Update README.md

README.md

@@ -67,8 +67,8 @@ All weights have been uploaded to HuggingFace🤗. It should be noted that all t
 <h3 id="1-1">1.1 Environment Configuration</h3>
 
 ```shell
-conda create -n zhixi python=3.9 -y
-conda activate zhixi
+conda create -n knowlm python=3.9 -y
+conda activate knowlm
 pip install torch==1.12.0+cu116 torchvision==0.13.0+cu116 torchaudio==0.12.0 --extra-index-url https://download.pytorch.org/whl/cu116
 pip install -r requirements.txt
 ```
@@ -76,9 +76,9 @@ pip install -r requirements.txt
 
 <h3 id="1-2">1.2 Pretraining model weight acquisition and restoration</h3>
 
-❗❗❗ Note that in terms of hardware, performing step `2.2`, which involves merging LLaMA-13B with ZhiXI-13B-Diff, requires approximately **100GB** of RAM, with no demand for VRAM (this is due to the memory overhead caused by our merging strategy. For your convenience, we have provided the fp16 weights at this link: https://huggingface.co/zjunlp/zhixi-13b-diff-fp16. **fp16 weights require less memory but may slightly impact performance**. We will improve our merging approach in future updates, and we are currently developing a 7B model as well, so stay tuned). For step `2.4`, which involves inference using `ZhiXi`, a minimum of **26GB** of VRAM is required.
+❗❗❗ Note that in terms of hardware, performing step `2.2`, which involves merging LLaMA-13B with KnowLM-13B-Diff, requires approximately **100GB** of RAM, with no demand for VRAM (this is due to the memory overhead caused by our merging strategy. For your convenience, we have provided the fp16 weights at this link: https://huggingface.co/zjunlp/zhixi-13b-diff-fp16. **fp16 weights require less memory but may slightly impact performance**. We will improve our merging approach in future updates, and we are currently developing a 7B model as well, so stay tuned). For step `2.4`, which involves inference using `ZhiXi`, a minimum of **26GB** of VRAM is required.
 
-**1. Download LLaMA 13B and ZhiXi-13B-Diff**
+**1. Download LLaMA 13B and KnowLM-13B-Diff**
 
 Please click [here](https://forms.gle/jk851eBVbX1m5TAv5) to apply for the official pre-training weights of LLaMA from `meta`. In this case, we are using the `13B` version of the model, so you only need to download the `13B` version. Once downloaded, the file directory will be as follows:
 
@@ -108,7 +108,7 @@ To convert the original LLaMA-13B model into the HuggingFace format, you can use
 python convert_llama_weights_to_hf.py --input_dir ./ --model_size 13B --output_dir ./converted
 ```
 
-**3. Restore ZhiXi 13B**
+**3. Restore KnowLM 13B**
 
 Use the script we provided, located at `./tools/weight_diff.py`, execute the following command, and you will get the complete `KnowLM` weight:
 
@@ -133,37 +133,7 @@ The final complete weights are saved in the `./lora` folder.
 
 <h3 id="1-4">1.4 Model Usage Guide</h3>
 
-**1. Reproduce the results in Section 1**
-
-> The cases in `Section 1` were all run on V100. If running on other devices, the results may vary. Please run multiple times or change the decoding parameters.
-
-1. If you want to reproduce the results in section `1.1`(**pretraining cases**), please run the following command (assuming that the complete pre-training weights of `ZhiXi` have been obtained according to the steps in section `2.2`, and the ZhiXi weight is saved in the `./zhixi` folder):
-
-   ```shell
-   python examples/generate_finetune.py --base_model ./knowlm
-   ```
-
-   The result in section `1.1` can be obtained.
-
-2. If you want to reproduce the results in section `1.2`(**information extraction cases**), please run the following command (assuming that the LoRA weights of `ZhiXi` have been obtained according to the steps in section `2.3`, and the LoRA weights is saved in the `./lora` folder):
-
-   ```shell
-   python examples/generate_lora.py --load_8bit --base_model ./knowlm --lora_weights ./lora --run_ie_cases
-   ```
-
-   The result in section `1.2` can be obtained.
-
-3. If you want to reproduce the results in section `1.3`(**general ablities cases**), please run the following command (assuming that the LoRA weights of `ZhiXi` have been obtained according to the steps in section `2.3`, and the LoRA weights is saved in the `./lora` folder):
-
-   ```shell
-   python examples/generate_lora.py --load_8bit --base_model ./knowlm --lora_weights ./lora --run_general_cases
-   ```
-
-   The result in section `1.3` can be obtained.
-
-
-
-**2. Usage of Pretraining Model**
+**1. Usage of Pretraining Model**
 
 We offer two methods: the first one is **command-line interaction**, and the second one is **web-based interaction**, which provides greater flexibility.
 
@@ -186,7 +156,7 @@ We offer two methods: the first one is **command-line interaction**, and the sec
    </p>
 
 
-**3. Usage of Instruction tuning Model**
+**2. Usage of Instruction tuning Model**
 
 Here, we provide a web-based interaction method. Use the following command to access the web:
 
@@ -209,7 +179,7 @@ If you want to perform batch testing, please modify the `examples/generate_lora.
 
 For information extraction tasks such as named entity recognition (NER), event extraction (EE), and relation extraction (RE), we provide some prompts for ease of use. You can refer to this [link](https://github.com/zjunlp/KnowLM/blob/main/examples/ie_prompt.py) for examples. Of course, you can also try using your own prompts.
 
-Here is a [case](https://github.com/zjunlp/DeepKE/blob/main/example/llm/InstructKGC/README.md) where ZhiXi-13B-LoRA is used to accomplish the instruction-based knowledge graph construction task in CCKS2023.
+Here is a [case](https://github.com/zjunlp/DeepKE/blob/main/example/llm/InstructKGC/README.md) where KnowLM-13B-LoRA is used to accomplish the instruction-based knowledge graph construction task in CCKS2023.
 
 
 <h2 id="2">2. Training Details</h2>