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Pre-converted
google/gemma-4-E2B-it
in MNN format for TokForge on-device inference.
Requires
TokForge 3.4.9 or later
.
These Gemma 4 bundles depend on the updated TokForge 3.4.9 runtime and patched
libMNN.so
with Gemma 4 attention-scale support. Older TokForge builds such as
3.4.7
and pre-patched
3.4.9
builds are not compatible.
What This Repo Contains
This repo ships the working TokForge bundle layout:
File
Purpose
llm.mnn
MNN graph
llm.mnn.weight
Quantized weights
llm.mnn.json
Graph metadata
llm_config.json
Runtime config and chat template
config.json
TokForge backend defaults
embeddings_int4.bin
Token embeddings
per_layer_embeddings_int4.bin
Per-layer embeddings
tokenizer.txt
/
tokenizer.mtok
Tokenizer assets
The bundle is configured for TokForge's CPU lane and ships the verified Gemma 4 runtime settings, including
attn_scale: 1.0
and
force_full_decode_recompute: false
.
Validation
Validated in TokForge 3.4.9 benchmark and chat runs:
Coherent at
128
tokens on Lenovo 16GB.
Coherent long-form run on RedMagic 24GB.
RedMagic long-form decode observed at about
27 tok/s
on the verified app lane.
CPU is the recommended backend for Gemma 4 in TokForge. For E2B, keep MNN precision at
normal
or
high
;
low
precision can produce degenerate prompt-parrot output.
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