srimanth-d
/

GOT_CPU

Image-Text-to-Text

feature-extraction

vision-language

Model card Files Files and versions

srimanth-d commited on Dec 12, 2024

Commit

5b65c54

·

verified ·

1 Parent(s): 646d03f

Update modeling_got.py

Files changed (1) hide show

modeling_got.py +4 -4

modeling_got.py CHANGED Viewed

@@ -583,7 +583,7 @@ class GOTQwenForCausalLM(Qwen2ForCausalLM):
             elif device == "mps" or device == "cpu":
                 output_ids = self.generate(
                         input_ids,
-                        images=[image_tensor_1.unsqueeze(0).half().to(device)],
                         do_sample=False,
                         num_beams = 1,
                         no_repeat_ngram_size = 20,
@@ -609,7 +609,7 @@ class GOTQwenForCausalLM(Qwen2ForCausalLM):
             elif device == "mps" or device == "cpu":
                 output_ids = self.generate(
                         input_ids,
-                        images=[image_tensor_1.unsqueeze(0).half().to(device)],
                         do_sample=False,
                         num_beams = 1,
                         no_repeat_ngram_size = 20,
@@ -865,7 +865,7 @@ class GOTQwenForCausalLM(Qwen2ForCausalLM):
             elif device == "mps" or device == "cpu":
                     output_ids = self.generate(
                         input_ids,
-                        images=[image_list.half().to(device)],
                         do_sample=False,
                         num_beams = 1,
                         # no_repeat_ngram_size = 20,
@@ -891,7 +891,7 @@ class GOTQwenForCausalLM(Qwen2ForCausalLM):
             elif device == "mps" or device == "cpu":
                 output_ids = self.generate(
                         input_ids,
-                        images=[image_list.half().to(device)],
                         do_sample=False,
                         num_beams = 1,
                         # no_repeat_ngram_size = 20,

             elif device == "mps" or device == "cpu":
                 output_ids = self.generate(
                         input_ids,
+                        images=[image_tensor_1.unsqueeze(0).to(device)],
                         do_sample=False,
                         num_beams = 1,
                         no_repeat_ngram_size = 20,
             elif device == "mps" or device == "cpu":
                 output_ids = self.generate(
                         input_ids,
+                        images=[image_tensor_1.unsqueeze(0).to(device)],
                         do_sample=False,
                         num_beams = 1,
                         no_repeat_ngram_size = 20,
             elif device == "mps" or device == "cpu":
                     output_ids = self.generate(
                         input_ids,
+                        images=[image_list.to(device)],
                         do_sample=False,
                         num_beams = 1,
                         # no_repeat_ngram_size = 20,
             elif device == "mps" or device == "cpu":
                 output_ids = self.generate(
                         input_ids,
+                        images=[image_list.to(device)],
                         do_sample=False,
                         num_beams = 1,
                         # no_repeat_ngram_size = 20,