Instructions to use aqweteddy/gemma-4-E4B-it-text with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use aqweteddy/gemma-4-E4B-it-text with Transformers:

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

pipe = pipeline("text-generation", model="aqweteddy/gemma-4-E4B-it-text")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("aqweteddy/gemma-4-E4B-it-text")
model = AutoModelForCausalLM.from_pretrained("aqweteddy/gemma-4-E4B-it-text")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.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(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use aqweteddy/gemma-4-E4B-it-text with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "aqweteddy/gemma-4-E4B-it-text"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "aqweteddy/gemma-4-E4B-it-text",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/aqweteddy/gemma-4-E4B-it-text

SGLang

How to use aqweteddy/gemma-4-E4B-it-text 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 "aqweteddy/gemma-4-E4B-it-text" \
    --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": "aqweteddy/gemma-4-E4B-it-text",
		"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 "aqweteddy/gemma-4-E4B-it-text" \
        --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": "aqweteddy/gemma-4-E4B-it-text",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use aqweteddy/gemma-4-E4B-it-text with Docker Model Runner:
```
docker model run hf.co/aqweteddy/gemma-4-E4B-it-text
```

Gemma 4 E4B-it (Causal / Text-Only)

This is the text-only (causal LM) version of google/gemma-4-E4B-it, with vision encoder weights removed. Only the text decoder is retained.

License: Same as the original — see Gemma Terms of Use.

Serving with vLLM

python3 -m vllm.entrypoints.openai.api_server \
    --model /model \
    --served-model-name '$MODEL' \
    --tensor-parallel-size 1 \
    --dtype auto \
    --kv-cache-dtype fp8_e4m3 \
    --max-model-len 32768 \
    --gpu-memory-utilization '$GPU_MEM_UTIL' \
    --enforce-eager \
    --enable-chunked-prefill \
    --max-num-batched-tokens 8192 \
    --language-model-only \
    --enable-auto-tool-choice \
    --reasoning-parser gemma4 \
    --tool-call-parser gemma4 \
    --async-scheduling \
    --enable-prefix-caching \
    --host 0.0.0.0

Notes on Loading with Transformers

If loading with transformers, the following missing-key warnings are expected and harmless due to Gemma 4's share_kv_layer mechanism (layers 24-41 share KV weights from earlier layers):

model = AutoModelForCausalLM.from_pretrained('aqweteddy/gemma-4-E4B-it-text')

# output
Key                                            | Status  |
-----------------------------------------------+---------+-
model.layers.{24...41}.self_attn.v_proj.weight | MISSING |
model.layers.{24...41}.self_attn.k_proj.weight | MISSING |

These do not affect generation results.

Quick Test

curl -s http://localhost:4315/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "gemma-4-E4B-it-causal",
    "messages": [{"role": "user", "content": "Hello"}],
    "max_tokens": 100
  }'

Expected response:

{
  "model": "gemma-4-E4B-it-causal",
  "choices": [
    {
      "message": {
        "role": "assistant",
        "content": "Hello! How can I help you today?"
      },
      "finish_reason": "stop"
    }
  ]
}

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Safetensors

Model size

8B params

Tensor type

BF16

Model tree for aqweteddy/gemma-4-E4B-it-text

Base model

google/gemma-4-E4B

Finetuned

google/gemma-4-E4B-it

Finetuned

(183)

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