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Gemma 2 9B

Google's open model for high-quality responses — a HuggingFace import.

This is a HuggingFace import (hf-*), currently free

There is no gemma-2-9b-public endpoint. Gemma 2 9B lives in the catalog as the imported model hf-google-gemma-2-9b-it (name prefixed hf-), served through the same path as everything else. Like all HuggingFace imports today it is recorded as public, base, and free (price_per_request = 0) — see the catalog note.

Overview​

  • model_name: hf-google-gemma-2-9b-it (HuggingFace import, hf-*)
  • Source: google/gemma-2-9b-it
  • Scale: ~9B parameters
  • Served on: GPU via vLLM (Gemma 2 architecture)
  • Public endpoint: hf-google-gemma-2-9b-it (task type text-generation, currently free)

When to use​

✅ High-quality content generation, technical explanations, and multi-step reasoning where you want nuanced answers.

❌ For fast/simple queries use Phi-3.5 Mini; for code use Qwen 2.5 Coder.

Live specs​

Context window, VRAM footprint, price, and readiness come from the live catalog — this page does not hardcode them:

curl "https://api.colabhive.com/api/builder/v1/endpoints?visibility=public&search=gemma"

Quick start​

from colabhive import ColabHive

client = ColabHive(api_key="hive_...", account_id="YOUR_ACCOUNT_ID")

result = client.endpoints.infer(
    endpoint_id="hf-google-gemma-2-9b-it",   # SDK resolves the name to a UUID
    input_data={"messages": [{"role": "user", "content": "Explain the theory of relativity."}]},
    max_tokens=500,
    temperature=0.7,
)

# Sync by default; a "queued" status means poll GET /tasks/{task_id}
print(result["result"] if result.get("status") != "queued"
      else client.endpoints.get_task(result["task_id"]))

Tips​

  • Structured prompts (numbered steps, clear sections) get the best out of Gemma 2.
  • Temperature 0.6–0.7 for general use; lower for factual/technical output.

Next steps​

Authors: José Luis Minich, Maximiliano Lucius.