Create merge_qwen.py

merge_qwen.py

@@ -0,0 +1,93 @@
+from transformers import Qwen2Model, Qwen2ForCausalLM, Qwen2_5_VLPreTrainedModel, Qwen2_5_VLForConditionalGeneration, AutoProcessor, AutoTokenizer, AddedToken
+import torch
+from qwen_vl_utils import process_vision_info
+
+
+qwen25_model = Qwen2_5_VLForConditionalGeneration.from_pretrained("Qwen/Qwen2.5-VL-7B-Instruct", device_map="auto", torch_dtype=torch.bfloat16)
+llm_device = qwen25_model.model.device
+deepseek_model = Qwen2ForCausalLM.from_pretrained("deepseek-ai/DeepSeek-R1-Distill-Qwen-7B").to(torch.bfloat16).to(llm_device)
+qwen25_model.model.load_state_dict(deepseek_model.model.state_dict()) 
+qwen25_model.lm_head.load_state_dict(deepseek_model.lm_head.state_dict())
+
+
+qwen25_model = qwen25_model.to(torch.bfloat16)
+min_pixels = 256*28*28
+max_pixels = 1280*28*28
+processor = AutoProcessor.from_pretrained("Qwen/Qwen2.5-VL-7B-Instruct", min_pixels=min_pixels, max_pixels=max_pixels, use_fast=False)
+ID_TO_NEW_TOKEN = {
+    151643: "<｜end▁of▁sentence｜>",
+    151644: "<｜User｜>",
+    151645: "<｜Assistant｜>",
+    151646: "<｜begin▁of▁sentence｜>",
+    151648: "<think>",
+    151649: "</think>",
+}
+
+# The reverse mapping: new text -> old ID
+NEW_TOKEN_TO_ID = {v: k for k, v in ID_TO_NEW_TOKEN.items()}
+
+for old_id, text in ID_TO_NEW_TOKEN.items():
+    # Create an AddedToken that won't get split
+    # 'special=True' ensures it is recognized as one piece
+    # 'normalized=False' means "do not lowercase or strip it"
+    # so it is preserved exactly.
+    tok = AddedToken(
+        text,
+        special=True,
+        normalized=False,
+        lstrip=False,
+        rstrip=False,
+        single_word=False
+    )
+    # Register in the slow tokenizer's internal data structures:
+    #   _added_tokens_decoder: maps ID -> AddedToken object
+    #   _added_tokens_encoder: maps text -> ID
+    # Then update the trie so that it can match them in raw text.
+    processor.tokenizer._added_tokens_decoder[old_id] = tok
+    processor.tokenizer._added_tokens_encoder[text] = old_id
+
+processor.tokenizer._update_trie()
+
+
+print("Model loaded and move to GPU")
+repo_name = "ahmedheakl/vlm-r1-base2"
+qwen25_model.push_to_hub(repo_name)
+processor.push_to_hub(repo_name)
+
+
+
+# messages = [
+#     {
+#         "role": "user",
+#         "content": [
+#             # {
+#             #     "type": "image",
+#             #     "image": "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-VL/assets/demo.jpeg",
+#             # },
+#             {"type": "text", "text": "What is the integration of cos^2(x)"},
+#         ],
+#     }
+# ]
+
+# text = processor.apply_chat_template(
+#     messages, tokenize=False, add_generation_prompt=True
+# )
+# image_inputs, video_inputs = process_vision_info(messages)
+# inputs = processor(
+#     text=[text],
+#     images=image_inputs,
+#     videos=video_inputs,
+#     padding=True,
+#     return_tensors="pt",
+# )
+# inputs = inputs.to("cuda")
+
+# # Inference: Generation of the output
+# generated_ids = qwen25_model.generate(**inputs, max_new_tokens=1000)
+# generated_ids_trimmed = [
+#     out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
+# ]
+# output_text = processor.batch_decode(
+#     generated_ids_trimmed, skip_special_tokens=False, clean_up_tokenization_spaces=False
+# )
+# print(output_text[0])