Update README.md

README.md

@@ -15,27 +15,26 @@ pip3 install fschat
 
 <a href="https://chat.lmsys.org"><img src="assets/screenshot_cli.png" width="70%"></a>
 
+When use huggingface, the </path/to/vicuna/weights> is "jinxuewen/vicuna-13b"
+
 #### Single GPU
 The command below requires around 28GB of GPU memory for Vicuna-13B and 14GB of GPU memory for Vicuna-7B.
 See the "No Enough Memory" section below if you do not have enough memory.
 ```
 python3 -m fastchat.serve.cli --model-path /path/to/vicuna/weights
 ```
-When use huggingface, the `/path/to/vicuna/weights` is `jinxuewen/vicuna-13b`
 
 #### Multiple GPUs
 You can use model parallelism to aggregate GPU memory from multiple GPUs on the same machine.
 ```
 python3 -m fastchat.serve.cli --model-path /path/to/vicuna/weights --num-gpus 2
 ```
-When use huggingface, the `/path/to/vicuna/weights` is `jinxuewen/vicuna-13b`
 
 #### CPU Only
 This runs on the CPU only and does not require GPU. It requires around 60GB of CPU memory for Vicuna-13B and around 30GB of CPU memory for Vicuna-7B.
 ```
 python3 -m fastchat.serve.cli --model-path /path/to/vicuna/weights --device cpu
 ```
-When use huggingface, the `/path/to/vicuna/weights` is `jinxuewen/vicuna-13b`
 
 #### Metal Backend (Mac Computers with Apple Silicon or AMD GPUs)
 Use `--device mps` to enable GPU acceleration on Mac computers (requires torch >= 2.0).
@@ -43,11 +42,9 @@ Use `--load-8bit` to turn on 8-bit compression.
 ```
 python3 -m fastchat.serve.cli --model-path /path/to/vicuna/weights --device mps --load-8bit
 ```
-When use huggingface, the `/path/to/vicuna/weights` is `jinxuewen/vicuna-13b`
 
 Vicuna-7B can run on a 32GB M1 Macbook with 1 - 2 words / second.
 
-
 #### No Enough Memory or Other Platforms
 If you do not have enough memory, you can enable 8-bit compression by adding `--load-8bit` to commands above.
 This can reduce memory usage by around half with slightly degraded model quality.
@@ -57,8 +54,6 @@ Vicuna-13B with 8-bit compression can run on a single NVIDIA 3090/4080/V100(16GB
 ```
 python3 -m fastchat.serve.cli --model-path /path/to/vicuna/weights --load-8bit
 ```
-When use huggingface, the `/path/to/vicuna/weights` is `jinxuewen/vicuna-13b`
-
 Besides, we are actively exploring more methods to make the model easier to run on more platforms.
 Contributions and pull requests are welcome.
 
@@ -72,32 +67,26 @@ To serve using the web UI, you need three main components: web servers that inte
 ```bash
 python3 -m fastchat.serve.controller
 ```
-
 This controller manages the distributed workers.
 
 #### Launch the model worker
 ```bash
 python3 -m fastchat.serve.model_worker --model-path /path/to/vicuna/weights
 ```
-When use huggingface, the `/path/to/vicuna/weights` is `jinxuewen/vicuna-13b`
-
 Wait until the process finishes loading the model and you see "Uvicorn running on ...". You can launch multiple model workers to serve multiple models concurrently. The model worker will connect to the controller automatically.
 
 To ensure that your model worker is connected to your controller properly, send a test message using the following command:
 ```bash
 python3 -m fastchat.serve.test_message --model-name vicuna-13b
 ```
-
 #### Launch the Gradio web server
 ```bash
 python3 -m fastchat.serve.gradio_web_server
 ```
-
 This is the user interface that users will interact with.
 
 By following these steps, you will be able to serve your models using the web UI. You can open your browser and chat with a model now.
 
-
 ## API
 
 ### Huggingface Generation APIs