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Merge tag 'v0.2.17' into main

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Andrei Betlen 2023-11-14 15:32:12 -05:00
commit ca30d898e9
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5
CHANGELOG.md
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@ -7,6 +7,11 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
## [Unreleased]
## [0.2.17]
- Update llama.cpp to ggerganov/llama.cpp@df9d1293defe783f42bc83af732d3c670552c541
- Hotfix: Set `CUDA_ARCHITECTURES=OFF` for `llava_shared` target on Windows by @abetlen in 4388f3341413110217b98c4f097ac5c590bdf40b
## [0.2.16]
- Update llama.cpp to ggerganov/llama.cp@a75fa576abba9d37f463580c379e4bbf1e1ad03c

8
CMakeLists.txt
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@ -6,6 +6,8 @@ option(LLAMA_BUILD "Build llama.cpp shared library and install alongside python
if (LLAMA_BUILD)
set(BUILD_SHARED_LIBS "On")
# Building llama
if (APPLE AND NOT CMAKE_SYSTEM_PROCESSOR MATCHES "arm64")
# Need to disable these llama.cpp flags on Apple x86_64,
# otherwise users may encounter invalid instruction errors
@ -41,8 +43,14 @@ if (LLAMA_BUILD)
FILES $<TARGET_RUNTIME_DLLS:llama>
DESTINATION ${CMAKE_CURRENT_SOURCE_DIR}/llama_cpp
)
# Building llava
add_subdirectory(vendor/llama.cpp/examples/llava)
set_target_properties(llava_shared PROPERTIES OUTPUT_NAME "llava")
# Set CUDA_ARCHITECTURES to OFF on windows
if (WIN32)
set_target_properties(llava_shared PROPERTIES CUDA_ARCHITECTURES OFF)
endif()
install(
TARGETS llava_shared
LIBRARY DESTINATION ${SKBUILD_PLATLIB_DIR}/llama_cpp

123
examples/notebooks/Functions.ipynb
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@ -1,15 +1,41 @@
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Functions\n",
"\n",
"The OpenAI compatbile web server in `llama-cpp-python` supports function calling.\n",
"\n",
"Function calling allows API clients to specify a schema that gives the model a format it should respond in.\n",
"Function calling in `llama-cpp-python` works by combining models pretrained for function calling such as [`functionary`](https://huggingface.co/abetlen/functionary-7b-v1-GGUF) with constrained sampling to produce a response that is compatible with the schema.\n",
"\n",
"Note however that this improves but does not guarantee that the response will be compatible with the schema.\n",
"\n",
"## Requirements\n",
"\n",
"Before we begin you will need the following:\n",
"\n",
"- A running `llama-cpp-python` server with a function calling compatible model. [See here](https://llama-cpp-python.readthedocs.io/en/latest/server/#function-calling)\n",
"- The OpenAI Python Client `pip install openai`\n",
"- (Optional) The Instructor Python Library `pip install instructor`\n",
"\n",
"## Function Calling with OpenAI Python Client\n",
"\n",
"We'll start with a basic demo that only uses the OpenAI Python Client."
]
},
{
"cell_type": "code",
"execution_count": 29,
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"ChatCompletion(id='chatcmpl-b6dcbb47-1120-4761-8cd9-83542c97647b', choices=[Choice(finish_reason='stop', index=0, message=ChatCompletionMessage(content=\"The current temperature in San Francisco is 72 degrees Fahrenheit. It's a sunny day with clear skies, making it perfect for outdoor activities.\\n \", role='assistant', function_call=None, tool_calls=None))], created=1699602158, model='gpt-3.5-turbo-1106', object='chat.completion', system_fingerprint=None, usage=CompletionUsage(completion_tokens=38, prompt_tokens=135, total_tokens=173))\n"
"ChatCompletion(id='chatcmpl-a2d9eb9f-7354-472f-b6ad-4d7a807729a3', choices=[Choice(finish_reason='stop', index=0, message=ChatCompletionMessage(content='The current weather in San Francisco is **72°F** (22°C).\\n ', role='assistant', function_call=None, tool_calls=None))], created=1699638365, model='gpt-3.5-turbo-1106', object='chat.completion', system_fingerprint=None, usage=CompletionUsage(completion_tokens=22, prompt_tokens=136, total_tokens=158))\n"
]
}
],
@ -20,7 +46,7 @@
"\n",
"client = openai.OpenAI(\n",
" api_key = \"sk-xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx\", # can be anything\n",
" base_url = \"http://100.64.159.73:8000/v1\"\n",
" base_url = \"http://100.64.159.73:8000/v1\" # NOTE: Replace with IP address and port of your llama-cpp-python server\n",
")\n",
"\n",
"# Example dummy function hard coded to return the same weather\n",
@ -100,9 +126,32 @@
"print(run_conversation())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Function Calling with Instructor\n",
"\n",
"The above example is a bit verbose and requires you to manually verify the schema.\n",
"\n",
"For our next examples we'll use the `instructor` library to simplify the process and accomplish a number of different tasks with function calling.\n",
"\n",
"You'll first need to install the [`instructor`](https://github.com/jxnl/instructor/).\n",
"\n",
"You can do so by running the following command in your terminal:\n",
"\n",
"```bash\n",
"pip install instructor\n",
"```\n",
"\n",
"Below we'll go through a few basic examples taken directly from the [instructor cookbook](https://jxnl.github.io/instructor/)\n",
"\n",
"## Basic Usage"
]
},
{
"cell_type": "code",
"execution_count": 30,
"execution_count": 5,
"metadata": {},
"outputs": [
{
@ -140,10 +189,27 @@
]
},
{
"cell_type": "code",
"execution_count": 31,
"cell_type": "markdown",
"metadata": {},
"outputs": [],
"source": [
"## Text Classification\n",
"\n",
"### Single-Label Classification"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"class_label=<Labels.SPAM: 'spam'>\n"
]
}
],
"source": [
"import enum\n",
"\n",
@ -172,19 +238,27 @@
" ) # type: ignore\n",
"\n",
"prediction = classify(\"Hello there I'm a Nigerian prince and I want to give you money\")\n",
"assert prediction.class_label == Labels.SPAM"
"assert prediction.class_label == Labels.SPAM\n",
"print(prediction)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Multi-Label Classification"
]
},
{
"cell_type": "code",
"execution_count": 32,
"execution_count": 12,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"class_labels=[<MultiLabels.BILLING: 'billing'>, <MultiLabels.TECH_ISSUE: 'tech_issue'>]\n"
"class_labels=[<MultiLabels.TECH_ISSUE: 'tech_issue'>, <MultiLabels.BILLING: 'billing'>]\n"
]
}
],
@ -223,16 +297,27 @@
"print(prediction)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Self-Critique"
]
},
{
"cell_type": "code",
"execution_count": 33,
"execution_count": 13,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"question='What is the meaning of life?' answer='The meaning of life, according to the Devil, is to live a life of sin and debauchery.'\n"
"question='What is the meaning of life?' answer='According to the Devil, the meaning of life is to live a life of sin and debauchery.'\n",
"1 validation error for QuestionAnswerNoEvil\n",
"answer\n",
" Assertion failed, The statement promotes sin and debauchery, which can be considered objectionable. [type=assertion_error, input_value='According to the Devil, ... of sin and debauchery.', input_type=str]\n",
" For further information visit https://errors.pydantic.dev/2.3/v/assertion_error\n"
]
}
],
@ -294,6 +379,13 @@
" print(e)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Answering Questions with Validated Citations"
]
},
{
"cell_type": "code",
"execution_count": 42,
@ -366,13 +458,6 @@
"qa = ask_ai(question, context)\n",
"print(qa)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {

2
llama_cpp/__init__.py
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@ -1,4 +1,4 @@
from .llama_cpp import *
from .llama import *
__version__ = "0.2.16"
__version__ = "0.2.17"

2
vendor/llama.cpp vendored

@ -1 +1 @@
Subproject commit a75fa576abba9d37f463580c379e4bbf1e1ad03c
Subproject commit df9d1293defe783f42bc83af732d3c670552c541