Update README.md

README.md

@@ -47,7 +47,7 @@ The dataset was extended from the original colpali train set with the gemini 1.5
 We train models  use low-rank adapters ([LoRA](https://arxiv.org/abs/2106.09685)) 
 with `alpha=64`  and `r=64` on the transformer layers from the language model, 
 as well as the final randomly initialized projection layer, and use a `paged_adamw_8bit` optimizer. 
-We train on an 8xH100 GPU setup with distriuted data parallelism (via accelerate), a learning rate of 2e-4 with linear decay with 1% warmup steps, batch size per device is 64, in `bfloat16` format
+We train on an 8xH100 GPU setup with distributed data parallelism (via accelerate), a learning rate of 2e-4 with linear decay with 1% warmup steps, batch size per device is 64, in `bfloat16` format
 
 ## Usage
 
@@ -65,11 +65,11 @@ from PIL import Image
 from colpali_engine.models import ColQwen2, ColQwen2Processor
 
 model = ColQwen2.from_pretrained(
-        "vidore/colqwen2-v1.0",
+        "tsystems/colqwen2-7b-v1.0",
         torch_dtype=torch.bfloat16,
         device_map="cuda:0",  # or "mps" if on Apple Silicon
     ).eval()
-processor = ColQwen2Processor.from_pretrained("vidore/colqwen2-v1.0")
+processor = ColQwen2Processor.from_pretrained("tsystems/colqwen2-7b-v1.0")
 
 # Your inputs
 images = [