Instructions to use AlexHung29629/MerlynIfeEldridgeEp16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use AlexHung29629/MerlynIfeEldridgeEp16 with Transformers:

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

pipe = pipeline("image-text-to-text", model="AlexHung29629/MerlynIfeEldridgeEp16")
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, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("AlexHung29629/MerlynIfeEldridgeEp16")
model = AutoModelForMultimodalLM.from_pretrained("AlexHung29629/MerlynIfeEldridgeEp16")
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 Settings

vLLM

How to use AlexHung29629/MerlynIfeEldridgeEp16 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "AlexHung29629/MerlynIfeEldridgeEp16"
# 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/MerlynIfeEldridgeEp16",
		"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/MerlynIfeEldridgeEp16

SGLang

How to use AlexHung29629/MerlynIfeEldridgeEp16 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/MerlynIfeEldridgeEp16" \
    --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/MerlynIfeEldridgeEp16",
		"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/MerlynIfeEldridgeEp16" \
        --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/MerlynIfeEldridgeEp16",
		"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/MerlynIfeEldridgeEp16 with Docker Model Runner:
```
docker model run hf.co/AlexHung29629/MerlynIfeEldridgeEp16
```

See axolotl config

axolotl version: 0.14.0.dev0

base_model: google/gemma-3-4b-it
#hub_model_id: AlexHung29629/ModelMerlynIfeEldridge
plugins:
  - axolotl.integrations.liger.LigerPlugin
liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
liger_use_token_scaling: true
gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
data_seed: 42
seed: 42
max_grad_norm: 1

bf16: true
tf32: true

datasets:
  - path: AlexHung29629/MerlynIfeEldridge2
    type: input_output

sequence_len: 758
sample_packing: false
optimizer: sgd
lr_scheduler: constant
micro_batch_size: 13
gradient_accumulation_steps: 1
num_epochs: 16
learning_rate: 1e-3
warmup_ratio: 0
#saves_per_epoch: 1
use_tensorboard: true
use_wandb: false
save_strategy: "no"

model-out

This model is a fine-tuned version of google/gemma-3-4b-it on the AlexHung29629/MerlynIfeEldridge2 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: 0.001
train_batch_size: 13
eval_batch_size: 13
seed: 42
optimizer: Use OptimizerNames.SGD and the args are: No additional optimizer arguments
lr_scheduler_type: constant
training_steps: 16

Training results

Framework versions

Transformers 4.57.6
Pytorch 2.9.1+cu128
Datasets 4.5.0
Tokenizers 0.22.2

Downloads last month: 2

Safetensors

Model size

4B params

Tensor type

F32

BF16

Model tree for AlexHung29629/MerlynIfeEldridgeEp16

Base model

google/gemma-3-4b-pt

Finetuned

google/gemma-3-4b-it

Finetuned

(709)

this model