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Make the code render as Python.
Signed-off-by: Sherlock113 <[email protected]>
Fixes #856 The code this PR removes introduces an artificial and erroneous loop transition in every final state that is always traversed, regardless of the generation. The comment doesn't make sense in my opinion, as the `if` above just handles exactly this case. Removing this piece of code fixes the bug that surfaced in the upgrade of outlines in the vLLM integration.
Fixes #823 This comment details the issues error: #823 (comment) The reproduction code provided results in a json schema with `OneOf[pets]`: ``` class Model(BaseModel): pet: Union[Cat, Dog] = Field(..., discriminator='pet_type') ``` Before this PR: `OneOf` uses negative lookaheads to assert that only one schema member is included. This is illegal in `interegular`, more details available here: #456 After `OneOf` uses or-joined non-capturing groups which don't have the same issues with `interegular`.
In the outlines docs, we have the example
```python
import outlines
def my_tool(arg1: str, arg2: int):
"""Tool description.
The rest of the docstring
"""
pass
@outlines.prompt
def tool_prompt(question, tool):
"""{{ question }}
COMMANDS
1. {{ tool | name }}: {{ tool | description }}, args: {{ tool | args }}
{{ tool | source }}
"""
prompt = tool_prompt("Can you do something?", my_tool)
print(prompt)
```
However, when I tried running this code, it did not work because the
`args` filter used in `{{ tool | args }}` was not implemented. I
implemented the `args` filter so now this example works.
Now the args filter will output all of the arguments with the type
annotations and default values (if they are provided).
Example:
```python
from typing import List
def foo(x, y: str, z: List[int]=[1, 2, 3]):
pass
@outlines.prompt
def tool_prompt(fn):
"""My args: {{ fn | args }}"""
prompt = tool_prompt(foo)
print(prompt)
```
which outputs
```python
My args: x, y: str, z: List[int] = [1, 2, 3]
```
Hi, Thank you for this great library! It seems that the docstrings are not rendered correctly in the docs. I think we should explicitly set the `docstring_style` because [it defaults to `"google"`](https://mkdocstrings.github.io/python/usage/configuration/docstrings/#docstring_style) but outlines is using numpy. Before:  After:  There seem to be other issues in the docstrings: - for example [`Properties`](https://github.com/dottxt-ai/outlines/blob/main/outlines/models/openai.py#L23) should be [`Attributes`](https://numpydoc.readthedocs.io/en/latest/format.html#parameters) - only openai and transformers models are present in the [api reference](https://github.com/dottxt-ai/outlines/blob/main/docs/api/models.md) I'm happy to make followup PRs for those. Please let me know if I missed something, I couldn't find related issues/PRs.
Use `is` and `is not` for type comparisons, or `isinstance()` for isinstance checks
the old library structure has not been updated to reflect the present one.
Before this commit, when you ran pytest -k specific_test, it spawned dozens of the same skipped warnings message on stdout... IMHO, that was not ideal ^^ Bug introduced in d32dfde
Allow giving custom filters to the prompt decorator
```
def reverses: str) -> str:
return s[::-1]
@prompt(filters={ 'reverse': reverse })
def reverse_prompt(text):
'''{{ text | reverse }}'''
prompt = reverse_prompt("Hello")
print(prompt)
>>> "olleH"
```
There's an extra `outlines.generate` row in the feature matrix docs. This removes it. I also modified the markdown syntax for one header to use ** rather than __, consistent with the rest of the table.
We have noticing the following error with a recent version of outlines when used with MLX: ``` TypeError: argument 'token_id': 'float' object cannot be interpreted as an integer At: /.../outlines_core/fsm/guide.py(294): get_next_state /.../outlines/processors/structured.py(101): process_logits /.../outlines/processors/base_logits_processor.py(90): __call__ ``` The issue is that the MLX array of tokens, which are integers, are being force-converted to floats, even though outlines expects an integer array. This is because all MLX arrays are being converted to `float32`, even when it's not necessarily appropriate, like in this case. Looking at the [commented link](https://ml-explore.github.io/mlx/build/html/usage/numpy.html#pytorch), the advice was to convert to `float32` only for `bfloat16`, because numpy does not support `bfloat16`. Now the MLX `_to_torch` implementation matches the other array libraries, none of the other libraries are being force-casted to float
The existing README has underwhelming or incorrect results (Example is underwhelming #1347) due to lack of templating for instruct models. This adds special tokens for each instruct model call, as well as provide comments on how to obtain/produce special tokens. --------- Co-authored-by: Victoria Terenina <[email protected]>
Also add instructions about different outlines "flavors"! Co-authored-by: Cameron Pfiffer <[email protected]>
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