Instructions to use kontextdev/agent-gemma with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use kontextdev/agent-gemma with Transformers:

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

pipe = pipeline("image-text-to-text", model="kontextdev/agent-gemma")
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("kontextdev/agent-gemma")
model = AutoModelForImageTextToText.from_pretrained("kontextdev/agent-gemma")
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 kontextdev/agent-gemma with vLLM:

Install from pip and serve model

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

SGLang

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

macmacmacmac commited on Mar 23

Commit

b906d26

verified ·

1 Parent(s): 4174976

Upload tokenizer_config.json with huggingface_hub

Browse files

Files changed (1) hide show

tokenizer_config.json +3 -3

tokenizer_config.json CHANGED Viewed

@@ -9,7 +9,6 @@
   "eoi_token": "<end_of_image>",
   "eos_token": "<eos>",
   "image_token": "<image_soft_token>",
-  "is_local": true,
   "mask_token": "<mask>",
   "model_max_length": 1000000000000000019884624838656,
   "model_specific_special_tokens": {
@@ -26,5 +25,6 @@
   "spaces_between_special_tokens": false,
   "tokenizer_class": "GemmaTokenizer",
   "unk_token": "<unk>",
-  "use_default_system_prompt": false
-}

   "eoi_token": "<end_of_image>",
   "eos_token": "<eos>",
   "image_token": "<image_soft_token>",
   "mask_token": "<mask>",
   "model_max_length": 1000000000000000019884624838656,
   "model_specific_special_tokens": {
   "spaces_between_special_tokens": false,
   "tokenizer_class": "GemmaTokenizer",
   "unk_token": "<unk>",
+  "use_default_system_prompt": false,
+  "chat_template": "{%- for message in messages -%}{%- if message.role == 'developer' or message.role == 'system' -%}<start_of_turn>developer\n{{ message.content }}{%- if tools is defined and tools|length > 0 %}\n\nAvailable tools:{%- for tool in tools %}\n<start_function_declaration>{%- if tool.function is defined %}{{ tool.function | tojson }}{%- else %}{{ tool | tojson }}{%- endif %}<end_function_declaration>{%- endfor %}{%- endif %}<end_of_turn>\n{%- elif message.role == 'user' -%}<start_of_turn>user\n{{ message.content }}<end_of_turn>\n{%- elif message.role == 'model' or message.role == 'assistant' -%}<start_of_turn>model\n{%- if message.tool_calls is defined and message.tool_calls -%}{%- for tc in message.tool_calls -%}<start_function_call>call:{{ tc.function.name }}{{ '{' }}{%- for k, v in tc.function.arguments.items() -%}{{ k }}:<escape>{{ v }}<escape>{%- if not loop.last %},{% endif -%}{%- endfor -%}{{ '}' }}<end_function_call>{%- endfor -%}{%- else -%}{{ message.content }}{%- endif -%}<end_of_turn>\n{%- elif message.role == 'tool' -%}<start_of_turn>tool\n{{ message.content }}<end_of_turn>\n{%- endif -%}{%- endfor -%}{%- if add_generation_prompt -%}<start_of_turn>model\n{%- endif -%}"
+}