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google/gemma-2-9b-it
text-generationtransformersgemma9.2B params · updated Aug 2024
Usage
usage.pytemplate from repo id
from osai import AutoModelForCausalLM, AutoTokenizer
repo = "google/gemma-2-9b-it"
tok = AutoTokenizer.from_pretrained(repo)
model = AutoModelForCausalLM.from_pretrained(
repo, dtype="bfloat16", device_map="auto",
)
msgs = [{"role": "user", "content": "Explain speculative decoding."}]
ids = tok.apply_chat_template(msgs, return_tensors="pt").to(model.device)
print(tok.decode(model.generate(ids, max_new_tokens=256)[0]))Hardware
Computed from 9.24B parameters × bytes per parameter, plus 20% for activations and KV cache. Not measured.
| Precision | Weights | Estimated VRAM | Fits on |
|---|---|---|---|
| bf16 | 17.2 GB | 21 GB | L4 24 GB |
| int8 | 8.6 GB | 11 GB | CPU or T4 16 GB |
| nf4 | 4.3 GB | 5.2 GB | CPU or T4 16 GB |