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  1. README.md +14 -13
README.md CHANGED
@@ -82,7 +82,7 @@ pipeline_tag: text-generation
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  - [Quick start](#quick-start)
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  - [Choose your path](#choose-your-parth)
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  - [pip](#pip)
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- - [llama.cpp](https://github.com/01-ai/Yi/blob/main/docs/yi_llama.cpp.md)
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  - [Web demo](#web-demo)
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  - [Fine tune](#fine-tune)
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  - [Quantization](#quantization)
@@ -265,12 +265,12 @@ sequence length and can be extended to 32K during inference time.
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  - [Quick start](#quick-start)
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  - [Choose your path](#choose-your-parth)
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  - [pip](#pip)
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- - [llama.cpp](https://github.com/01-ai/Yi/blob/main/docs/yi_llama.cpp.md)
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  - [Web demo](#web-demo)
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  - [Fine tune](#fine-tune)
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  - [Quantization](#quantization)
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- - [Deployment](https://github.com/01-ai/Yi/blob/main/docs/deployment.md)
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- - [Learning hub](https://github.com/01-ai/Yi/blob/main/docs/learning_hub.md)
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  ## Quick start
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@@ -280,7 +280,7 @@ Getting up and running with Yi models is simple with multiple choices available.
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  Select one of the following paths to begin your journey with Yi!
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- ![Quick start - Choose your path](https://github.com/01-ai/Yi/blob/main/assets/img/quick_start_path.png)
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  #### 🎯 Deploy Yi locally
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@@ -288,7 +288,7 @@ If you prefer to deploy Yi models locally,
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  - 🙋‍♀️ and you have **sufficient** resources (for example, NVIDIA A800 80GB), you can choose one of the following methods:
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  - [pip](#pip)
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- - [Docker](https://github.com/01-ai/Yi/blob/main/docs/README_legacy.md#11-docker)
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  - [conda-lock](https://github.com/01-ai/Yi/blob/main/docs/README_legacy.md#12-local-development-environment)
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  - 🙋‍♀️ and you have **limited** resources (for example, a MacBook Pro), you can use [llama.cpp](#quick-start---llamacpp)
@@ -427,7 +427,7 @@ Then you can see an output similar to the one below. 🥳
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  ### Quick start - Docker
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  <details>
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  <summary> Run Yi-34B-chat locally with Docker: a step-by-step guide. ⬇️</summary>
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- <br>This tutorial guides you through every step of running <strong>Yi-34B-Chat on an A800 GPU</strong> locally and then performing inference.
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  <h4>Step 0: Prerequisites</h4>
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  <p>Make sure you've installed <a href="https://docs.docker.com/engine/install/?open_in_browser=true">Docker</a> and <a href="https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/install-guide.html">nvidia-container-toolkit</a>.</p>
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@@ -536,9 +536,10 @@ Now you have successfully asked a question to the Yi model and got an answer!
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  ##### Method 2: Perform inference in web
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- 1. To initialize a lightweight and swift chatbot, navigate to the `llama.cpp` directory, and run the following command.
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  ```bash
 
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  ./server --ctx-size 2048 --host 0.0.0.0 --n-gpu-layers 64 --model /Users/yu/yi-chat-6B-GGUF/yi-chat-6b.Q2_K.gguf
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  ```
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@@ -576,12 +577,12 @@ Now you have successfully asked a question to the Yi model and got an answer!
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  2. To access the chatbot interface, open your web browser and enter `http://0.0.0.0:8080` into the address bar.
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- ![Yi model chatbot interface - llama.cpp](https://github.com/01-ai/Yi/blob/main/assets/img/yi_llama_cpp1.png)
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  3. Enter a question, such as "How do you feed your pet fox? Please answer this question in 6 simple steps" into the prompt window, and you will receive a corresponding answer.
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- ![Ask a question to Yi model - llama.cpp](https://github.com/01-ai/Yi/blob/main/assets/img/yi_llama_cpp2.png)
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  </ul>
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  </details>
@@ -602,7 +603,7 @@ python demo/web_demo.py -c <your-model-path>
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  You can access the web UI by entering the address provided in the console into your browser.
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- ![Quick start - web demo](https://github.com/01-ai/Yi/blob/main/assets/img/yi_34b_chat_web_demo.gif)
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  ### Finetuning
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@@ -1010,7 +1011,7 @@ If you're seeking to explore the diverse capabilities within Yi's thriving famil
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  Yi-34B-Chat model demonstrates exceptional performance, ranking first among all existing open-source models in the benchmarks including MMLU, CMMLU, BBH, GSM8k, and more.
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- ![Chat model performance](https://github.com/01-ai/Yi/blob/main/assets/img/benchmark_base.png)
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  <details>
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  <summary> Evaluation methods and challenges. ⬇️ </summary>
@@ -1027,7 +1028,7 @@ Yi-34B-Chat model demonstrates exceptional performance, ranking first among all
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  The Yi-34B and Yi-34B-200K models stand out as the top performers among open-source models, especially excelling in MMLU, CMML, common-sense reasoning, reading comprehension, and more.
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- ![Base model performance](https://github.com/01-ai/Yi/blob/main/assets/img/benchmark_base.png)
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  <details>
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  <summary> Evaluation methods. ⬇️</summary>
 
82
  - [Quick start](#quick-start)
83
  - [Choose your path](#choose-your-parth)
84
  - [pip](#pip)
85
+ - [llama.cpp](#quick-start---llamacpp)
86
  - [Web demo](#web-demo)
87
  - [Fine tune](#fine-tune)
88
  - [Quantization](#quantization)
 
265
  - [Quick start](#quick-start)
266
  - [Choose your path](#choose-your-parth)
267
  - [pip](#pip)
268
+ - [llama.cpp](#quick-start---llamacpp)
269
  - [Web demo](#web-demo)
270
  - [Fine tune](#fine-tune)
271
  - [Quantization](#quantization)
272
+ - [Deployment](#deployment)
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+ - [Learning hub](#learning-hub)
274
 
275
  ## Quick start
276
 
 
280
 
281
  Select one of the following paths to begin your journey with Yi!
282
 
283
+ ![Quick start - Choose your path](https://github.com/01-ai/Yi/blob/main/assets/img/quick_start_path.png?raw=true)
284
 
285
  #### 🎯 Deploy Yi locally
286
 
 
288
 
289
  - 🙋‍♀️ and you have **sufficient** resources (for example, NVIDIA A800 80GB), you can choose one of the following methods:
290
  - [pip](#pip)
291
+ - [Docker](#quick-start---docker)
292
  - [conda-lock](https://github.com/01-ai/Yi/blob/main/docs/README_legacy.md#12-local-development-environment)
293
 
294
  - 🙋‍♀️ and you have **limited** resources (for example, a MacBook Pro), you can use [llama.cpp](#quick-start---llamacpp)
 
427
  ### Quick start - Docker
428
  <details>
429
  <summary> Run Yi-34B-chat locally with Docker: a step-by-step guide. ⬇️</summary>
430
+ <br>This tutorial guides you through every step of running <strong>Yi-34B-Chat on an A800 GPU</strong> or <strong>4*4090</strong> locally and then performing inference.
431
  <h4>Step 0: Prerequisites</h4>
432
  <p>Make sure you've installed <a href="https://docs.docker.com/engine/install/?open_in_browser=true">Docker</a> and <a href="https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/install-guide.html">nvidia-container-toolkit</a>.</p>
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536
 
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  ##### Method 2: Perform inference in web
538
 
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+ 1. To initialize a lightweight and swift chatbot, run the following command.
540
 
541
  ```bash
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+ cd llama.cpp
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  ./server --ctx-size 2048 --host 0.0.0.0 --n-gpu-layers 64 --model /Users/yu/yi-chat-6B-GGUF/yi-chat-6b.Q2_K.gguf
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  ```
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577
 
578
  2. To access the chatbot interface, open your web browser and enter `http://0.0.0.0:8080` into the address bar.
579
 
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+ ![Yi model chatbot interface - llama.cpp](https://github.com/01-ai/Yi/blob/main/assets/img/yi_llama_cpp1.png?raw=true)
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582
 
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  3. Enter a question, such as "How do you feed your pet fox? Please answer this question in 6 simple steps" into the prompt window, and you will receive a corresponding answer.
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+ ![Ask a question to Yi model - llama.cpp](https://github.com/01-ai/Yi/blob/main/assets/img/yi_llama_cpp2.png?raw=true)
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  </ul>
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  </details>
 
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  You can access the web UI by entering the address provided in the console into your browser.
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+ ![Quick start - web demo](https://github.com/01-ai/Yi/blob/main/assets/img/yi_34b_chat_web_demo.gif?raw=true)
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608
  ### Finetuning
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1011
 
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  Yi-34B-Chat model demonstrates exceptional performance, ranking first among all existing open-source models in the benchmarks including MMLU, CMMLU, BBH, GSM8k, and more.
1013
 
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+ ![Chat model performance](https://github.com/01-ai/Yi/blob/main/assets/img/benchmark_chat.png?raw=true)
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1016
  <details>
1017
  <summary> Evaluation methods and challenges. ⬇️ </summary>
 
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  The Yi-34B and Yi-34B-200K models stand out as the top performers among open-source models, especially excelling in MMLU, CMML, common-sense reasoning, reading comprehension, and more.
1030
 
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+ ![Base model performance](https://github.com/01-ai/Yi/blob/main/assets/img/benchmark_base.png?raw=true)
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  <details>
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  <summary> Evaluation methods. ⬇️</summary>