Release v0.2.15 & Fix load-8bit & Improve documentations
diff --git a/README.md b/README.md
index 8c3ce53503..cee16abb8d 100644
--- a/README.md
+++ b/README.md
@@ -205,10 +205,6 @@ CUDA_VISIBLE_DEVICES=1 python3 -m fastchat.serve.model_worker --model-path lmsys
 ```bash
 python3 -m fastchat.serve.gradio_web_server_multi
 ```
-- You can protect your webserver with Gradio's Authentication with a password file. The password file should contain one or more "user:password" pairs in this format: `u1:p1,u2:p2,u3:p3`
-```bash
-python3 -m fastchat.serve.gradio_web_server --gradio-auth-path login.txt
-```
 
 ## API
 ### OpenAI-Compatible RESTful APIs & SDK
diff --git a/docs/langchain_integration.md b/docs/langchain_integration.md
index db9e7a124d..a59d739ab1 100644
--- a/docs/langchain_integration.md
+++ b/docs/langchain_integration.md
@@ -50,11 +50,7 @@ If you meet the following OOM error while creating embeddings, please set a smal
 openai.error.APIError: Invalid response object from API: '{"object":"error","message":"**NETWORK ERROR DUE TO HIGH TRAFFIC. PLEASE REGENERATE OR REFRESH THIS PAGE.**\\n\\n(CUDA out of memory. Tried to allocate xxx MiB (GPU 0; xxx GiB total capacity; xxx GiB already allocated; xxx MiB free; xxx GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation.  See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF)","code":50002}' (HTTP response code was 400)
 ~~~
 
-You can try
-
-~~~bash
-export FASTCHAT_WORKER_API_EMBEDDING_BATCH_SIZE=1
-~~~
+You can try `export FASTCHAT_WORKER_API_EMBEDDING_BATCH_SIZE=1`.
 
 ## Try local LangChain
 
diff --git a/fastchat/model/compression.py b/fastchat/model/compression.py
index 886eba27c4..2880384f01 100644
--- a/fastchat/model/compression.py
+++ b/fastchat/model/compression.py
@@ -5,7 +5,7 @@
 
 from accelerate import init_empty_weights
 from accelerate.utils import set_module_tensor_to_device
-from huggingface_hub import hf_hub_download
+from huggingface_hub import snapshot_download
 import torch
 from torch import Tensor
 from torch.nn import functional as F
@@ -123,8 +123,7 @@ def load_compress_model(
         base_pattern = os.path.join(model_path, "pytorch_model*.bin")
     else:
         # `model_path` is a cached Hugging Face repo
-        cf = hf_hub_download(model_path, "config.json")
-        model_path = os.path.dirname(cf)
+        model_path = snapshot_download(model_path, revision=revision)
         base_pattern = os.path.join(model_path, "pytorch_model*.bin")
 
     files = glob.glob(base_pattern)
