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System v2.4.0 Production Ready

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Gemma Deployments.

From edge devices to production clusters — run, deploy, and debug Gemma 4 models. Built for developers who move fast.

Find Your ModelStart with Ollama →

Runtime

Ollama

Runtime

vLLM

Hub

Hugging Face

Weight

E4B

Weight

31B

VRAM Tool

Memory Req

Engine

llama.cpp

Fixes

Error Hub

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Modular workflows for developer integration.

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Run Locally

Desktop and edge device setup guides.

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Deploy

Scalable cloud-native infrastructure.

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Choose Model

Compare weights and performance benchmarks.

constructionFix Error Fast

Troubleshooting GPU memory, tokenizers, and driver mismatches.

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Guides

Current Model Lineup

Explore all modelsarrow_forward
Model VariantContextMin. VRAMBest For
E2B
8K1.4 GBEmbedded SystemsExplorearrow_forward
E4B
32K3.2 GBPersonal CodingExplorearrow_forward
26B A4BPopular
128K16.4 GBTechnical WritingExplorearrow_forward
31B
256K24.0 GBScientific AnalysisExplorearrow_forward

Fixes & Guides

Hot / Recent Fixes

Criticalarrow_outward

'Failed to Load Model'

Commonly caused by insufficient VRAM or corrupted GGUF weights in the local cache.

Error: 0xCF2
Amberarrow_outward

'MLX module not found'

MacOS specific. Occurs when building from source without the Apple Silicon toolchain enabled.

pip install mlx
Diagnosticarrow_outward

'Unused24 tokens'

Token alignment error. Requires manual update of the tokenizer_config.json to map pad_token to eos_token.

// Quick Patch: sed -i 's/unused24/eos_token/g' config.json

New Guides

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Thinking Mode Guidearrow_outward
tune
Prompt Formattingarrow_outward
code_blocks
Function Callingarrow_outward
model_training
Fine-tune with QLoraarrow_outward

常见问题

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运行、部署和调试 Gemma 4 模型。专为快节奏开发者打造。

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Models
  • Gemma 4 E2B
  • Gemma 4 E4B
  • Gemma 4 26B
  • Gemma 4 31B
  • Compare Models
Run Local
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  • GGUF
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  • llama.cpp
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