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feat: Generic chatml Function Calling (#957)

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* 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.
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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 259: blk.28.ffn_down.weight q6_K [ 14336, 4096, 1, 1 ]\n",
"llama_model_loader: - tensor 260: blk.28.attn_norm.weight f32 [ 4096, 1, 1, 1 ]\n",
"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
}

38
llama_cpp/llama.py
View file

@ -50,6 +50,9 @@ from ._internals import (
_LlamaSamplingContext, # type: ignore _LlamaSamplingContext, # type: ignore
) )
from ._logger import set_verbose from ._logger import set_verbose
from ._utils import (
suppress_stdout_stderr
)
class Llama: class Llama:
@ -182,7 +185,8 @@ class Llama:
self.numa = numa self.numa = numa
if not Llama.__backend_initialized: if not Llama.__backend_initialized:
llama_cpp.llama_backend_init(self.numa) with suppress_stdout_stderr(disable=verbose):
llama_cpp.llama_backend_init(self.numa)
Llama.__backend_initialized = True Llama.__backend_initialized = True
self.model_path = model_path self.model_path = model_path
@ -1567,6 +1571,38 @@ class Llama:
logit_bias=logit_bias, logit_bias=logit_bias,
) )
def create_chat_completion_openai_v1(
self,
*args: Any,
**kwargs: Any,
):
"""Generate a chat completion with return type based on the the OpenAI v1 API.
OpenAI python package is required to use this method.
You can install it with `pip install openai`.
Args:
*args: Positional arguments to pass to create_chat_completion.
**kwargs: Keyword arguments to pass to create_chat_completion.
Returns:
Generated chat completion or a stream of chat completion chunks.
"""
try:
from openai.types.chat import ChatCompletion, ChatCompletionChunk
stream = kwargs.get("stream", False) # type: ignore
assert isinstance(stream, bool)
if stream:
return (ChatCompletionChunk(**chunk) for chunk in self.create_chat_completion(*args, **kwargs)) # type: ignore
else:
return ChatCompletion(**self.create_chat_completion(*args, **kwargs)) # type: ignore
except ImportError:
raise ImportError(
"To use create_chat_completion_openai_v1, you must install the openai package."
"You can install it with `pip install openai`."
)
def __getstate__(self): def __getstate__(self):
return dict( return dict(
model_path=self.model_path, model_path=self.model_path,

761
llama_cpp/llama_chat_format.py
View file

@ -31,6 +31,7 @@ MISTRAL_INSTRUCT_EOS_TOKEN = "</s>"
### Chat Completion Handler ### ### Chat Completion Handler ###
class LlamaChatCompletionHandler(Protocol): class LlamaChatCompletionHandler(Protocol):
"""Base Protocol for a llama chat completion handler. """Base Protocol for a llama chat completion handler.
@ -77,8 +78,7 @@ class LlamaChatCompletionHandler(Protocol):
) -> Union[ ) -> Union[
llama_types.CreateChatCompletionResponse, llama_types.CreateChatCompletionResponse,
Iterator[llama_types.CreateChatCompletionStreamResponse], Iterator[llama_types.CreateChatCompletionStreamResponse],
]: ]: ...
...
class LlamaChatCompletionHandlerNotFoundException(Exception): class LlamaChatCompletionHandlerNotFoundException(Exception):
@ -134,6 +134,7 @@ def register_chat_completion_handler(name: str):
### Chat Formatter ### ### Chat Formatter ###
@dataclasses.dataclass @dataclasses.dataclass
class ChatFormatterResponse: class ChatFormatterResponse:
"""Dataclass that stores completion parameters for a given chat format and """Dataclass that stores completion parameters for a given chat format and
@ -157,8 +158,7 @@ class ChatFormatter(Protocol):
*, *,
messages: List[llama_types.ChatCompletionRequestMessage], messages: List[llama_types.ChatCompletionRequestMessage],
**kwargs: Any, **kwargs: Any,
) -> ChatFormatterResponse: ) -> ChatFormatterResponse: ...
...
class Jinja2ChatFormatter(ChatFormatter): class Jinja2ChatFormatter(ChatFormatter):
@ -195,7 +195,7 @@ class Jinja2ChatFormatter(ChatFormatter):
eos_token=self.eos_token, eos_token=self.eos_token,
bos_token=self.bos_token, bos_token=self.bos_token,
raise_exception=raise_exception, raise_exception=raise_exception,
add_generation_prompt=self.add_generation_prompt add_generation_prompt=self.add_generation_prompt,
) )
return ChatFormatterResponse(prompt=prompt, stop=[self.eos_token]) return ChatFormatterResponse(prompt=prompt, stop=[self.eos_token])
@ -255,11 +255,13 @@ def _convert_text_completion_chunks_to_chat(
"choices": [ "choices": [
{ {
"index": 0, "index": 0,
"delta": { "delta": (
"content": chunk["choices"][0]["text"], {
} "content": chunk["choices"][0]["text"],
if chunk["choices"][0]["finish_reason"] is None }
else {}, if chunk["choices"][0]["finish_reason"] is None
else {}
),
"finish_reason": chunk["choices"][0]["finish_reason"], "finish_reason": chunk["choices"][0]["finish_reason"],
} }
], ],
@ -338,10 +340,12 @@ def chat_formatter_to_chat_completion_handler(
# create grammar from json schema # create grammar from json schema
if "schema" in response_format: if "schema" in response_format:
grammar = llama_grammar.LlamaGrammar.from_json_schema( grammar = llama_grammar.LlamaGrammar.from_json_schema(
json.dumps(response_format["schema"]) json.dumps(response_format["schema"]), verbose=llama.verbose
) )
except Exception as e: except Exception as e:
grammar = llama_grammar.LlamaGrammar.from_string(llama_grammar.JSON_GBNF) grammar = llama_grammar.LlamaGrammar.from_string(
llama_grammar.JSON_GBNF, verbose=llama.verbose
)
completion_or_chunks = llama.create_completion( completion_or_chunks = llama.create_completion(
prompt=prompt, prompt=prompt,
@ -452,7 +456,9 @@ def hf_tokenizer_config_to_chat_completion_handler(
tokenizer_config: Dict[str, Any], tokenizer_config: Dict[str, Any],
add_generation_prompt: bool = True, add_generation_prompt: bool = True,
) -> LlamaChatCompletionHandler: ) -> LlamaChatCompletionHandler:
chat_formatter = hf_tokenizer_config_to_chat_formatter(tokenizer_config, add_generation_prompt=add_generation_prompt) chat_formatter = hf_tokenizer_config_to_chat_formatter(
tokenizer_config, add_generation_prompt=add_generation_prompt
)
return chat_formatter_to_chat_completion_handler(chat_formatter) return chat_formatter_to_chat_completion_handler(chat_formatter)
@ -463,11 +469,12 @@ def guess_chat_format_from_gguf_metadata(metadata: Dict[str, str]) -> Optional[s
if metadata["tokenizer.chat_template"] == CHATML_CHAT_TEMPLATE: if metadata["tokenizer.chat_template"] == CHATML_CHAT_TEMPLATE:
return "chatml" return "chatml"
if metadata["tokenizer.chat_template"] == MISTRAL_INSTRUCT_CHAT_TEMPLATE: if metadata["tokenizer.chat_template"] == MISTRAL_INSTRUCT_CHAT_TEMPLATE:
return "mistral-instruct" return "mistral-instruct"
return None return None
### Utility functions for formatting chat prompts ### ### Utility functions for formatting chat prompts ###
# TODO: Replace these with jinja2 templates # TODO: Replace these with jinja2 templates
@ -916,9 +923,17 @@ def format_mistral_instruct(
stop = eos stop = eos
prompt = bos prompt = bos
for message in messages: for message in messages:
if message["role"] == "user" and message["content"] is not None and isinstance(message["content"], str): if (
message["role"] == "user"
and message["content"] is not None
and isinstance(message["content"], str)
):
prompt += "[INST] " + message["content"] prompt += "[INST] " + message["content"]
elif message["role"] == "assistant" and message["content"] is not None and isinstance(message["content"], str): elif (
message["role"] == "assistant"
and message["content"] is not None
and isinstance(message["content"], str)
):
prompt += " [/INST]" + message["content"] + eos prompt += " [/INST]" + message["content"] + eos
prompt += " [/INST]" prompt += " [/INST]"
return ChatFormatterResponse(prompt=prompt, stop=stop) return ChatFormatterResponse(prompt=prompt, stop=stop)
@ -958,6 +973,7 @@ def format_openchat(
_prompt = _format_chatml(system_message, _messages, _sep) _prompt = _format_chatml(system_message, _messages, _sep)
return ChatFormatterResponse(prompt=_prompt, stop=_sep) return ChatFormatterResponse(prompt=_prompt, stop=_sep)
# Chat format for Saiga models, see more details and available models: # Chat format for Saiga models, see more details and available models:
# https://huggingface.co/collections/IlyaGusev/saiga2-saigamistral-6505d4ccc3d1e53166b636cd # https://huggingface.co/collections/IlyaGusev/saiga2-saigamistral-6505d4ccc3d1e53166b636cd
@register_chat_format("saiga") @register_chat_format("saiga")
@ -979,8 +995,10 @@ def format_saiga(
_prompt += "<s>bot" _prompt += "<s>bot"
return ChatFormatterResponse(prompt=_prompt.strip()) return ChatFormatterResponse(prompt=_prompt.strip())
# Tricky chat formats that require custom chat handlers # Tricky chat formats that require custom chat handlers
@register_chat_completion_handler("functionary") @register_chat_completion_handler("functionary")
def functionary_chat_handler( def functionary_chat_handler(
llama: llama.Llama, llama: llama.Llama,
@ -1253,7 +1271,8 @@ def functionary_chat_handler(
json.dumps(function_body) json.dumps(function_body)
) )
grammar = llama_grammar.LlamaGrammar.from_string( grammar = llama_grammar.LlamaGrammar.from_string(
llama_grammar.json_schema_to_gbnf(json.dumps(function_body)) llama_grammar.json_schema_to_gbnf(json.dumps(function_body)),
verbose=llama.verbose,
) )
print(grammar_text) print(grammar_text)
except Exception as e: except Exception as e:
@ -1264,11 +1283,14 @@ def functionary_chat_handler(
print(e) print(e)
with suppress_stdout_stderr(disable=llama.verbose): with suppress_stdout_stderr(disable=llama.verbose):
grammar = llama_grammar.LlamaGrammar.from_string( grammar = llama_grammar.LlamaGrammar.from_string(
llama_grammar.JSON_GBNF llama_grammar.JSON_GBNF,
verbose=llama.verbose,
) )
else: else:
with suppress_stdout_stderr(disable=llama.verbose): with suppress_stdout_stderr(disable=llama.verbose):
grammar = llama_grammar.LlamaGrammar.from_string(llama_grammar.JSON_GBNF) grammar = llama_grammar.LlamaGrammar.from_string(
llama_grammar.JSON_GBNF, verbose=llama.verbose
)
completion: llama_types.Completion = llama.create_completion( completion: llama_types.Completion = llama.create_completion(
prompt=new_prompt, prompt=new_prompt,
@ -1365,11 +1387,13 @@ def functionary_v1_v2_chat_handler(
**kwargs, # type: ignore **kwargs, # type: ignore
) -> Union[llama_types.ChatCompletion, Iterator[llama_types.ChatCompletionChunk]]: ) -> Union[llama_types.ChatCompletion, Iterator[llama_types.ChatCompletionChunk]]:
SYSTEM_MESSAGE = """A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. The assistant calls functions with appropriate input when necessary""" SYSTEM_MESSAGE = """A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. The assistant calls functions with appropriate input when necessary"""
tokenizer = llama.tokenizer_ tokenizer = llama.tokenizer_
assert hasattr(tokenizer, "hf_tokenizer"), "Please provide a valid hf_tokenizer_path from https://huggingface.co/meetkai when initializing the Llama class" assert hasattr(
tokenizer, "hf_tokenizer"
), "Please provide a valid hf_tokenizer_path from https://huggingface.co/meetkai when initializing the Llama class"
from transformers import AutoTokenizer from transformers import AutoTokenizer
if "<|START_OF_FUNCTION_CALL|>" in tokenizer.hf_tokenizer.additional_special_tokens: if "<|START_OF_FUNCTION_CALL|>" in tokenizer.hf_tokenizer.additional_special_tokens:
version = "v1" version = "v1"
END_SYSTEM_TOKEN = "<|END_OF_SYSTEM|>" END_SYSTEM_TOKEN = "<|END_OF_SYSTEM|>"
@ -1513,13 +1537,16 @@ def functionary_v1_v2_chat_handler(
"name" "name"
] = f"functions.{message['function_call']['name']}" ] = f"functions.{message['function_call']['name']}"
all_messages.append(message) all_messages.append(message)
if version == "v1": if version == "v1":
suffix = "assistant:\n" suffix = "assistant:\n"
else: else:
suffix = "<|from|>assistant\n<|recipient|>" suffix = "<|from|>assistant\n<|recipient|>"
return tokenizer.hf_tokenizer.apply_chat_template(all_messages, tokenize=False) + suffix return (
tokenizer.hf_tokenizer.apply_chat_template(all_messages, tokenize=False)
+ suffix
)
if tools is not None: if tools is not None:
functions = [tool["function"] for tool in tools if tool["type"] == "function"] functions = [tool["function"] for tool in tools if tool["type"] == "function"]
@ -1529,8 +1556,10 @@ def functionary_v1_v2_chat_handler(
tool_choice if isinstance(tool_choice, str) else tool_choice["function"] tool_choice if isinstance(tool_choice, str) else tool_choice["function"]
) )
prompt = prepare_messages_for_inference(messages, tokenizer, version, functions, tools) prompt = prepare_messages_for_inference(
messages, tokenizer, version, functions, tools
)
# If no tools/functions are provided # If no tools/functions are provided
if function_call is None and (functions is None or len(functions) == 0): if function_call is None and (functions is None or len(functions) == 0):
if version == "v1": if version == "v1":
@ -1538,7 +1567,7 @@ def functionary_v1_v2_chat_handler(
else: else:
stop = STOP_TOKEN stop = STOP_TOKEN
prompt += "all\n<|content|>" prompt += "all\n<|content|>"
completion_or_completion_chunks = llama.create_completion( completion_or_completion_chunks = llama.create_completion(
prompt=prompt, prompt=prompt,
temperature=temperature, temperature=temperature,
@ -1561,9 +1590,9 @@ def functionary_v1_v2_chat_handler(
grammar=grammar, grammar=grammar,
) )
return _convert_completion_to_chat(completion_or_completion_chunks, stream=stream) # type: ignore return _convert_completion_to_chat(completion_or_completion_chunks, stream=stream) # type: ignore
assert stream is False # TODO: support stream mode assert stream is False # TODO: support stream mode
def get_grammar(function_call): def get_grammar(function_call):
function_body = None function_body = None
for function in functions or []: for function in functions or []:
@ -1574,7 +1603,7 @@ def functionary_v1_v2_chat_handler(
if tool["type"] == "function" and tool["function"]["name"] == function_call: if tool["type"] == "function" and tool["function"]["name"] == function_call:
function_body = tool["function"]["parameters"] function_body = tool["function"]["parameters"]
break break
try: try:
with suppress_stdout_stderr(disable=llama.verbose): with suppress_stdout_stderr(disable=llama.verbose):
grammar_text = llama_grammar.json_schema_to_gbnf( grammar_text = llama_grammar.json_schema_to_gbnf(
@ -1592,11 +1621,11 @@ def functionary_v1_v2_chat_handler(
print(e) print(e)
with suppress_stdout_stderr(disable=llama.verbose): with suppress_stdout_stderr(disable=llama.verbose):
grammar = llama_grammar.LlamaGrammar.from_string( grammar = llama_grammar.LlamaGrammar.from_string(
llama_grammar.JSON_GBNF llama_grammar.JSON_GBNF, verbose=llama.verbose
) )
return grammar return grammar
def create_completion(stop): def create_completion(stop):
completion: llama_types.Completion = llama.create_completion( completion: llama_types.Completion = llama.create_completion(
prompt=prompt, prompt=prompt,
@ -1619,11 +1648,11 @@ def functionary_v1_v2_chat_handler(
logits_processor=logits_processor, logits_processor=logits_processor,
grammar=grammar, grammar=grammar,
) )
return completion return completion
function_calls, function_bodies = [], [] function_calls, function_bodies = [], []
if version == "v1": if version == "v1":
# If no or "auto" tool_choice/function_call # If no or "auto" tool_choice/function_call
if function_call is None or ( if function_call is None or (
@ -1632,7 +1661,9 @@ def functionary_v1_v2_chat_handler(
stops = ["\n", END_ASSISTANT_TOKEN] stops = ["\n", END_ASSISTANT_TOKEN]
# If tool_choice/function_call is "none" # If tool_choice/function_call is "none"
elif isinstance(function_call, str) and function_call == "none": elif isinstance(function_call, str) and function_call == "none":
prompt = prepare_messages_for_inference(messages, tokenizer, version, [], []) prompt = prepare_messages_for_inference(
messages, tokenizer, version, [], []
)
stops = END_ASSISTANT_TOKEN stops = END_ASSISTANT_TOKEN
# If tool_choice/function_call is provided # If tool_choice/function_call is provided
elif isinstance(function_call, dict): elif isinstance(function_call, dict):
@ -1647,14 +1678,27 @@ def functionary_v1_v2_chat_handler(
completion = create_completion(stop=stops) completion = create_completion(stop=stops)
completion_text = completion["choices"][0]["text"] completion_text = completion["choices"][0]["text"]
# If the generation does not involve a function call # If the generation does not involve a function call
if START_FUNCTION_CALL_TOKEN not in prompt and START_FUNCTION_CALL_TOKEN not in completion_text: if (
START_FUNCTION_CALL_TOKEN not in prompt
and START_FUNCTION_CALL_TOKEN not in completion_text
):
return _convert_completion_to_chat(completion, stream=stream) # type: ignore return _convert_completion_to_chat(completion, stream=stream) # type: ignore
# If the generation involves a function call in completion, generate the parameters # If the generation involves a function call in completion, generate the parameters
elif START_FUNCTION_CALL_TOKEN not in prompt and START_FUNCTION_CALL_TOKEN in completion_text: elif (
prompt += completion_text.replace(f"{START_FUNCTION_CALL_TOKEN} ", START_FUNCTION_CALL_TOKEN) + "\n" START_FUNCTION_CALL_TOKEN not in prompt
function_calls.append(completion_text.split(START_FUNCTION_CALL_TOKEN)[-1][:-1].strip()) and START_FUNCTION_CALL_TOKEN in completion_text
):
prompt += (
completion_text.replace(
f"{START_FUNCTION_CALL_TOKEN} ", START_FUNCTION_CALL_TOKEN
)
+ "\n"
)
function_calls.append(
completion_text.split(START_FUNCTION_CALL_TOKEN)[-1][:-1].strip()
)
grammar = get_grammar(function_calls[-1]) grammar = get_grammar(function_calls[-1])
completion = create_completion(stop=END_FUNCTION_CALL_TOKEN) completion = create_completion(stop=END_FUNCTION_CALL_TOKEN)
function_bodies.append(completion["choices"][0]["text"].strip()) function_bodies.append(completion["choices"][0]["text"].strip())
@ -1672,7 +1716,10 @@ def functionary_v1_v2_chat_handler(
stops = CONTENT_TOKEN stops = CONTENT_TOKEN
# If tool_choice/function_call is "none" # If tool_choice/function_call is "none"
elif isinstance(function_call, str) and function_call == "none": elif isinstance(function_call, str) and function_call == "none":
prompt = prepare_messages_for_inference(messages, tokenizer, version, [], []) + "all\n<|content|>" prompt = (
prepare_messages_for_inference(messages, tokenizer, version, [], [])
+ "all\n<|content|>"
)
stops = STOP_TOKEN stops = STOP_TOKEN
# If tool_choice/function_call is provided # If tool_choice/function_call is provided
elif isinstance(function_call, dict): elif isinstance(function_call, dict):
@ -1684,15 +1731,17 @@ def functionary_v1_v2_chat_handler(
else: else:
prompt = prompt prompt = prompt
stops = STOP_TOKEN stops = STOP_TOKEN
completion = create_completion(stop=stops) completion = create_completion(stop=stops)
completion_text = completion["choices"][0]["text"] completion_text = completion["choices"][0]["text"]
# If the generation does not involve a function call # If the generation does not involve a function call
if prompt.endswith("all\n<|content|>") and not completion_text.startswith("all"): if prompt.endswith("all\n<|content|>") and not completion_text.startswith(
"all"
):
return _convert_completion_to_chat(completion, stream=stream) # type: ignore return _convert_completion_to_chat(completion, stream=stream) # type: ignore
# Generate model response if the model decides not to call any function # Generate model response if the model decides not to call any function
elif (prompt.endswith(RECIPIENT_TOKEN) and completion_text.startswith("all")): elif prompt.endswith(RECIPIENT_TOKEN) and completion_text.startswith("all"):
prompt += completion_text + CONTENT_TOKEN prompt += completion_text + CONTENT_TOKEN
completion = create_completion(stop=STOP_TOKEN) completion = create_completion(stop=STOP_TOKEN)
return _convert_completion_to_chat(completion, stream=stream) # type: ignore return _convert_completion_to_chat(completion, stream=stream) # type: ignore
@ -1704,7 +1753,7 @@ def functionary_v1_v2_chat_handler(
function_bodies.append(completion["choices"][0]["text"].strip()) function_bodies.append(completion["choices"][0]["text"].strip())
prompt += f"{function_calls[-1]}\n{CONTENT_TOKEN}{function_bodies[-1]}" prompt += f"{function_calls[-1]}\n{CONTENT_TOKEN}{function_bodies[-1]}"
grammar = None grammar = None
# Try to generate the beginning of next turn # Try to generate the beginning of next turn
# If empty completion, break from loop # If empty completion, break from loop
next_turn_completion_text = create_completion( next_turn_completion_text = create_completion(
@ -1718,17 +1767,21 @@ def functionary_v1_v2_chat_handler(
else: else:
function_bodies.append(completion_text.strip()) function_bodies.append(completion_text.strip())
break break
assert "usage" in completion assert "usage" in completion
assert len(function_calls) > 0 assert len(function_calls) > 0
assert len(function_calls) == len(function_bodies) assert len(function_calls) == len(function_bodies)
tool_calls = [] tool_calls = []
for function_call, function_body in zip(function_calls, function_bodies): for function_call, function_body in zip(function_calls, function_bodies):
tool_calls.append( tool_calls.append(
{ {
"id": "call_" + "".join( "id": "call_"
[random.choice(string.ascii_letters + string.digits) for _ in range(24)] + "".join(
[
random.choice(string.ascii_letters + string.digits)
for _ in range(24)
]
), ),
"type": "function", "type": "function",
"function": { "function": {
@ -1924,7 +1977,9 @@ class Llava15ChatHandler:
json.dumps(response_format["schema"]) json.dumps(response_format["schema"])
) )
except Exception as e: except Exception as e:
grammar = llama_grammar.LlamaGrammar.from_string(llama_grammar.JSON_GBNF) grammar = llama_grammar.LlamaGrammar.from_string(
llama_grammar.JSON_GBNF
)
return _convert_completion_to_chat( return _convert_completion_to_chat(
llama.create_completion( llama.create_completion(
@ -1950,3 +2005,601 @@ class Llava15ChatHandler:
), ),
stream=stream, stream=stream,
) )
@register_chat_completion_handler("chatml-function-calling")
def chatml_function_calling(
llama: llama.Llama,
messages: List[llama_types.ChatCompletionRequestMessage],
functions: Optional[List[llama_types.ChatCompletionFunction]] = None,
function_call: Optional[llama_types.ChatCompletionRequestFunctionCall] = None,
tools: Optional[List[llama_types.ChatCompletionTool]] = None,
tool_choice: Optional[llama_types.ChatCompletionToolChoiceOption] = None,
temperature: float = 0.2,
top_p: float = 0.95,
top_k: int = 40,
min_p: float = 0.05,
typical_p: float = 1.0,
stream: bool = False,
stop: Optional[Union[str, List[str]]] = [],
response_format: Optional[llama_types.ChatCompletionRequestResponseFormat] = None,
max_tokens: Optional[int] = None,
presence_penalty: float = 0.0,
frequency_penalty: float = 0.0,
repeat_penalty: float = 1.1,
tfs_z: float = 1.0,
mirostat_mode: int = 0,
mirostat_tau: float = 5.0,
mirostat_eta: float = 0.1,
model: Optional[str] = None,
logits_processor: Optional[llama.LogitsProcessorList] = None,
grammar: Optional[llama.LlamaGrammar] = None,
**kwargs, # type: ignore
) -> Union[
llama_types.CreateChatCompletionResponse,
Iterator[llama_types.CreateChatCompletionStreamResponse],
]:
function_calling_template = (
"{% for message in messages %}"
"<|im_start|>{{ message.role }}\n"
# System message
"{% if message.role == 'system' %}"
"{{ message.content }}"
"{% if tool_calls %}"
"\n\nYou have access to the following functions:\n"
"{% for tool in tools %}"
"\nfunctions.{{ tool.function.name }}:\n"
"{{ tool.function.parameters | tojson }}"
"\n{% endfor %}"
"\n\nYou can respond to users messages with either a single message or one or more function calls."
"\n\nTo respond with a message begin the message with 'message:', use the following format:"
"\n\nmessage:"
"\n<message>"
"\n\nTo respond with one or more function calls begin the message with 'functions.<function_name>:', use the following format:"
"\n\nfunctions.<function_name>:"
'\n{ "arg1": "value1", "arg2": "value2" }'
"\nfunctions.<function_name>:"
'\n{ "arg1": "value1", "arg2": "value2" }'
"{% endif %}"
"\n<|im_end|>\n"
"{% endif %}"
# User message
"{% if message.role == 'user' %}"
"{{ message.content }}"
"\n<|im_end|>\n"
"{% endif %}"
# Assistant message
"{% if message.role == 'assistant' %}"
## Reglar message
"{% if message.content and message.content | length > 0 %}"
"message:\n"
"{{ message.content }}"
"\n<|im_end|>\n"
"{% endif %}"
## Function calls
"{% if message.tool_calls %}"
"{% for tool_call in message.tool_calls %}"
"functions.{{ tool_call.function.name }}:\n"
"{{ tool_call.function.arguments }}"
"{% endfor %}"
"\n<|im_end|>\n"
"{% endif %}"
"{% endif %}"
"{% endfor %}"
)
template_renderer = jinja2.Environment(
loader=jinja2.BaseLoader(),
autoescape=jinja2.select_autoescape(["html", "xml"]),
undefined=jinja2.StrictUndefined,
).from_string(function_calling_template)
# Convert legacy functions to tools
if functions is not None:
tools = [
{
"type": "function",
"function": function,
}
for function in functions
]
# Convert legacy function_call to tool_choice
if function_call is not None:
if isinstance(function_call, str) and (
function_call == "none" or function_call == "auto"
):
tool_choice = function_call
if isinstance(function_call, dict) and "name" in function_call:
tool_choice = {
"type": "function",
"function": {
"name": function_call["name"],
},
}
# Case 1: No tool choice by user
if (
tool_choice is None
or (isinstance(tool_choice, str) and tool_choice == "none")
or tools is None
or len(tools) == 0
):
prompt = template_renderer.render(
messages=messages,
tools=[],
tool_calls=None,
)
if response_format is not None and response_format["type"] == "json_object":
try:
grammar = (
llama_grammar.LlamaGrammar.from_json_schema(
json.dumps(response_format["schema"])
)
if "schema" in response_format
else None
)
except Exception as e:
if llama.verbose:
print(
"Failed to parse response format as JSON schema, falling back to default grammar"
)
print(e)
grammar = (
llama_grammar.LlamaGrammar.from_string(llama_grammar.JSON_GBNF)
if grammar is None
else grammar
)
return _convert_completion_to_chat(
llama.create_completion(
prompt=prompt,
temperature=temperature,
top_p=top_p,
top_k=top_k,
min_p=min_p,
typical_p=typical_p,
stream=stream,
stop=stop,
max_tokens=max_tokens,
presence_penalty=presence_penalty,
frequency_penalty=frequency_penalty,
repeat_penalty=repeat_penalty,
tfs_z=tfs_z,
mirostat_mode=mirostat_mode,
mirostat_tau=mirostat_tau,
mirostat_eta=mirostat_eta,
model=model,
logits_processor=logits_processor,
grammar=grammar,
),
stream=stream,
)
def _convert_completion_to_chat_function(
tool_name: str,
completion_or_chunks: Union[
llama_types.CreateCompletionResponse,
Iterator[llama_types.CreateCompletionStreamResponse],
],
stream: bool,
):
if not stream:
completion: llama_types.CreateCompletionResponse = completion_or_chunks # type: ignore
assert "usage" in completion
tool_id = "call_" + "_0_" + tool_name + "_" + completion["id"]
# TODO: Fix for legacy function calls
chat_completion: llama_types.CreateChatCompletionResponse = {
"id": "chat" + completion["id"],
"object": "chat.completion",
"created": completion["created"],
"model": completion["model"],
"choices": [
{
"index": 0,
"message": {
"role": "assistant",
"content": None,
"function_call": {
"name": tool_name,
"arguments": completion["choices"][0]["text"],
},
"tool_calls": [
{
"id": tool_id,
"type": "function",
"function": {
"name": tool_name,
"arguments": completion["choices"][0]["text"],
},
}
],
},
"finish_reason": "tool_calls",
}
],
"usage": completion["usage"],
}
return chat_completion
else:
chunks: Iterator[llama_types.CreateCompletionStreamResponse] = completion_or_chunks # type: ignore
def _stream_response_to_function_stream(
chunks: Iterator[llama_types.CreateCompletionStreamResponse],
) -> Iterator[llama_types.CreateChatCompletionStreamResponse]:
# blank first message
first = True
id_ = None
created = None
model = None
tool_id = None
for chunk in chunks:
if first:
id_ = "chat" + chunk["id"]
created = chunk["created"]
model = chunk["model"]
tool_id = "call_" + "_0_" + tool_name + "_" + chunk["id"]
yield {
"id": id_,
"object": "chat.completion.chunk",
"created": created,
"model": model,
"choices": [
{
"index": 0,
"finish_reason": None,
"logprobs": None,
"delta": {
"role": "assistant",
"content": None,
"function_call": None,
"tool_calls": None,
},
}
],
}
yield {
"id": "chat" + chunk["id"],
"object": "chat.completion.chunk",
"created": chunk["created"],
"model": chunk["model"],
"choices": [
{
"index": 0,
"finish_reason": None,
"logprobs": None,
"delta": {
"role": None,
"content": None,
"function_call": {
"name": tool_name,
"arguments": chunk["choices"][0]["text"],
},
"tool_calls": [
{
"index": 0,
"id": tool_id,
"type": "function",
"function": {
"name": tool_name,
"arguments": "",
},
}
],
},
}
],
}
first = False
continue
assert tool_id is not None
yield {
"id": "chat" + chunk["id"],
"object": "chat.completion.chunk",
"created": chunk["created"],
"model": chunk["model"],
"choices": [
{
"index": 0,
"finish_reason": None,
"logprobs": None,
"delta": {
"role": None,
"content": None,
"function_call": {
"name": tool_name,
"arguments": chunk["choices"][0]["text"],
},
"tool_calls": [
{
"index": 0,
"id": tool_id,
"type": "function",
"function": {
"name": tool_name,
"arguments": chunk["choices"][0][
"text"
],
},
}
],
},
}
],
}
if id_ is not None and created is not None and model is not None:
yield {
"id": id_,
"object": "chat.completion.chunk",
"created": created,
"model": model,
"choices": [
{
"index": 0,
"finish_reason": "tool_calls",
"logprobs": None,
"delta": {
"role": None,
"content": None,
"function_call": None,
"tool_calls": None,
},
}
],
}
return _stream_response_to_function_stream(chunks)
# Case 2: Tool choice by user
if isinstance(tool_choice, dict):
tool_name = tool_choice["function"]["name"]
tool = next(
(tool for tool in tools if tool["function"]["name"] == tool_name), None
)
if tool is None:
raise ValueError(f"Tool with name '{tool_name}' not found in tools")
prompt = template_renderer.render(
messages=messages,
tools=tools,
tool_calls=True,
)
prompt += f"functions.{tool_name}:\n"
try:
grammar = llama_grammar.LlamaGrammar.from_json_schema(
json.dumps(tool["function"]["parameters"]), verbose=llama.verbose
)
except Exception as e:
grammar = llama_grammar.LlamaGrammar.from_string(
llama_grammar.JSON_GBNF, verbose=llama.verbose
)
if llama.verbose:
print(
"Failed to parse function body as JSON schema, falling back to default grammar"
)
print(e)
completion_or_chunks = llama.create_completion(
prompt=prompt,
temperature=temperature,
top_p=top_p,
top_k=top_k,
min_p=min_p,
typical_p=typical_p,
stream=stream,
stop=stop,
max_tokens=max_tokens,
presence_penalty=presence_penalty,
frequency_penalty=frequency_penalty,
repeat_penalty=repeat_penalty,
tfs_z=tfs_z,
mirostat_mode=mirostat_mode,
mirostat_tau=mirostat_tau,
mirostat_eta=mirostat_eta,
model=model,
logits_processor=logits_processor,
grammar=grammar,
)
return _convert_completion_to_chat_function(
tool_name, completion_or_chunks, stream
)
# Case 3: Automatic tool choice
assert isinstance(tool_choice, str) and tool_choice == "auto"
function_names = " | ".join(
[f'''"functions.{tool['function']['name']}:"''' for tool in tools]
)
initial_gbnf_tool_grammar = (
"""root ::= functions | "message:"\n"""
f"""functions ::= {function_names}\n"""
)
follow_up_gbnf_tool_grammar = (
"""root ::= functions | "<|im_end|>"\n"""
f"""functions ::= {function_names}\n"""
)
prompt = template_renderer.render(
messages=messages,
tools=tools,
tool_calls=True,
)
completion_or_chunks = llama.create_completion(
prompt=prompt,
temperature=0,
top_p=top_p,
top_k=top_k,
min_p=min_p,
typical_p=typical_p,
stream=False,
stop=[":"],
max_tokens=None,
presence_penalty=presence_penalty,
frequency_penalty=frequency_penalty,
repeat_penalty=repeat_penalty,
tfs_z=tfs_z,
mirostat_mode=mirostat_mode,
mirostat_tau=mirostat_tau,
mirostat_eta=mirostat_eta,
model=model,
logits_processor=logits_processor,
grammar=llama_grammar.LlamaGrammar.from_string(
initial_gbnf_tool_grammar, verbose=llama.verbose
),
)
completion: llama_types.CreateCompletionResponse = completion_or_chunks # type: ignore
text = completion["choices"][0]["text"]
if "message" in text:
return _convert_completion_to_chat(
llama.create_completion(
prompt=prompt + "message:\n",
temperature=temperature,
top_p=top_p,
top_k=top_k,
min_p=min_p,
typical_p=typical_p,
stream=stream,
stop=["<|im_end|>"],
max_tokens=None,
presence_penalty=presence_penalty,
frequency_penalty=frequency_penalty,
repeat_penalty=repeat_penalty,
tfs_z=tfs_z,
mirostat_mode=mirostat_mode,
mirostat_tau=mirostat_tau,
mirostat_eta=mirostat_eta,
model=model,
logits_processor=logits_processor,
grammar=llama_grammar.LlamaGrammar.from_string(
follow_up_gbnf_tool_grammar, verbose=llama.verbose
),
),
stream=stream,
)
# One or more function calls
tool_name = text[len("functions.") :]
tool = next((tool for tool in tools if tool["function"]["name"] == tool_name), None)
if not stream:
completions = []
completions_tool_name = []
while tool is not None:
prompt += f"functions.{tool_name}:\n"
try:
grammar = llama_grammar.LlamaGrammar.from_json_schema(
json.dumps(tool["function"]["parameters"]), verbose=llama.verbose
)
except Exception as e:
grammar = llama_grammar.LlamaGrammar.from_string(
llama_grammar.JSON_GBNF, verbose=llama.verbose
)
if llama.verbose:
print(
"Failed to parse function body as JSON schema, falling back to default grammar"
)
print(e)
completion_or_chunks = llama.create_completion(
prompt=prompt,
temperature=temperature,
top_p=top_p,
top_k=top_k,
min_p=min_p,
typical_p=typical_p,
stream=False,
stop=stop,
max_tokens=None,
presence_penalty=presence_penalty,
frequency_penalty=frequency_penalty,
repeat_penalty=repeat_penalty,
tfs_z=tfs_z,
mirostat_mode=mirostat_mode,
mirostat_tau=mirostat_tau,
mirostat_eta=mirostat_eta,
model=model,
logits_processor=logits_processor,
grammar=grammar,
)
completions.append(completion_or_chunks)
completions_tool_name.append(tool_name)
prompt += completion_or_chunks["choices"][0]["text"]
prompt += "\n"
response = llama.create_completion(
prompt=prompt,
temperature=temperature,
top_p=top_p,
top_k=top_k,
min_p=min_p,
typical_p=typical_p,
stream=False,
stop=stop,
max_tokens=None,
presence_penalty=presence_penalty,
frequency_penalty=frequency_penalty,
repeat_penalty=repeat_penalty,
tfs_z=tfs_z,
mirostat_mode=mirostat_mode,
mirostat_tau=mirostat_tau,
mirostat_eta=mirostat_eta,
model=model,
logits_processor=logits_processor,
grammar=llama_grammar.LlamaGrammar.from_string(
follow_up_gbnf_tool_grammar, verbose=llama.verbose
),
)
tool_name = response["choices"][0]["text"][len("functions.") :]
tool = next(
(tool for tool in tools if tool["function"]["name"] == tool_name), None
)
# Merge completions
function_call = {
"function_call": {
"name": tool_name,
"arguments": completions[0]["choices"][0]["text"],
}
} if len(completions) == 1 else {}
return {
"id": "chat" + completion["id"],
"object": "chat.completion",
"created": completion["created"],
"model": completion["model"],
"choices": [
{
"finish_reason": "tool_calls",
"index": 0,
"message": {
"role": "assistant",
"content": None,
"tool_calls": [
{
"id": "call_"
+ f"_{i}_"
+ tool_name
+ "_"
+ completion["id"],
"type": "function",
"function": {
"name": tool_name,
"arguments": completion["choices"][0]["text"],
},
}
for i, (tool_name, completion) in enumerate(
zip(completions_tool_name, completions)
)
],
**function_call
},
}
],
"usage": {
"completion_tokens": sum(
completion["usage"]["completion_tokens"]
for completion in completions
),
"prompt_tokens": sum(
completion["usage"]["prompt_tokens"] for completion in completions
),
"total_tokens": sum(
completion["usage"]["total_tokens"] for completion in completions
),
},
}
raise ValueError("Automatic streaming tool choice is not supported")

11
llama_cpp/llama_types.py
View file

@ -97,7 +97,7 @@ class CreateChatCompletionResponse(TypedDict):
class ChatCompletionMessageToolCallChunkFunction(TypedDict): class ChatCompletionMessageToolCallChunkFunction(TypedDict):
name: str name: Optional[str]
arguments: str arguments: str
@ -118,12 +118,12 @@ class ChatCompletionStreamResponseDeltaFunctionCall(TypedDict):
class ChatCompletionStreamResponseDelta(TypedDict): class ChatCompletionStreamResponseDelta(TypedDict):
content: NotRequired[str] content: NotRequired[Optional[str]]
function_call: NotRequired[ function_call: NotRequired[
ChatCompletionStreamResponseDeltaFunctionCall Optional[ChatCompletionStreamResponseDeltaFunctionCall]
] # DEPRECATED ] # DEPRECATED
tool_calls: NotRequired[List[ChatCompletionMessageToolCallChunk]] tool_calls: NotRequired[Optional[List[ChatCompletionMessageToolCallChunk]]]
role: NotRequired[Literal["system", "user", "assistant", "tool"]] role: NotRequired[Optional[Literal["system", "user", "assistant", "tool"]]]
class ChatCompletionStreamResponseChoice(TypedDict): class ChatCompletionStreamResponseChoice(TypedDict):
@ -132,6 +132,7 @@ class ChatCompletionStreamResponseChoice(TypedDict):
ChatCompletionStreamResponseDelta, ChatCompletionStreamResponseDeltaEmpty ChatCompletionStreamResponseDelta, ChatCompletionStreamResponseDeltaEmpty
] ]
finish_reason: Optional[Literal["stop", "length", "tool_calls", "function_call"]] finish_reason: Optional[Literal["stop", "length", "tool_calls", "function_call"]]
logprobs: NotRequired[Optional[CompletionLogprobs]]
class CreateChatCompletionStreamResponse(TypedDict): class CreateChatCompletionStreamResponse(TypedDict):