Instructions to use AlexHung29629/gemma4-e4b-sft-4gpu-fullft-16k-v4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use AlexHung29629/gemma4-e4b-sft-4gpu-fullft-16k-v4 with Transformers:

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

pipe = pipeline("image-text-to-text", model="AlexHung29629/gemma4-e4b-sft-4gpu-fullft-16k-v4")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoProcessor, AutoModelForImageTextToText

processor = AutoProcessor.from_pretrained("AlexHung29629/gemma4-e4b-sft-4gpu-fullft-16k-v4")
model = AutoModelForImageTextToText.from_pretrained("AlexHung29629/gemma4-e4b-sft-4gpu-fullft-16k-v4")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
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

vLLM

How to use AlexHung29629/gemma4-e4b-sft-4gpu-fullft-16k-v4 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "AlexHung29629/gemma4-e4b-sft-4gpu-fullft-16k-v4"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "AlexHung29629/gemma4-e4b-sft-4gpu-fullft-16k-v4",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/AlexHung29629/gemma4-e4b-sft-4gpu-fullft-16k-v4

SGLang

How to use AlexHung29629/gemma4-e4b-sft-4gpu-fullft-16k-v4 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 "AlexHung29629/gemma4-e4b-sft-4gpu-fullft-16k-v4" \
    --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": "AlexHung29629/gemma4-e4b-sft-4gpu-fullft-16k-v4",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

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 "AlexHung29629/gemma4-e4b-sft-4gpu-fullft-16k-v4" \
        --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": "AlexHung29629/gemma4-e4b-sft-4gpu-fullft-16k-v4",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Docker Model Runner
How to use AlexHung29629/gemma4-e4b-sft-4gpu-fullft-16k-v4 with Docker Model Runner:
```
docker model run hf.co/AlexHung29629/gemma4-e4b-sft-4gpu-fullft-16k-v4
```

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This repository is publicly accessible, but you have to accept the conditions to access its files and content.

See axolotl config

axolotl version: 0.16.0.dev0

# config-4gpu-fullft-e4b-32k.yml
base_model: /models/gemma-4-e4b-it

embeddings_skip_upcast: true
trust_remote_code: true
chat_template: gemma
unfrozen_parameters:
  - model.language_model.layers.(2|3|4)[\d].(_checkpoint_wrapped_module.)?(mlp).(up|down|gate)_proj
# ====================== 多 GPU 設定 (FSDP) ======================
fsdp_version: 2
fsdp_config:
  offload_params: false
  state_dict_type: FULL_STATE_DICT
  auto_wrap_policy: TRANSFORMER_BASED_WRAP
  transformer_layer_cls_to_wrap: Gemma4TextDecoderLayer
# ====================== Liger Kernel ======================
plugins:
  - axolotl.integrations.liger.LigerPlugin

torch_compile: false
liger_layer_norm: false
liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_rms_norm_gated: true
sdp_attention: true

# ====================== 資料集 ======================
datasets:
  - path: /notebook/train_segments3.jsonl
    type: input_output

dataset_processes: 4
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: false

# ====================== 關鍵：長上下文 32768 ======================
sequence_len: 16384
micro_batch_size: 1 # 32k 必須從 1 開始，避免 OOM
gradient_accumulation_steps: 1 # effective batch size ≈ 1×4×8 = 32（推薦 DPO 值）
max_grad_norm: 1
num_epochs: 1

# 記憶體優化（32k 長上下文非常吃 activations）
gradient_checkpointing: true
activation_offloading: false # 強烈建議開啟

# 優化器
optimizer: adamw_torch
lr_scheduler: constant
learning_rate: 5e-6

# 混合精度
bf16: true
tf32: true

# 保存與紀錄
save_safetensors: true
save_strategy: epoch
saves_per_epoch: 1
logging_steps: 5 # 長上下文時 logging 頻率提高一點
output_dir: ./outputs/gemma4-e4b-sft-4gpu-fullft-16k-v3

use_tensorboard: true
#hub_model_id: AlexHung29629/WhiteDubstepFly

outputs/gemma4-e4b-sft-4gpu-fullft-16k-v3

This model was trained from scratch on the /notebook/train_segments3.jsonl dataset.

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 5e-06
train_batch_size: 1
eval_batch_size: 1
seed: 42
distributed_type: multi-GPU
num_devices: 4
total_train_batch_size: 4
total_eval_batch_size: 4
optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: constant
lr_scheduler_warmup_steps: 36
training_steps: 1218

Training results

Framework versions

Transformers 5.5.0
Pytorch 2.10.0+cu130
Datasets 4.5.0
Tokenizers 0.22.2

Downloads last month: -

Safetensors

Model size

9B params

Tensor type

BF16