
" \"What's the weather in 10001?\",\n",
" \"Determine the monthly mortgage payment for a loan amount of $200,000, an interest rate of 4%, and a loan term of 30 years.\",\n",
" \"What's the current exchange rate for USD to EUR?\"\n",
"]\n",
"functions = [get_weather, calculate_mortgage_payment, get_article_details]\n",
"\n",
"for prompt in prompts:\n",
" prompt = generate_hermes_prompt(prompt, functions)\n",
" completion = llama.create_completion(prompt, max_tokens=-1)[\"choices\"][0][\"text\"]\n",
" function_calls = extract_function_calls(completion)\n",
" if function_calls:\n",
" print(function_calls)\n",
" else:\n",
" print(completion.strip())\n",
" print(\"=\"*100)"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"get_weather\n",
"{'zip_code': '05751'}\n",
"====================================================================================================\n",
"get_weather\n",
"{'zip_code': '05751'}\n",
"get_weather\n",
"{'zip_code': '07030'}\n",
"calculate_mortgage_payment\n",
"{'loan_amount': 250000, 'interest_rate': 4.18, 'loan_term': 30}\n",
"====================================================================================================\n",
"I don't have a function to get exchange rates, but I can provide some resources where you can find this information. You can check websites like Google Finance, XE.com, or Yahoo Finance for up-to-date currency exchange rates.\n",
"====================================================================================================\n"
]
}
],
"source": [
"prompts = [\n",
" \"What's the weather in 05751?\",\n",
" \"I'm planning a trip to Killington, Vermont (05751) from Hoboken, NJ (07030). Can you get me weather for both locations and directions?\",\n",
" \"What's the current exchange rate for USD to EUR?\"\n",
"]\n",
"\n",
"for prompt in prompts:\n",
" completion = llama.create_completion(generate_hermes_prompt(prompt, functions), max_tokens=-1)[\"choices\"][0][\"text\"]\n",
" function_calls = extract_function_calls(completion)\n",
"\n",
" if function_calls:\n",
" for function in function_calls:\n",
" print(function[\"name\"])\n",
" print(function[\"arguments\"])\n",
" else:\n",
" print(completion.strip())\n",
"\n",
" print(\"=\"*100)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": ".venv",
"language": "python",
"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": "3.11.5+"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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