Update README.md

README.md

@@ -1,6 +1,6 @@
 
 <p align="center" width="100%">
-<a href="" target="_blank"><img src="https://github.com/zjunlp/cama/assets/logo.jpg" alt="ZJU-CaMA" style="width: 30%; min-width: 30px; display: block; margin: auto;"></a>
+<a href="" target="_blank"><img src="https://github.com/zjunlp/CaMA/blob/main/assets/logo.jpg" alt="ZJU-CaMA" style="width: 30%; min-width: 30px; display: block; margin: auto;"></a>
 </p>
 
 > This is the result of the weight difference between `Llama 13B` and `CaMA-13B`. You can click [here](https://github.com/zjunlp/cama) to learn more.
@@ -193,7 +193,7 @@ Our pre-trained model has demonstrated certain abilities in instruction followin
 The effectiveness of information extraction is illustrated in the following figure. We tested different instructions for different tasks as well as the same instructions for the same task, and achieved good results for all of them.
 
 <p align="center" width="100%">
-<a href="" target="_blank"><img src="https://github.com/zjunlp/cama/assets/ie-case.jpg" alt="IE" style="width: 60%; min-width: 60px; display: block; margin: auto;"></a>
+<a href="" target="_blank"><img src="https://github.com/zjunlp/CaMA/blob/main/assets/ie-case.jpg" alt="IE" style="width: 60%; min-width: 60px; display: block; margin: auto;"></a>
 </p>
 
 
@@ -463,7 +463,7 @@ We offer two methods: the first one is **command-line interaction**, and the sec
    ```
    Here is a screenshot of the web-based interaction:
    <p align="center" width="100%">
-   <a href="" target="_blank"><img src="https://github.com/zjunlp/cama/assets/finetune_web.jpg" alt="finetune-web" style="width: 100%; min-width: 100px; display: block; margin: auto;"></a>
+   <a href="" target="_blank"><img src="https://github.com/zjunlp/CaMA/blob/main/assets/finetune_web.jpg" alt="finetune-web" style="width: 100%; min-width: 100px; display: block; margin: auto;"></a>
    </p>
 
 **3. Usage of Instruction tuning Model**
@@ -476,7 +476,7 @@ python examples/generate_lora_web.py --base_model ./CaMA --lora_weights ./LoRA
 
 Here is a screenshot of the web-based interaction:
 <p align="center" width="100%">
-<a href="" target="_blank"><img src="https://github.com/zjunlp/cama/assets/lora_web.png" alt="finetune-web" style="width: 100%; min-width: 100px; display: block; margin: auto;"></a>
+<a href="" target="_blank"><img src="https://github.com/zjunlp/CaMA/blob/main/assets/lora_web.png" alt="finetune-web" style="width: 100%; min-width: 100px; display: block; margin: auto;"></a>
 </p>
 
 The `instruction` is a required parameter, while `input` is an optional parameter. For general tasks (such as the examples provided in section `1.3`), you can directly enter the input in the `instruction` field. For information extraction tasks (as shown in the example in section `1.2`), please enter the instruction in the `instruction` field and the sentence to be extracted in the `input` field. We provide an information extraction prompt in section `2.5`. 
@@ -499,7 +499,7 @@ For information extraction tasks such as named entity recognition (NER), event e
 >
 >  (2) Instruction tuning stage using LoRA. This stage enables the model to understand human instructions and generate appropriate responses.
 
-![](https://github.com/zjunlp/cama/assets/main.jpg)
+![](https://github.com/zjunlp/CaMA/blob/main/assets/main.jpg)
 
 <h3 id="3-1">3.1 Dataset Construction (Pretraining)</h3>