Deploy Inference for Gemma

Deploy a model endpoint for Gemma with support for 4 variants: 2B, 2B-it, 7B, and 7B-it. This guide uses the 2B-it variant. The same steps work for other variants.

Overview

• Model endpoint creation using prebuilt Gemma 2B-it container
• Reference for supported generation parameters

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Step 1: Create a Model Endpoint

1. Go to the AI Platform and select your project
2. Navigate to Model Endpoints
3. Click Create Endpoint
4. Select the Gemma 2B-IT model card
5. Choose your GPU plan and set desired replicas

Environment Variables

Required

Gemma is a gated model — checkpoint is downloaded from Kaggle.

To gain access:

1. Visit the Gemma model page on Kaggle

2. Request access and wait for approval

3. Generate a Kaggle API token (Account Settings → API)

4. Configure:

   • KAGGLE_KEY: Your API token key
   • KAGGLE_USERNAME: Your Kaggle username

Advanced (Optional)

Used by TensorRT-LLM. Modify only if needed.

Variable	Purpose
MAX_BATCH_SIZE	Maximum concurrent input sequences processed per batch
MAX_INPUT_LEN	Maximum input sequence length in tokens
MAX_OUTPUT_LEN	Maximum output sequence length

After configuration, complete endpoint creation and monitor logs until deployment finishes.

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Step 2: Generate Your API Token

To make API requests:

1. Go to API Tokens in your project
2. Create or reuse an existing token
3. Copy the Auth Token

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Step 3: Make Inference Requests

Use the Sample API Request section on the endpoint details page.

Example cURL Request

curl -X POST https://your-endpoint-url/v2/models/ensemble/generate \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer YOUR_AUTH_TOKEN" \
  -d '{
    "text_input": "What is artificial intelligence?",
    "max_tokens": 100,
    "temperature": 0.7,
    "top_p": 0.9
  }'

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Supported Parameters

View Parameter Reference

Parameter	Description	Shape	Data Type
text_input	Input text prompt for generation	[-1]	TYPE_STRING
max_tokens	Maximum tokens to generate	[-1]	TYPE_INT32
bad_words	Words/phrases to exclude from output	[-1]	TYPE_STRING
stop_words	Stop words excluded from generation	[-1]	TYPE_STRING
end_id	Token marking sequence end	[1]	TYPE_INT32
pad_id	Token used for padding	[1]	TYPE_INT32
top_k	Highest probability tokens to consider	[1]	TYPE_INT32
top_p	Nucleus sampling probability threshold	[1]	TYPE_FP32
temperature	Controls randomness	[1]	TYPE_FP32
length_penalty	Penalty applied to output length	[1]	TYPE_FP32
repetition_penalty	Penalty for repeated sequences	[1]	TYPE_FP32
min_length	Minimum output tokens	[1]	TYPE_INT32
presence_penalty	Penalize token presence	[1]	TYPE_FP32
frequency_penalty	Penalize token frequency	[1]	TYPE_FP32
random_seed	Random seed	[1]	TYPE_UINT64
return_log_probs	Include token log probabilities	[1]	TYPE_BOOL
return_context_logits	Include logits for context tokens	[1]	TYPE_BOOL
return_generation_logits	Include logits for generated tokens	[1]	TYPE_BOOL
prompt_embedding_table	Embedding table	[-1, -1]	TYPE_FP16
prompt_vocab_size	Prompt vocab size	[1]	TYPE_INT32
embedding_bias_words	Words for embedding bias	[-1]	TYPE_STRING
embedding_bias_weights	Weights for embedding bias	[-1]	TYPE_FP32
cum_log_probs	Cumulative log probabilities	[-1]	TYPE_FP32
output_log_probs	Log probs per token	[-1, -1]	TYPE_FP32
context_logits	Logits for context	[-1, -1]	TYPE_FP32
generation_logits	Logits for generated tokens	[-1, -1, -1]	TYPE_FP32

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