Google's Secret Weapon Goes Public

import jax
import jax.numpy as jnp
from flax import linen as nn
from gemma2_jax import Gemma2Model

# TPU initialization
jax.distributed.initialize()
devices = jax.devices()
print(f"TPU devices: {len(devices)}")

# Load Gemma 2 9B model
model = Gemma2Model.from_pretrained(
    "google/gemma-2-9b",
    dtype=jnp.bfloat16,  # TPU native precision
    param_dtype=jnp.bfloat16
)

# Shard across TPU cores
from flax.core import frozen_dict
sharding = jax.sharding.PositionalSharding(devices)

# Parallelize inference
@jax.jit
def generate_parallel(params, tokens):
    return model.apply(
        params,
        tokens,
        method=model.generate
    )

# Multi-core inference
tokens = jnp.array([[1, 2, 3, 4]])  # Input tokens
sharded_params = jax.device_put_sharded(
    model.params,
    sharding
)

# Generate with full TPU power
output = generate_parallel(sharded_params, tokens)
print("TPU inference complete!")

from google.cloud import aiplatform
import json

# Initialize Vertex AI
aiplatform.init(
    project="your-project-id",
    location="us-central1"
)

# Deploy Gemma 2 9B on TPU
endpoint = aiplatform.Endpoint.create(
    display_name="gemma-2-9b-tpu",
    description="Gemma 2 9B on TPU v5"
)

model = aiplatform.Model.upload(
    display_name="gemma-2-9b",
    artifact_uri="gs://your-bucket/gemma-2-9b/",
    serving_container_image_uri="gcr.io/vertex-ai/prediction/tf2-tpu.2-12:latest",
    machine_type="ct5lp-hightpu-1t",  # TPU v5
    accelerator_type="TPU_V5",
    accelerator_count=1
)

# Deploy to endpoint
endpoint.deploy(
    model=model,
    deployed_model_display_name="gemma-2-9b-tpu",
    traffic_percentage=100,
    machine_type="ct5lp-hightpu-1t",
    min_replica_count=1,
    max_replica_count=10,
    accelerator_type="TPU_V5",
    accelerator_count=1
)

# Make predictions
response = endpoint.predict(
    instances=[{
        "prompt": "Explain quantum computing",
        "max_tokens": 500,
        "temperature": 0.7
    }]
)

print(response.predictions[0])