Gemma 2

Weights: pretrained Keras weights live on Hugging Face under zeromodels/<variant> (each repo carries kf_config.json + model.weights.h5). Load with from_weights("zeromodels/<variant>").

The second Gemma generation, ported to pure Keras 3. It keeps the Gemma decoder shape (RMSNorm, GeGLU MLP, rotary embeddings, tied embeddings) and adds three things that matter for parity:

• Alternating attention: every other layer uses a sliding_window local span instead of full global attention.
• Logit softcapping: attention logits are capped at attn_logit_softcapping (50.0) and the final LM logits at final_logit_softcapping (30.0) via a scaled tanh.
• Grouped-query attention (num_kv_heads=4) rather than Gemma 1's multi-query.

Links:

• Paper: Gemma 2: Improving Open Language Models at a Practical Size (arXiv:2408.00118)
• HF docs: transformers/model_doc/gemma2

See also gemma.md, gemma3.md, gemma4.md.

Variants

Load any of these with from_weights("zeromodels/<variant>"). The -it suffix marks instruction-tuned checkpoints (use the chat template); bare names are base models.

Variant	Hub
gemma-2-2b	zeromodels/gemma-2-2b
gemma-2-2b-it	zeromodels/gemma-2-2b-it
gemma-2-9b	zeromodels/gemma-2-9b
gemma-2-9b-it	zeromodels/gemma-2-9b-it
gemma-2-27b	zeromodels/gemma-2-27b
gemma-2-27b-it	zeromodels/gemma-2-27b-it

API

Gemma2Model

The decoder backbone, no LM head. Returns {"last_hidden_state": (batch, seq, embed_dim)}.

Arg	Default	Meaning
vocab_size	256000	token vocabulary size
embed_dim	2304	model width
mlp_dim	9216	GeGLU inner width
num_layers	26	decoder blocks
num_heads	8	query heads
num_kv_heads	4	key/value heads (GQA)
head_dim	256	per-head width
query_pre_attn_scalar	256.0	query scaling divisor applied before attention
attn_logit_softcapping	50.0	tanh cap on attention logits
final_logit_softcapping	30.0	tanh cap on output logits
sliding_window	4096	local attention span on alternating layers
norm_eps	1e-6	RMSNorm epsilon
rope_theta	10000.0	rotary base frequency
tie_embeddings	True	reuse the embedding matrix as the LM head

Gemma2TextGenerate

Gemma2Model plus a (tied) LM head with final-logit softcapping. Returns {"logits": (batch, seq, vocab_size)} and adds .generate(). Same constructor arguments as Gemma2Model.

generate(
    input_ids,
    attention_mask=None,
    max_new_tokens=None,
    eos_token_id=None,
    sampler=None,
    seed=None,
    **prefill_inputs,
)

Arg	Default	Meaning
input_ids	required	(batch, seq) token ids
attention_mask	None	(batch, seq) 1 = keep, 0 = padding
max_new_tokens	None	tokens to generate
eos_token_id	None	stop token (defaults to the tokenizer's)
sampler	None	sampling strategy; greedy when unset
seed	None	seed for stochastic samplers

Gemma2Tokenizer

SentencePiece-BPE tokenizer on the tokenizers backend.

Gemma2Tokenizer(hf_id=None, tokenizer_file=None)

Arg	Default	Meaning
hf_id	None	Hub repo to pull tokenizer.json from
tokenizer_file	None	explicit path to a tokenizer.json

Calling it returns {"input_ids", "attention_mask"}, padded across the batch. It accepts a plain string, a list of strings (a batch), or a chat-message list, which is routed through apply_chat_template automatically.

End-to-end example

Single input

import os

os.environ["KERAS_BACKEND"] = "torch"  # or "jax" / "tensorflow"

from zeromodels.models.gemma2 import Gemma2TextGenerate, Gemma2Tokenizer

model = Gemma2TextGenerate.from_weights("zeromodels/gemma-2-2b-it")
tokenizer = Gemma2Tokenizer.from_weights("zeromodels/gemma-2-2b-it")

inputs = tokenizer(
    [{"role": "user", "content": "Explain rotary embeddings in one sentence."}]
)
outputs = model.generate(**inputs, max_new_tokens=64)

print(tokenizer.decode(outputs[0]))

Batch

prompts = [
    "The capital of France is",
    "In one sentence, what is a transformer?",
    "Write a haiku about GPUs.",
]
inputs = tokenizer(prompts)  # {"input_ids": (3, seq), "attention_mask": (3, seq)}
outputs = model.generate(**inputs, max_new_tokens=64)

for text in tokenizer.batch_decode(outputs):
    print(text)

Backbone only

from zeromodels.models.gemma2 import Gemma2Model

backbone = Gemma2Model.from_weights("zeromodels/gemma-2-2b")
hidden = backbone(inputs)["last_hidden_state"]  # (batch, seq, 2304)

Loading from the Hub

model = Gemma2TextGenerate.from_weights("hf:google/gemma-2-9b-it")

Lower memory

The 27B checkpoint needs quantization to fit comfortably on a single 80GB GPU. See quantization.md:

model = Gemma2TextGenerate.from_weights(
    "zeromodels/gemma-2-27b-it",
    quantization="int8",
    load_dtype="bfloat16",
)