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version update post release v1.2.2 (#1005)

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Chi Wang 2023-04-24 21:48:17 -07:00 committed by GitHub
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4
notebook/autogen_chatgpt.ipynb
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@ -23,7 +23,7 @@
"\n",
"FLAML requires `Python>=3.7`. To run this notebook example, please install flaml with the [openai,blendsearch] option:\n",
"```bash\n",
"pip install flaml[openai,blendsearch]==1.2.1\n",
"pip install flaml[openai,blendsearch]==1.2.2\n",
"```"
]
},
@ -40,7 +40,7 @@
},
"outputs": [],
"source": [
"# %pip install flaml[openai,blendsearch]==1.2.1 datasets"
"# %pip install flaml[openai,blendsearch]==1.2.2 datasets"
]
},
{

4
notebook/autogen_openai.ipynb
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@ -23,7 +23,7 @@
"\n",
"FLAML requires `Python>=3.7`. To run this notebook example, please install flaml with the [autogen,blendsearch] option:\n",
"```bash\n",
"pip install flaml[autogen,blendsearch]==1.2.1\n",
"pip install flaml[autogen,blendsearch]==1.2.2\n",
"```"
]
},
@ -40,7 +40,7 @@
},
"outputs": [],
"source": [
"# %pip install flaml[autogen,blendsearch]==1.2.1 datasets"
"# %pip install flaml[autogen,blendsearch]==1.2.2 datasets"
]
},
{

4
notebook/research/autogen_code.ipynb
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@ -21,7 +21,7 @@
"\n",
"FLAML requires `Python>=3.7`. To run this notebook example, please install flaml with the [autogen] option:\n",
"```bash\n",
"pip install flaml[autogen]==1.2.1\n",
"pip install flaml[autogen]==1.2.2\n",
"```"
]
},
@ -38,7 +38,7 @@
},
"outputs": [],
"source": [
"# %pip install flaml[autogen]==1.2.1 datasets"
"# %pip install flaml[autogen]==1.2.2 datasets"
]
},
{

4
notebook/research/math_level5counting.ipynb
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@ -21,7 +21,7 @@
"\n",
"FLAML requires `Python>=3.7`. To run this notebook example, please install flaml with the [openai] option:\n",
"```bash\n",
"pip install flaml[openai]==1.2.1\n",
"pip install flaml[openai]==1.2.2\n",
"```"
]
},
@ -38,7 +38,7 @@
},
"outputs": [],
"source": [
"# %pip install flaml[openai]==1.2.1 datasets"
"# %pip install flaml[openai]==1.2.2 datasets"
]
},
{

8
website/docs/Examples/AutoGen-OpenAI.md
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@ -5,9 +5,9 @@ In this example, we will tune several hyperparameters for the OpenAI's completio
### Prerequisites
Install the [autogen,blendsearch] option. The OpenAI integration is in preview.
Install the [autogen,blendsearch] option.
```bash
pip install "flaml[autogen,blendsearch]==1.2.1 datasets"
pip install "flaml[autogen,blendsearch]==1.2.2 datasets"
```
Setup your OpenAI key:
@ -64,7 +64,9 @@ Before starting tuning, you need to define the metric for the optimization. For
from functools import partial
from flaml.autogen.code_utils import eval_function_completions, generate_assertions
eval_with_generated_assertions = partial(eval_function_completions, assertions=generate_assertions)
eval_with_generated_assertions = partial(
eval_function_completions, assertions=generate_assertions,
)
```
This function will first generate assertion statements for each problem. Then, it uses the assertions to select the generated responses.

2
website/docs/Use-Cases/Auto-Generation.md
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@ -1,6 +1,6 @@
# Auto Generation
`flaml.autogen` is a subpackage for automating generation tasks. It uses [`flaml.tune`](../reference/tune/tune) to find good hyperparameter configurations under budget constraints.
`flaml.autogen` is a package for automating generation tasks (in preview). It uses [`flaml.tune`](../reference/tune/tune) to find good hyperparameter configurations under budget constraints.
Such optimization has several benefits:
* Maximize the utility out of using expensive foundation models.
* Reduce the inference cost by using cheaper models or configurations which achieve equal or better performance.