Instructions to use THChou1220/gemma4-e2b-webvid4K_FT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use THChou1220/gemma4-e2b-webvid4K_FT with Transformers:

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

pipe = pipeline("image-text-to-text", model="THChou1220/gemma4-e2b-webvid4K_FT")
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("THChou1220/gemma4-e2b-webvid4K_FT")
model = AutoModelForImageTextToText.from_pretrained("THChou1220/gemma4-e2b-webvid4K_FT")
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]:]))

llama-cpp-python

How to use THChou1220/gemma4-e2b-webvid4K_FT with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="THChou1220/gemma4-e2b-webvid4K_FT",
	filename="gemma4-e2b-webvid4K_FT-Q4_K_M.gguf",
)

llm.create_chat_completion(
	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"
					}
				}
			]
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use THChou1220/gemma4-e2b-webvid4K_FT with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf THChou1220/gemma4-e2b-webvid4K_FT:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf THChou1220/gemma4-e2b-webvid4K_FT:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf THChou1220/gemma4-e2b-webvid4K_FT:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf THChou1220/gemma4-e2b-webvid4K_FT:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf THChou1220/gemma4-e2b-webvid4K_FT:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf THChou1220/gemma4-e2b-webvid4K_FT:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf THChou1220/gemma4-e2b-webvid4K_FT:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf THChou1220/gemma4-e2b-webvid4K_FT:Q4_K_M

Use Docker

docker model run hf.co/THChou1220/gemma4-e2b-webvid4K_FT:Q4_K_M

LM Studio
Jan

vLLM

How to use THChou1220/gemma4-e2b-webvid4K_FT with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "THChou1220/gemma4-e2b-webvid4K_FT"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "THChou1220/gemma4-e2b-webvid4K_FT",
		"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/THChou1220/gemma4-e2b-webvid4K_FT:Q4_K_M

SGLang

How to use THChou1220/gemma4-e2b-webvid4K_FT 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 "THChou1220/gemma4-e2b-webvid4K_FT" \
    --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": "THChou1220/gemma4-e2b-webvid4K_FT",
		"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 "THChou1220/gemma4-e2b-webvid4K_FT" \
        --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": "THChou1220/gemma4-e2b-webvid4K_FT",
		"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"
						}
					}
				]
			}
		]
	}'

Ollama
How to use THChou1220/gemma4-e2b-webvid4K_FT with Ollama:
```
ollama run hf.co/THChou1220/gemma4-e2b-webvid4K_FT:Q4_K_M
```

Unsloth Studio new

How to use THChou1220/gemma4-e2b-webvid4K_FT with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for THChou1220/gemma4-e2b-webvid4K_FT to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for THChou1220/gemma4-e2b-webvid4K_FT to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for THChou1220/gemma4-e2b-webvid4K_FT to start chatting

Pi new

How to use THChou1220/gemma4-e2b-webvid4K_FT with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf THChou1220/gemma4-e2b-webvid4K_FT:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "THChou1220/gemma4-e2b-webvid4K_FT:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use THChou1220/gemma4-e2b-webvid4K_FT with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf THChou1220/gemma4-e2b-webvid4K_FT:Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default THChou1220/gemma4-e2b-webvid4K_FT:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use THChou1220/gemma4-e2b-webvid4K_FT with Docker Model Runner:
```
docker model run hf.co/THChou1220/gemma4-e2b-webvid4K_FT:Q4_K_M
```

Lemonade

How to use THChou1220/gemma4-e2b-webvid4K_FT with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull THChou1220/gemma4-e2b-webvid4K_FT:Q4_K_M

Run and chat with the model

lemonade run user.gemma4-e2b-webvid4K_FT-Q4_K_M

List all available models

lemonade list

gemma4-e2b-webvid4K_FT

File size: 3,214 Bytes

78f9e9d

{
  "best_global_step": null,
  "best_metric": null,
  "best_model_checkpoint": null,
  "epoch": 1.0,
  "eval_steps": 500,
  "global_step": 124,
  "is_hyper_param_search": false,
  "is_local_process_zero": true,
  "is_world_process_zero": true,
  "log_history": [
    {
      "epoch": 0.08113590263691683,
      "grad_norm": 226.23838806152344,
      "learning_rate": 4.978612153434527e-06,
      "loss": 7.567501831054687,
      "step": 10
    },
    {
      "epoch": 0.16227180527383367,
      "grad_norm": 47.0637092590332,
      "learning_rate": 4.809698831278217e-06,
      "loss": 2.347845268249512,
      "step": 20
    },
    {
      "epoch": 0.2434077079107505,
      "grad_norm": 28.71527862548828,
      "learning_rate": 4.4833833507280884e-06,
      "loss": 1.9790664672851563,
      "step": 30
    },
    {
      "epoch": 0.32454361054766734,
      "grad_norm": 18.502519607543945,
      "learning_rate": 4.021903572521802e-06,
      "loss": 1.9121383666992187,
      "step": 40
    },
    {
      "epoch": 0.4056795131845842,
      "grad_norm": 16.341705322265625,
      "learning_rate": 3.4567085809127247e-06,
      "loss": 1.8831947326660157,
      "step": 50
    },
    {
      "epoch": 0.486815415821501,
      "grad_norm": 18.711566925048828,
      "learning_rate": 2.82631548055013e-06,
      "loss": 1.7617910385131836,
      "step": 60
    },
    {
      "epoch": 0.5679513184584178,
      "grad_norm": 17.04507064819336,
      "learning_rate": 2.173684519449872e-06,
      "loss": 1.8086307525634766,
      "step": 70
    },
    {
      "epoch": 0.6490872210953347,
      "grad_norm": 12.945286750793457,
      "learning_rate": 1.5432914190872757e-06,
      "loss": 1.7495216369628905,
      "step": 80
    },
    {
      "epoch": 0.7302231237322515,
      "grad_norm": 18.343416213989258,
      "learning_rate": 9.780964274781984e-07,
      "loss": 1.8054061889648438,
      "step": 90
    },
    {
      "epoch": 0.8113590263691683,
      "grad_norm": 41.2520751953125,
      "learning_rate": 5.166166492719124e-07,
      "loss": 1.854791259765625,
      "step": 100
    },
    {
      "epoch": 0.8924949290060852,
      "grad_norm": 61.88145446777344,
      "learning_rate": 1.9030116872178317e-07,
      "loss": 1.733109474182129,
      "step": 110
    },
    {
      "epoch": 0.973630831643002,
      "grad_norm": 19.958646774291992,
      "learning_rate": 2.1387846565474047e-08,
      "loss": 1.8175289154052734,
      "step": 120
    },
    {
      "epoch": 1.0,
      "step": 124,
      "total_flos": 756286140448768.0,
      "train_loss": 2.33440653739437,
      "train_runtime": 12466.658,
      "train_samples_per_second": 0.316,
      "train_steps_per_second": 0.01
    }
  ],
  "logging_steps": 10,
  "max_steps": 124,
  "num_input_tokens_seen": 0,
  "num_train_epochs": 1,
  "save_steps": 500,
  "stateful_callbacks": {
    "TrainerControl": {
      "args": {
        "should_epoch_stop": false,
        "should_evaluate": false,
        "should_log": false,
        "should_save": true,
        "should_training_stop": true
      },
      "attributes": {}
    }
  },
  "total_flos": 756286140448768.0,
  "train_batch_size": 1,
  "trial_name": null,
  "trial_params": null
}