Integrating the Yi series models (#3958)

* Add files via upload

* Update and rename qwen2-7b.yaml to yi15-6b.yaml

* Add files via upload

* Update yi15-9b.yaml

* Update yi15-34b.yaml

* Update yi15-6b.yaml

* Add files via upload

* Update yicoder-1_5b.yaml

* Update yicoder-9b.yaml

* Add files via upload

* Update yi15-34b.yaml

* Update yi15-6b.yaml

* Update yi15-9b.yaml

* Update yicoder-1_5b.yaml

* Update yicoder-9b.yaml

llm/yi/README.md

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+# Serving Yi on Your Own Kubernetes or Cloud
+
+🤖 The Yi series models are the next generation of open-source large language models trained from scratch by [01.AI](https://www.lingyiwanwu.com/en).
+
+**Update (Sep 19, 2024) -** SkyPilot now supports the [**Yi**](https://01-ai.github.io/) model(Yi-Coder Yi-1.5)! 
+
+<p align="center">
+    <img src="https://raw.githubusercontent.com/01-ai/Yi/main/assets/img/coder/bench1.webp" alt="yi" width="600"/>
+</p>
+
+## Why use SkyPilot to deploy over commercial hosted solutions?
+
+* Get the best GPU availability by utilizing multiple resources pools across Kubernetes clusters and multiple regions/clouds.
+* Pay absolute minimum — SkyPilot picks the cheapest resources across Kubernetes clusters and regions/clouds. No managed solution markups.
+* Scale up to multiple replicas across different locations and accelerators, all served with a single endpoint 
+* Everything stays in your Kubernetes or cloud account (your VMs & buckets)
+* Completely private - no one else sees your chat history
+
+
+## Running Yi model with SkyPilot
+
+After [installing SkyPilot](https://skypilot.readthedocs.io/en/latest/getting-started/installation.html), run your own Yi model on vLLM with SkyPilot in 1-click:
+
+1. Start serving Yi-1.5 34B on a single instance with any available GPU in the list specified in [yi15-34b.yaml](https://github.com/skypilot-org/skypilot/blob/master/llm/yi/yi15-34b.yaml) with a vLLM powered OpenAI-compatible endpoint (You can also switch to [yicoder-9b.yaml](https://github.com/skypilot-org/skypilot/blob/master/llm/yi/yicoder-9b.yaml) or [other model](https://github.com/skypilot-org/skypilot/tree/master/llm/yi) for a smaller model):
+
+```console
+sky launch -c yi yi15-34b.yaml
+```
+2. Send a request to the endpoint for completion:
+```bash
+ENDPOINT=$(sky status --endpoint 8000 yi)
+
+curl http://$ENDPOINT/v1/completions \
+    -H "Content-Type: application/json" \
+    -d '{
+      "model": "01-ai/Yi-1.5-34B-Chat",
+      "prompt": "Who are you?",
+      "max_tokens": 512
+  }' | jq -r '.choices[0].text'
+```
+
+3. Send a request for chat completion:
+```bash
+curl http://$ENDPOINT/v1/chat/completions \
+    -H "Content-Type: application/json" \
+    -d '{
+      "model": "01-ai/Yi-1.5-34B-Chat",
+      "messages": [
+        {
+          "role": "system",
+          "content": "You are a helpful assistant."
+        },
+        {
+          "role": "user",
+          "content": "Who are you?"
+        }
+      ],
+      "max_tokens": 512
+  }' | jq -r '.choices[0].message.content'
+```

llm/yi/yi15-34b.yaml

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+envs:
+  MODEL_NAME: 01-ai/Yi-1.5-34B-Chat
+
+resources:
+  accelerators: {A100:4, A100:8, A100-80GB:2, A100-80GB:4, A100-80GB:8}
+  disk_size: 1024
+  disk_tier: best
+  memory: 32+
+  ports: 8000
+
+setup: |
+  pip install vllm==0.6.1.post2
+  pip install vllm-flash-attn
+
+run: |
+  export PATH=$PATH:/sbin
+  vllm serve $MODEL_NAME \
+    --host 0.0.0.0 \
+    --tensor-parallel-size $SKYPILOT_NUM_GPUS_PER_NODE \
+    --max-model-len 1024 | tee ~/openai_api_server.log

llm/yi/yi15-6b.yaml

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+envs:
+  MODEL_NAME: 01-ai/Yi-1.5-6B-Chat
+
+resources:
+  accelerators: {L4, A10g, A10, L40, A40, A100, A100-80GB}
+  disk_tier: best
+  ports: 8000
+
+setup: |
+  pip install vllm==0.6.1.post2
+  pip install vllm-flash-attn
+
+run: |
+  export PATH=$PATH:/sbin
+  vllm serve $MODEL_NAME \
+    --host 0.0.0.0 \
+    --tensor-parallel-size $SKYPILOT_NUM_GPUS_PER_NODE \
+    --max-model-len 1024 | tee ~/openai_api_server.log

llm/yi/yi15-9b.yaml

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+envs:
+  MODEL_NAME: 01-ai/Yi-1.5-9B-Chat
+
+resources:
+  accelerators: {L4:8, A10g:8, A10:8, A100:4, A100:8, A100-80GB:2, A100-80GB:4, A100-80GB:8}
+  disk_tier: best
+  ports: 8000
+
+setup: |
+  pip install vllm==0.6.1.post2
+  pip install vllm-flash-attn
+
+run: |
+  export PATH=$PATH:/sbin
+  vllm serve $MODEL_NAME \
+    --host 0.0.0.0 \
+    --tensor-parallel-size $SKYPILOT_NUM_GPUS_PER_NODE \
+    --max-model-len 1024 | tee ~/openai_api_server.log

llm/yi/yicoder-1_5b.yaml

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+envs:
+  MODEL_NAME: 01-ai/Yi-Coder-1.5B-Chat
+
+resources:
+  accelerators: {L4, A10g, A10, L40, A40, A100, A100-80GB}
+  disk_tier: best
+  ports: 8000
+
+setup: |
+  pip install vllm==0.6.1.post2
+  pip install vllm-flash-attn
+
+run: |
+  export PATH=$PATH:/sbin
+  vllm serve $MODEL_NAME \
+    --host 0.0.0.0 \
+    --tensor-parallel-size $SKYPILOT_NUM_GPUS_PER_NODE \
+    --max-model-len 1024 | tee ~/openai_api_server.log

llm/yi/yicoder-9b.yaml

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+envs:
+  MODEL_NAME: 01-ai/Yi-Coder-9B-Chat
+
+resources:
+  accelerators: {L4:8, A10g:8, A10:8, A100:4, A100:8, A100-80GB:2, A100-80GB:4, A100-80GB:8}
+  disk_tier: best
+  ports: 8000
+
+setup: |
+  pip install vllm==0.6.1.post2
+  pip install vllm-flash-attn
+
+run: |
+  export PATH=$PATH:/sbin
+  vllm serve $MODEL_NAME \
+    --host 0.0.0.0 \
+    --tensor-parallel-size $SKYPILOT_NUM_GPUS_PER_NODE \
+    --max-model-len 1024 | tee ~/openai_api_server.log