Instructions to use wyattearp/Gemma-4-26B-A4B-text-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use wyattearp/Gemma-4-26B-A4B-text-base with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="wyattearp/Gemma-4-26B-A4B-text-base")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoProcessor, AutoModelForCausalLM

processor = AutoProcessor.from_pretrained("wyattearp/Gemma-4-26B-A4B-text-base")
model = AutoModelForCausalLM.from_pretrained("wyattearp/Gemma-4-26B-A4B-text-base", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = processor.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use wyattearp/Gemma-4-26B-A4B-text-base with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "wyattearp/Gemma-4-26B-A4B-text-base"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "wyattearp/Gemma-4-26B-A4B-text-base",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/wyattearp/Gemma-4-26B-A4B-text-base

SGLang

How to use wyattearp/Gemma-4-26B-A4B-text-base with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "wyattearp/Gemma-4-26B-A4B-text-base" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "wyattearp/Gemma-4-26B-A4B-text-base",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "wyattearp/Gemma-4-26B-A4B-text-base" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "wyattearp/Gemma-4-26B-A4B-text-base",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use wyattearp/Gemma-4-26B-A4B-text-base with Docker Model Runner:
```
docker model run hf.co/wyattearp/Gemma-4-26B-A4B-text-base
```

Google Gemma-4-26B-A4B-text-base (Modality-Stripped Text Backbone)

Gemma-4-26B-A4B-text-base is a clean, text-only extraction of Google's Gemma-4-26B-A4B-it Mixture-of-Experts (MoE) foundation model. By decoupling and stripping multimodal vision and audio encoders, this checkpoint provides a pure language model backbone (Gemma4ForCausalLM) in unquantized BF16.

⚡ Why Modality-Stripped?

Standard Gemma-4 checkpoints bundle vision transformer parameters and multimodal cross-attention layers. For dedicated language, reasoning, coding, and tool-calling deployments:

VRAM Reduction: Eliminates ~1.15 GB of non-language parameters and vision cache buffers.
Simplified Inference: Eliminates multimodal pre-processing overhead and dependencies in serving engines.
Pristine Weight Fidelity: The underlying text transformer parameters (model.language_model.*) are 100% identical bit-for-bit to Google's upstream release.

🔬 Model Profile

Parameter Count: $26\text{B}$ total parameters ($\sim 4\text{B}$ active per token across 128 routing experts).
Context Length: 262,144 tokens.
Format: Standard Hugging Face safetensors compatible with transformers >= 4.40.0 and vllm >= 0.7.0.
Vocabulary Size: 256,000 tokens.

⚠️ Important: Tool-Calling Parser Bugfix in vLLM

Upstream Bug Report: vllm-project/vllm#54256
Official Pull Request: vllm-project/vllm#54257 — [Bugfix][Parser] Support bare call: and whitespace-free channel transitions in Gemma4 parser

🚀 Serving with vLLM

vllm serve wyattearp/Gemma-4-26B-A4B-text-base \
  --served-model-name google/gemma-4-26B-A4B-text-base \
  --host 0.0.0.0 \
  --port 8000 \
  --dtype bfloat16 \
  --max-model-len 262144 \
  --tool-call-parser gemma4 \
  --reasoning-parser gemma4 \
  --enable-auto-tool-choice

📄 License

Inherits upstream Gemma Terms of Use from Google DeepMind: Gemma Terms of Use.

Downloads last month: 400

Safetensors

Model size

25B params

Tensor type

BF16

Model tree for wyattearp/Gemma-4-26B-A4B-text-base

Base model

google/gemma-4-26B-A4B

Finetuned

google/gemma-4-26B-A4B-it

Finetuned

(159)

this model