Merge branch 'main' of github.com:probcomp/hfppl

README.md

@@ -1,8 +1,6 @@
 # LLaMPPL + HuggingFace
 
-<div align="center">
 [![docs](https://github.com/probcomp/hfppl/actions/workflows/docs.yml/badge.svg)](https://probcomp.github.io/hfppl)
-</div>
 
 LLaMPPL is a research prototype for language model probabilistic programming: specifying language generation tasks by writing probabilistic programs that combine calls to LLMs, symbolic program logic, and probabilistic conditioning. To solve these tasks, LLaMPPL uses a specialized sequential Monte Carlo inference algorithm. This technique, SMC steering, is described in [our recent workshop abstract](https://arxiv.org/abs/2306.03081).
 
@@ -96,4 +94,4 @@ Further documentation can be found at https://probcomp.github.io/hfppl.
 ## Installation
 To get started, clone this repository and run `pip install .`
 
-The example `examples/hard_constraints.py` uses LLaMA2, which requires authorization from Meta. To run it, create a file `examples/hf_api_token.py`, with a constant `HF_AUTH_TOKEN` set to your HuggingFace API token. Alternatively, you can switch to a different (open) model by changing the first argument of `CachedCausalLM.from_pretrained` to another HuggingFace model identifier.
+The example `examples/hard_constraints.py` uses LLaMA2, which requires authorization from Meta. To run it, create a file `examples/hf_api_token.py`, with a constant `HF_AUTH_TOKEN` set to your HuggingFace API token. Alternatively, you can switch to a different (open) model by changing the first argument of `CachedCausalLM.from_pretrained` to another HuggingFace model identifier.

mkdocs.yml

@@ -2,6 +2,8 @@ site_name: LLaMPPL Docs
 
 theme:
   name: "material"
+  palette:
+    scheme: slate
 
 markdown_extensions:
   - pymdownx.highlight:
@@ -33,4 +35,4 @@ nav:
     - batching.md
     - caching.md
     - immutability.md
-  - Code Reference: reference/
+  - Code Reference: reference/