初始化项目，由ModelHub XC社区提供模型

Model: swift/Simple-VL-8B Source: Original Platform
2026-10-12 04:50:18 +08:00
commit acae18f7d9
22 changed files with 925213 additions and 0 deletions
--- a/README.md
+++ b/README.md
@@ -0,0 +1,102 @@
+---
+frameworks:
+- Pytorch
+license: Apache License 2.0
+tasks:
+- image-text-to-text
+
+model-type:
+#如 gpt、phi、llama、chatglm、baichuan 等
+- qwen
+
+domain:
+#如 nlp、cv、audio、multi-modal
+- multi-modal
+
+language:
+#语言代码列表 https://help.aliyun.com/document_detail/215387.html?spm=a2c4g.11186623.0.0.9f8d7467kni6Aa
+- en
+
+base_model:
+  - Qwen/Qwen3-8B
+  - Qwen/Qwen2.5-VL-7B-Instruct
+#metrics:
+##如 CIDEr、Blue、ROUGE 等
+#- CIDEr
+
+#tags:
+##各种自定义，包括 pretrained、fine-tuned、instruction-tuned、RL-tuned 等训练方法和其他
+#- pretrained
+
+#tools:
+##如 vllm、fastchat、llamacpp、AdaSeq 等
+#- vllm
+---
+
+Simple-VL-8B is a vision-language (VL) model trained by integrating the language modeling capabilities of Qwen3-8B with the visual understanding architecture of Qwen2.5-VL-7B-Instruct . 
+
+The model is trained under [ms-swift](https://github.com/modelscope/ms-swift/tree/main) framework, the SOP process document can be found [here](https://swift.readthedocs.io/en/latest/BestPractices/Rapidly-Training-VL-model.html)
+
+Base Models :
+- [Qwen2.5-VL-7B-Instruct](https://www.modelscope.cn/models/Qwen/Qwen2.5-VL-7B-Instruct)
+- [Qwen3-8B](https://www.modelscope.cn/models/Qwen/Qwen3-8B)
+
+The Simple-VL-8B model was created through a two-stage fine-tuning process:
+
+1. Architecture Modification : The original Qwen2.5-VL-7B-Instruct model's LLM component was replaced with weights from Qwen3-8B. Several key parameters in the configuration were updated to match Qwen3-8B's structure.
+2. Two-Stage Training :
+    1. Stage 1 : Only the vision-to-language aligner (merger layer) was trained while keeping the ViT and LLM components frozen.
+    2. Stage 2 : All components were unfrozen and jointly fine-tuned to enhance overall performance.
+
+Here we show a code snippet to show you how to use the chat model
+
+```python
+from modelscope import Qwen2_5_VLForConditionalGeneration, AutoTokenizer, AutoProcessor
+from qwen_vl_utils import process_vision_info
+
+# default: Load the model on the available device(s)
+model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
+    "swift/Simple-VL-8B", torch_dtype="auto", device_map="auto"
+)
+
+# default processer
+processor = AutoProcessor.from_pretrained("swift/Simple-VL-8B")
+
+messages = [
+    {
+        "role": "user",
+        "content": [
+            {
+                "type": "image",
+                "image": "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-VL/assets/demo.jpeg",
+            },
+            {"type": "text", "text": "Describe this image."},
+        ],
+    }
+]
+
+# Preparation for inference
+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 = model.generate(**inputs, max_new_tokens=128)
+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=True, clean_up_tokenization_spaces=False
+)
+print(output_text)
+
+```