Push model using huggingface_hub.

config.json

@@ -35,7 +35,7 @@
   "remove_template": false,
   "token_pooling": true,
   "torch_dtype": "float32",
-  "transformers_version": "4.42.3",
+  "transformers_version": "4.37.2",
   "use_grad_checkpoint": true,
   "use_grad_checkpoint_llm": true,
   "vit_model": "eva_clip_g",

generation_config.json

@@ -1,4 +1,4 @@
 {
   "_from_model_config": true,
-  "transformers_version": "4.42.3"
+  "transformers_version": "4.37.2"
 }

mini_gpt4_llama_v2.py

@@ -644,6 +644,7 @@ class MiniGPT4_Video(Blip2Base, PreTrainedModel):
                 repetition_penalty=repetition_penalty,
                 # stopping_criteria=stopping_criteria,
                 # use_fastv=False,
+                use_cache=True,
             )
 
         answers = []
@@ -845,11 +846,11 @@ class MiniGPT4_Video(Blip2Base, PreTrainedModel):
             msg = model.load_state_dict(ckpt['model'], strict=False)  
         # push the model to the hub with its metadata and config file
         model.to('cuda')
-        model.push_to_hub("Vision-CAIR/MiniGPT4-video-mistral-hf")
+        # model.push_to_hub("Vision-CAIR/MiniGPT4-video-mistral-hf")
         video_config = minigpt4_video_config(cfg)
         # video_config.save_pretrained("minigpt4_video_config")
         # print("Save Minigpt-4-LLM Config: minigpt4_video_config")
-        video_config.push_to_hub("Vision-CAIR/MiniGPT4-video-mistral-hf")
+        # video_config.push_to_hub("Vision-CAIR/MiniGPT4-video-mistral-hf")
         return model
 
 

model-00001-of-00003.safetensors

@@ -1,3 +1,3 @@
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-oid sha256:9f3ca9f784f5486e3935dc30b7b6e6cf8bb4635f71098cc8b30b6a48cf914cc7
+oid sha256:23a0ae99f0ba8f23e67afc7c82027eb2ae2df03505b3b7f4cf2ca3af78cdac47
 size 4981849121

model-00002-of-00003.safetensors

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:46225a736e4a1e79a61f7f47fd1b3180e1ae0f7b6118abfc6907961f9b8ebda3
+oid sha256:2d877cf7c7a09380e9a42a11aa951dc1fcc83c48c03f37d14d7b7b8b3f3ae224
 size 4610750095