Update README.md

README.md

@@ -96,7 +96,7 @@ transformers==4.37.2
 from transformers import AutoModelForSequenceClassification
 import torch
 
-model_name = "OpenBMB/MiniCPM-Reranker-Light"
+model_name = "openbmb/MiniCPM-Reranker-Light"
 model = AutoModelForSequenceClassification.from_pretrained(model_name, trust_remote_code=True, torch_dtype=torch.float16).to("cuda")
 # You can also use the following code to use flash_attention_2
 # model = AutoModelForSequenceClassification.from_pretrained(model_name, trust_remote_code=True,attn_implementation="flash_attention_2", torch_dtype=torch.float16).to("cuda")
@@ -121,7 +121,7 @@ from sentence_transformers import CrossEncoder
 from transformers import LlamaTokenizer
 import torch
 
-model_name = "OpenBMB/MiniCPM-Reranker-Light"
+model_name = "openbmb/MiniCPM-Reranker-Light"
 model = CrossEncoder(model_name,max_length=1024,trust_remote_code=True, automodel_args={"torch_dtype": torch.float16})
 # You can also use the following code to use flash_attention_2
 #model = CrossEncoder(model_name,max_length=1024,trust_remote_code=True, automodel_args={"attn_implementation":"flash_attention_2","torch_dtype": torch.float16})
@@ -158,7 +158,7 @@ INSTRUCTION = "Query:"
 query = f"{INSTRUCTION} {query}"
 
 array = AsyncEngineArray.from_args(
-  [EngineArgs(model_name_or_path = "OpenBMB/MiniCPM-Reranker-Light", engine="torch", dtype="float16", bettertransformer=False, trust_remote_code=True, model_warmup=False)]
+  [EngineArgs(model_name_or_path = "openbmb/MiniCPM-Reranker-Light", engine="torch", dtype="float16", bettertransformer=False, trust_remote_code=True, model_warmup=False)]
 )
 
 async def rerank(engine: AsyncEmbeddingEngine): 
@@ -173,7 +173,7 @@ asyncio.run(rerank(array[0])) # [(RerankReturnType(relevance_score=0.017917344,
 
 ```python
 from FlagEmbedding import FlagReranker
-model_name = "OpenBMB/MiniCPM-Reranker-Light"
+model_name = "openbmb/MiniCPM-Reranker-Light"
 model = FlagReranker(model_name, use_fp16=True, query_instruction_for_rerank="Query: ", trust_remote_code=True)
 # You can hack the __init__() method of the FlagEmbedding BaseReranker class to use flash_attention_2 for faster inference
 #  self.model = AutoModelForSequenceClassification.from_pretrained(
@@ -231,10 +231,10 @@ We re-rank top-100 documents from `bge-m3` (Dense).
 
 ## 许可证 License
 
-- 本仓库中代码依照 [Apache-2.0 协议](https://github.com/OpenBMB/MiniCPM/blob/main/LICENSE)开源。
-- MiniCPM-Reranker-Light 模型权重的使用则需要遵循 [MiniCPM 模型协议](https://github.com/OpenBMB/MiniCPM/blob/main/MiniCPM%20Model%20License.md)。
+- 本仓库中代码依照 [Apache-2.0 协议](https://github.com/openbmb/MiniCPM/blob/main/LICENSE)开源。
+- MiniCPM-Reranker-Light 模型权重的使用则需要遵循 [MiniCPM 模型协议](https://github.com/openbmb/MiniCPM/blob/main/MiniCPM%20Model%20License.md)。
 - MiniCPM-Reranker-Light 模型权重对学术研究完全开放。如需将模型用于商业用途，请填写[此问卷](https://modelbest.feishu.cn/share/base/form/shrcnpV5ZT9EJ6xYjh3Kx0J6v8g)。
 
-* The code in this repo is released under the [Apache-2.0](https://github.com/OpenBMB/MiniCPM/blob/main/LICENSE) License. 
-* The usage of MiniCPM-Reranker-Light model weights must strictly follow [MiniCPM Model License.md](https://github.com/OpenBMB/MiniCPM/blob/main/MiniCPM%20Model%20License.md).
+* The code in this repo is released under the [Apache-2.0](https://github.com/openbmb/MiniCPM/blob/main/LICENSE) License. 
+* The usage of MiniCPM-Reranker-Light model weights must strictly follow [MiniCPM Model License.md](https://github.com/openbmb/MiniCPM/blob/main/MiniCPM%20Model%20License.md).
 * The models and weights of MiniCPM-Reranker-Light are completely free for academic research. After filling out a ["questionnaire"](https://modelbest.feishu.cn/share/base/form/shrcnpV5ZT9EJ6xYjh3Kx0J6v8g) for registration, MiniCPM-Reranker-Light weights are also available for free commercial use.