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README.md
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@@ -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](
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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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- [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](
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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](
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- [Learning hub](
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## Quick start
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Select one of the following paths to begin your journey with Yi!
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
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#### 🎯 Deploy Yi 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](
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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)
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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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##### Method 2: Perform inference in web
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1. To initialize a lightweight and swift chatbot,
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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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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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
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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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
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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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
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### Finetuning
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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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
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<details>
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<summary> Evaluation methods. ⬇️</summary>
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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](#quick-start---llamacpp)
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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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- [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](#quick-start---llamacpp)
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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](#deployment)
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+
- [Learning hub](#learning-hub)
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## Quick start
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|
|
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Select one of the following paths to begin your journey with Yi!
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|
283 |
+

|
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|
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#### 🎯 Deploy Yi locally
|
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|
|
|
288 |
|
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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](#quick-start---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)
|
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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> or <strong>4*4090</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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##### Method 2: Perform inference in web
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1. To initialize a lightweight and swift chatbot, run the following command.
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```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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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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+

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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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+

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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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+

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### Finetuning
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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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+

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<details>
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<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.
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
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<details>
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<summary> Evaluation methods. ⬇️</summary>
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