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Deploy the Llama3 model for text_generation task with Hugging Face LLM Serving Runtime

In this example, We demonstrate how to deploy Llama3 model for text generation task from Hugging Face by deploying the InferenceService with Hugging Face Serving runtime.

Serve the Hugging Face LLM model using vLLM backend

KServe Hugging Face runtime by default uses vLLM to serve the LLM models for faster time-to-first-token(TTFT) and higher token generation throughput than the Hugging Face API. vLLM is implemented with common inference optimization techniques, such as paged attention, continuous batching and an optimized CUDA kernel. If the model is not supported by vLLM, KServe falls back to HuggingFace backend as a failsafe.

Note

The Llama3 model requires huggingface hub token to download the model. You can set the token using HF_TOKEN environment variable.

Create a secret with the Hugging Face token.

apiVersion: v1
kind: Secret
metadata:
    name: hf-secret
type: Opaque    
stringData:
    HF_TOKEN: <token>

Then create the inference service.

kubectl apply -f - <<EOF
apiVersion: serving.kserve.io/v1beta1
kind: InferenceService
metadata:
  name: huggingface-llama3
spec:
  predictor:
    model:
      modelFormat:
        name: huggingface
      args:
        - --model_name=llama3
        - --model_id=meta-llama/meta-llama-3-8b-instruct
      env:
        - name: HF_TOKEN
          valueFrom:
            secretKeyRef:
              name: hf-secret
              key: HF_TOKEN
              optional: false
      resources:
        limits:
          cpu: "6"
          memory: 24Gi
          nvidia.com/gpu: "1"
        requests:
          cpu: "6"
          memory: 24Gi
          nvidia.com/gpu: "1"
EOF

Check InferenceService status.

kubectl get inferenceservices huggingface-llama3

Expected Output

NAME                 URL                                                   READY   PREV   LATEST   PREVROLLEDOUTREVISION   LATESTREADYREVISION                          AGE
huggingface-llama3   http://huggingface-llama3.default.example.com         True           100                              huggingface-llama3-predictor-default-47q2g   7d23h

Perform Model Inference

The first step is to determine the ingress IP and ports and set INGRESS_HOST and INGRESS_PORT.

MODEL_NAME=llama3
SERVICE_HOSTNAME=$(kubectl get inferenceservice huggingface-llama3 -o jsonpath='{.status.url}' | cut -d "/" -f 3)

KServe Hugging Face vLLM runtime supports the OpenAI /v1/completions and /v1/chat/completions endpoints for inference

Sample OpenAI Completions request:

curl -v http://${INGRESS_HOST}:${INGRESS_PORT}/openai/v1/completions \
-H "content-type: application/json" -H "Host: ${SERVICE_HOSTNAME}" \
-d '{"model": "llama3", "prompt": "Write a poem about colors", "stream":false, "max_tokens": 30}'

Expected Output

{
  "id": "cmpl-625a9240f25e463487a9b6c53cbed080",
  "choices": [
    {
      "finish_reason": "length",
      "index": 0,
      "logprobs": null,
      "text": " and how they make you feel\nColors, oh colors, so vibrant and bright\nA world of emotions, a kaleidoscope in sight\nRed"
    }
  ],
  "created": 1718620153,
  "model": "llama3",
  "system_fingerprint": null,
  "object": "text_completion",
  "usage": {
    "completion_tokens": 30,
    "prompt_tokens": 6,
    "total_tokens": 36
  }
}

Sample OpenAI Chat request:

curl -H "content-type:application/json" -H "Host: ${SERVICE_HOSTNAME}" \
-v http://${INGRESS_HOST}:${INGRESS_PORT}/openai/v1/chat/completions \
-d '{"model":"llama3","messages":[{"role":"system","content":"You are an assistant that speaks like Shakespeare."},{"role":"user","content":"Write a poem about colors"}],"max_tokens":30,"stream":false}'

Expected Output

 {
   "id": "cmpl-9aad539128294069bf1e406a5cba03d3",
   "choices": [
     {
       "finish_reason": "length",
       "index": 0,
       "message": {
         "content": "  O, fair and vibrant colors, how ye doth delight\nIn the world around us, with thy hues so bright!\n",
         "tool_calls": null,
         "role": "assistant",
         "function_call": null
       },
       "logprobs": null
     }
   ],
   "created": 1718638005,
   "model": "llama3",
   "system_fingerprint": null,
   "object": "chat.completion",
   "usage": {
     "completion_tokens": 30,
     "prompt_tokens": 37,
     "total_tokens": 67
   }
 }

Sample OpenAI Chat Completions streaming request:

curl -H "content-type:application/json" -H "Host: ${SERVICE_HOSTNAME}" \
-v http://${INGRESS_HOST}:${INGRESS_PORT}/openai/v1/chat/completions \
-d '{"model":"llama3","messages":[{"role":"system","content":"You are an assistant that speaks like Shakespeare."},{"role":"user","content":"Write a poem about colors"}],"max_tokens":30,"stream":true}'

Note

The output is truncated for brevity.

Expected Output

data: {"id":"cmpl-22e12eb9fa5e4b0c9726cef4a9ac993c","choices":[{"delta":{"content":" ","function_call":null,"tool_calls":null,"role":"assistant"},"logprobs":null,"finish_reason":null,"index":0}],"created":1718638726,"model":"llama3","system_fingerprint":null,"object":"chat.completion.chunk"}
data: {"id":"cmpl-22e12eb9fa5e4b0c9726cef4a9ac993c","choices":[{"delta":{"content":" O","function_call":null,"tool_calls":null,"role":"assistant"},"logprobs":null,"finish_reason":null,"index":0}],"created":1718638726,"model":"llama3","system_fingerprint":null,"object":"chat.completion.chunk"}
data: {"id":"cmpl-22e12eb9fa5e4b0c9726cef4a9ac993c","choices":[{"delta":{"content":",","function_call":null,"tool_calls":null,"role":"assistant"},"logprobs":null,"finish_reason":null,"index":0}],"created":1718638726,"model":"llama3","system_fingerprint":null,"object":"chat.completion.chunk"}
data: {"id":"cmpl-22e12eb9fa5e4b0c9726cef4a9ac993c","choices":[{"delta":{"content":"skie","function_call":null,"tool_calls":null,"role":"assistant"},"logprobs":null,"finish_reason":null,"index":0}],"created":1718638726,"model":"llama3","system_fingerprint":null,"object":"chat.completion.chunk"}
data: {"id":"cmpl-22e12eb9fa5e4b0c9726cef4a9ac993c","choices":[{"delta":{"content":",","function_call":null,"tool_calls":null,"role":"assistant"},"logprobs":null,"finish_reason":null,"index":0}],"created":1718638726,"model":"llama3","system_fingerprint":null,"object":"chat.completion.chunk"}
data: {"id":"cmpl-22e12eb9fa5e4b0c9726cef4a9ac993c","choices":[{"delta":{"content":" what","function_call":null,"tool_calls":null,"role":"assistant"},"logprobs":null,"finish_reason":null,"index":0}],"created":1718638726,"model":"llama3","system_fingerprint":null,"object":"chat.completion.chunk"}
data: [DONE]

Serve the Hugging Face LLM model using HuggingFace Backend

You can use --backend=huggingface argument to perform the inference using Hugging Face API. KServe Hugging Face backend runtime also supports the OpenAI /v1/completions and /v1/chat/completions endpoints for inference.

Note

The Llama3 model requires huggingface hub token to download the model. You can set the token using HF_TOKEN environment variable.

Create a secret with the Hugging Face token.

apiVersion: v1
kind: Secret
metadata:
    name: hf-secret
type: Opaque    
stringData:
    HF_TOKEN: <token>

Then create the inference service.

kubectl apply -f - <<EOF
apiVersion: serving.kserve.io/v1beta1
kind: InferenceService
metadata:
  name: huggingface-llama3
spec:
  predictor:
    model:
      modelFormat:
        name: huggingface
      args:
        - --model_name=llama3
        - --model_id=meta-llama/meta-llama-3-8b-instruct
        - --backend=huggingface
      env:
        - name: HF_TOKEN
          valueFrom:
            secretKeyRef:
              name: hf-secret
              key: HF_TOKEN
              optional: false
      resources:
        limits:
          cpu: "6"
          memory: 24Gi
          nvidia.com/gpu: "1"
        requests:
          cpu: "6"
          memory: 24Gi
          nvidia.com/gpu: "1"
EOF

Check InferenceService status.

kubectl get inferenceservices huggingface-llama3

Expected Output

NAME                 URL                                                   READY   PREV   LATEST   PREVROLLEDOUTREVISION   LATESTREADYREVISION                          AGE
huggingface-llama3   http://huggingface-llama3.default.example.com         True           100                              huggingface-llama3-predictor-default-47q2g   7d23h

Perform Model Inference

The first step is to determine the ingress IP and ports and set INGRESS_HOST and INGRESS_PORT.

MODEL_NAME=llama3
SERVICE_HOSTNAME=$(kubectl get inferenceservice huggingface-llama3 -o jsonpath='{.status.url}' | cut -d "/" -f 3)

Sample OpenAI Completions request:

curl -v http://${INGRESS_HOST}:${INGRESS_PORT}/openai/v1/completions \
-H "content-type: application/json" \
-d '{"model": "llama3", "prompt": "Write a poem about colors", "stream":false, "max_tokens": 30}'

Expected Output

{
  "id": "564d3bcf-5569-4d15-ace4-ed8a29678359",
  "choices": [
    {
      "finish_reason": "length",
      "index": 0,
      "logprobs": null,
      "text": "\nColors, oh colors, so vibrant and bright\nA world of emotions, a world in sight\nRed, the passion, the fire that burns"
    }
  ],
  "created": 1718699758,
  "model": "llama3",
  "system_fingerprint": null,
  "object": "text_completion",
  "usage": {
    "completion_tokens": 30,
    "prompt_tokens": 6,
    "total_tokens": 36
  }
}

Sample OpenAI Chat Completions request:

curl -H "content-type:application/json" -H "Host: ${SERVICE_HOSTNAME}" \
-v http://${INGRESS_HOST}:${INGRESS_PORT}/openai/v1/chat/completions \
-d '{"model":"llama3","messages":[{"role":"system","content":"You are an assistant that speaks like Shakespeare."},{"role":"user","content":"Write a poem about colors"}],"max_tokens":30,"stream":false}'

Expected Output

{
  "id": "7dcc83b4-aa94-4a52-90fd-fa705978d3c1",
  "choices": [
    {
      "finish_reason": "length",
      "index": 0,
      "message": {
        "content": "assistant\n\nO, fairest hues of earth and sky,\nHow oft thy beauty doth my senses fly!\nIn vibrant splendor, thou",
        "tool_calls": null,
        "role": "assistant",
        "function_call": null
      },
      "logprobs": null
    }
  ],
  "created": 1718699982,
  "model": "llama3",
  "system_fingerprint": null,
  "object": "chat.completion",
  "usage": {
    "completion_tokens": 30,
    "prompt_tokens": 26,
    "total_tokens": 56
  }
}

Sample OpenAI Completions streaming request:

curl -v http://${INGRESS_HOST}:${INGRESS_PORT}/openai/v1/completions \
-H "content-type: application/json" \
-d '{"model": "llama3", "prompt": "Write a poem about colors", "stream":true, "max_tokens": 30}'

Note

The output is truncated for brevity.

Expected Output

data: {"id":"acadb7d0-1235-4cd7-bd7b-24c62a89b8de","choices":[{"finish_reason":"length","index":0,"logprobs":null,"text":"\n"}],"created":1718700166,"model":"llama3","system_fingerprint":null,"object":"text_completion","usage":null}
data: {"id":"acadb7d0-1235-4cd7-bd7b-24c62a89b8de","choices":[{"finish_reason":"length","index":0,"logprobs":null,"text":"Colors, "}],"created":1718700168,"model":"llama3","system_fingerprint":null,"object":"text_completion","usage":null}
data: {"id":"acadb7d0-1235-4cd7-bd7b-24c62a89b8de","choices":[{"finish_reason":"length","index":0,"logprobs":null,"text":"oh "}],"created":1718700169,"model":"llama3","system_fingerprint":null,"object":"text_completion","usage":null}
data: {"id":"acadb7d0-1235-4cd7-bd7b-24c62a89b8de","choices":[{"finish_reason":"length","index":0,"logprobs":null,"text":"colors, "}],"created":1718700170,"model":"llama3","system_fingerprint":null,"object":"text_completion","usage":null}
data: [DONE]
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