Update README.md

LICENSE

@@ -0,0 +1,51 @@
+Qwen LICENSE AGREEMENT
+
+Qwen LICENSE AGREEMENT Release Date: September 19, 2024
+
+By clicking to agree or by using or distributing any portion or element of the Qwen Materials, you will be deemed to have recognized and accepted the content of this Agreement, which is effective immediately.
+
+1. Definitions
+    a. This Qwen LICENSE AGREEMENT (this "Agreement") shall mean the terms and conditions for use, reproduction, distribution and modification of the Materials as defined by this Agreement.
+    b. "We" (or "Us") shall mean Alibaba Cloud.
+    c. "You" (or "Your") shall mean a natural person or legal entity exercising the rights granted by this Agreement and/or using the Materials for any purpose and in any field of use.
+    d. "Third Parties" shall mean individuals or legal entities that are not under common control with us or you.
+    e. "Qwen" shall mean the large language models, and software and algorithms, consisting of trained model weights, parameters (including optimizer states), machine-learning model code, inference-enabling code, training-enabling code, fine-tuning enabling code and other elements of the foregoing distributed by us.
+    f. "Materials" shall mean, collectively, Alibaba Cloud's proprietary Qwen and Documentation (and any portion thereof) made available under this Agreement.
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+5. Rules of use
+    a. The Materials may be subject to export controls or restrictions in China, the United States or other countries or regions. You shall comply with applicable laws and regulations in your use of the Materials.
+    b. If you use the Materials or any outputs or results therefrom to create, train, fine-tune, or improve an AI model that is distributed or made available, you shall prominently display “Built with Qwen” or “Improved using Qwen” in the related product documentation.
+
+6. Intellectual Property
+    a. We retain ownership of all intellectual property rights in and to the Materials and derivatives made by or for us. Conditioned upon compliance with the terms and conditions of this Agreement, with respect to any derivative works and modifications of the Materials that are made by you, you are and will be the owner of such derivative works and modifications.
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README.md

@@ -4,10 +4,8 @@ license_name: qwen
 license_link: https://huggingface.co/Qwen/Qwen2.5-72B-Instruct/blob/main/LICENSE
 pipeline_tag: image-text-to-text
 library_name: transformers
-base_model:
-  - OpenGVLab/InternViT-6B-448px-V2_5
-  - Qwen/Qwen2.5-72B-Instruct
-base_model_relation: merge
+base_model: OpenGVLab/InternVL2_5-78B
+base_model_relation: quantized
 language:
   - multilingual
 tags:
@@ -83,7 +81,7 @@ The training pipeline for a single model in InternVL 2.5 is structured across th
 
 We introduce a progressive scaling strategy to align the vision encoder with LLMs efficiently. This approach trains with smaller LLMs first (e.g., 20B) to optimize foundational visual capabilities and cross-modal alignment before transferring the vision encoder to larger LLMs (e.g., 72B) without retraining. This reuse skips intermediate stages for larger models.
 
-![image/png](https://cdn-uploads.huggingface.co/production/uploads/64119264f0f81eb569e0d569/AVb_PSxhJq1z2eUFNYoqQ.png)
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/64006c09330a45b03605bba3/UoNUyS7ctN5pBxNv9KnzH.png)
 
 Compared to Qwen2-VL's 1.4 trillion tokens, InternVL2.5-78B uses only 120 billion tokens—less than one-tenth. This strategy minimizes redundancy, maximizes pre-trained component reuse, and enables efficient training for complex vision-language tasks.
 
@@ -166,7 +164,7 @@ As shown in the following figure, from InternVL 1.5 to 2.0 and then to 2.5, the
 
 ### Video Understanding
 
-![image/png](https://cdn-uploads.huggingface.co/production/uploads/64119264f0f81eb569e0d569/uD5aYt2wNYL94Xn8MOVih.png)
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/64006c09330a45b03605bba3/tcwH-i1qc8H16En-7AZ5M.png)
 
 ## Evaluation on Language Capability
 
@@ -178,10 +176,10 @@ Training InternVL 2.0 models led to a decline in pure language capabilities. Int
 
 ### LMDeploy
 
-LMDeploy is a toolkit for compressing, deploying, and serving LLM, developed by the MMRazor and MMDeploy teams.
+LMDeploy is a toolkit for compressing, deploying, and serving LLMs & VLMs.
 
 ```sh
-pip install lmdeploy>=0.5.3
+pip install lmdeploy>=0.6.4
 ```
 
 LMDeploy abstracts the complex inference process of multi-modal Vision-Language Models (VLM) into an easy-to-use pipeline, similar to the Large Language Model (LLM) inference pipeline.
@@ -205,8 +203,6 @@ If `ImportError` occurs while executing this case, please install the required d
 
 When dealing with multiple images, you can put them all in one list. Keep in mind that multiple images will lead to a higher number of input tokens, and as a result, the size of the context window typically needs to be increased.
 
-question = 'Describe this video in detail.'
-
 ```python
 from lmdeploy import pipeline, TurbomindEngineConfig
 from lmdeploy.vl import load_image
@@ -270,7 +266,7 @@ print(sess.response.text)
 LMDeploy's `api_server` enables models to be easily packed into services with a single command. The provided RESTful APIs are compatible with OpenAI's interfaces. Below are an example of service startup:
 
 ```shell
-lmdeploy serve api_server OpenGVLab/InternVL2_5-78B-AWQ --backend turbomind --server-port 23333 --tp 4
+lmdeploy serve api_server OpenGVLab/InternVL2_5-78B-AWQ --server-port 23333 --tp 4
 ```
 
 To use the OpenAI-style interface, you need to install OpenAI:

config.json

@@ -1,6 +1,5 @@
 {
   "_commit_hash": null,
-  "_name_or_path": "/mnt/bigdisk/InternVL2_5-78B/InternVL2_5-78B",
   "architectures": [
     "InternVLChatModel"
   ],
@@ -18,8 +17,8 @@
     "architectures": [
       "Qwen2ForCausalLM"
     ],
-    "attention_dropout": 0.0,
     "attn_implementation": "eager",
+    "attention_dropout": 0.0,
     "bad_words_ids": null,
     "begin_suppress_tokens": null,
     "bos_token_id": 151643,
@@ -110,91 +109,36 @@
   "select_layer": -1,
   "template": "internvl2_5",
   "torch_dtype": "float16",
-  "transformers_version": null,
   "use_backbone_lora": 0,
   "use_llm_lora": 0,
   "use_thumbnail": true,
   "vision_config": {
-    "_name_or_path": "",
-    "add_cross_attention": false,
     "architectures": [
       "InternVisionModel"
     ],
     "attention_dropout": 0.0,
-    "bad_words_ids": null,
-    "begin_suppress_tokens": null,
-    "bos_token_id": null,
-    "chunk_size_feed_forward": 0,
-    "cross_attention_hidden_size": null,
-    "decoder_start_token_id": null,
-    "diversity_penalty": 0.0,
-    "do_sample": false,
     "drop_path_rate": 0.0,
     "dropout": 0.0,
-    "early_stopping": false,
-    "encoder_no_repeat_ngram_size": 0,
-    "eos_token_id": null,
-    "exponential_decay_length_penalty": null,
-    "finetuning_task": null,
-    "forced_bos_token_id": null,
-    "forced_eos_token_id": null,
     "hidden_act": "gelu",
     "hidden_size": 3200,
-    "id2label": {
-      "0": "LABEL_0",
-      "1": "LABEL_1"
-    },
     "image_size": 448,
     "initializer_factor": 0.1,
     "initializer_range": 1e-10,
     "intermediate_size": 12800,
-    "is_decoder": false,
-    "is_encoder_decoder": false,
-    "label2id": {
-      "LABEL_0": 0,
-      "LABEL_1": 1
-    },
     "layer_norm_eps": 1e-06,
-    "length_penalty": 1.0,
-    "max_length": 20,
-    "min_length": 0,
     "model_type": "intern_vit_6b",
-    "no_repeat_ngram_size": 0,
     "norm_type": "rms_norm",
     "num_attention_heads": 25,
-    "num_beam_groups": 1,
-    "num_beams": 1,
     "num_channels": 3,
     "num_hidden_layers": 45,
-    "num_return_sequences": 1,
     "output_attentions": false,
     "output_hidden_states": false,
-    "output_scores": false,
-    "pad_token_id": null,
     "patch_size": 14,
-    "prefix": null,
-    "problem_type": null,
-    "pruned_heads": {},
     "qk_normalization": true,
     "qkv_bias": false,
-    "remove_invalid_values": false,
-    "repetition_penalty": 1.0,
     "return_dict": true,
-    "return_dict_in_generate": false,
-    "sep_token_id": null,
-    "suppress_tokens": null,
-    "task_specific_params": null,
-    "temperature": 1.0,
-    "tf_legacy_loss": false,
-    "tie_encoder_decoder": false,
-    "tie_word_embeddings": true,
-    "tokenizer_class": null,
-    "top_k": 50,
-    "top_p": 1.0,
     "torch_dtype": "bfloat16",
-    "torchscript": false,
     "transformers_version": "4.46.0.dev0",
-    "typical_p": 1.0,
     "use_bfloat16": true,
     "use_flash_attn": true
   }

configuration_internvl_chat.py

@@ -38,11 +38,11 @@ class InternVLChatConfig(PretrainedConfig):
         super().__init__(**kwargs)
 
         if vision_config is None:
-            vision_config = {}
+            vision_config = {'architectures': ['InternVisionModel']}
             logger.info('vision_config is None. Initializing the InternVisionConfig with default values.')
 
         if llm_config is None:
-            llm_config = {}
+            llm_config = {'architectures': ['Qwen2ForCausalLM']}
             logger.info('llm_config is None. Initializing the LlamaConfig config with default values (`LlamaConfig`).')
 
         self.vision_config = InternVisionConfig(**vision_config)

modeling_internvl_chat.py

@@ -111,7 +111,7 @@ class InternVLChatModel(PreTrainedModel):
         B, N, C = input_embeds.shape
         input_embeds = input_embeds.reshape(B * N, C)
 
-        if torch.distributed.get_rank() == 0:
+        if torch.distributed.is_initialized() and torch.distributed.get_rank() == 0:
             print(f'dynamic ViT batch size: {vit_batch_size}, images per sample: {vit_batch_size / B}, dynamic token length: {N}')
 
         input_ids = input_ids.reshape(B * N)