Update README.md

README.md

@@ -1,12 +1,16 @@
 ---
 library_name: transformers
-tags: []
+datasets:
+- Joctor/cn_bokete_oogiri_caption
+base_model:
+- Qwen/Qwen2-VL-7B-Instruct
+pipeline_tag: image-to-text
 ---
 
 # Model Card for Model ID
 
 <!-- Provide a quick summary of what the model is/does. -->
-
+AI大喜利
 
 
 ## Model Details
@@ -71,7 +75,55 @@ Users (both direct and downstream) should be made aware of the risks, biases and
 
 Use the code below to get started with the model.
 
-[More Information Needed]
+from transformers import Qwen2VLForConditionalGeneration, AutoTokenizer, AutoProcessor
+from qwen_vl_utils import process_vision_info
+
+model_id = "Joctor/qwen2-vl-7b-instruct-ogiri"
+
+# default: Load the model on the available device(s)
+model = Qwen2VLForConditionalGeneration.from_pretrained(
+    model_id, torch_dtype="auto", device_map="auto"
+)
+
+# default processer
+processor = AutoProcessor.from_pretrained(model_id)
+
+messages = [
+    {
+        "role": "user",
+        "content": [
+            {
+                "type": "image",
+                "image": "path/to/image",
+            },
+            {"type": "text", "text": "根据图片给出有趣巧妙的回答"},
+        ],
+    }
+]
+
+# 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)
 
 ## Training Details
 
@@ -79,12 +131,18 @@ Use the code below to get started with the model.
 
 <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
 
-[More Information Needed]
+https://huggingface.co/datasets/Joctor/cn_bokete_oogiri_caption
 
 ### Training Procedure
 
 <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
 
+基础模型：qwen2vl
+微调方式：数据量充足，采用SFT微调
+微调参数：max_length=1024(短就是好!), num_train_epochs=1, per_device_train_batch_size=1, gradient_accumulation_steps=1
+训练设备：10 * 4090D
+训练时长：22小时
+
 #### Preprocessing [optional]
 
 [More Information Needed]
@@ -126,7 +184,7 @@ Use the code below to get started with the model.
 
 ### Results
 
-[More Information Needed]
+https://www.gcores.com/articles/188405
 
 #### Summary