Image-Text-to-Text
Transformers
Safetensors
English
gemma4
text-generation-inference
unsloth
conversational
Instructions to use ertghiu256/Gemma4-e2b-qwen-glm-distill-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ertghiu256/Gemma4-e2b-qwen-glm-distill-lora with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="ertghiu256/Gemma4-e2b-qwen-glm-distill-lora") 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("ertghiu256/Gemma4-e2b-qwen-glm-distill-lora") model = AutoModelForMultimodalLM.from_pretrained("ertghiu256/Gemma4-e2b-qwen-glm-distill-lora", device_map="auto") 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 ertghiu256/Gemma4-e2b-qwen-glm-distill-lora with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "ertghiu256/Gemma4-e2b-qwen-glm-distill-lora" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "ertghiu256/Gemma4-e2b-qwen-glm-distill-lora", "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/ertghiu256/Gemma4-e2b-qwen-glm-distill-lora
- SGLang
How to use ertghiu256/Gemma4-e2b-qwen-glm-distill-lora 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 "ertghiu256/Gemma4-e2b-qwen-glm-distill-lora" \ --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": "ertghiu256/Gemma4-e2b-qwen-glm-distill-lora", "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 "ertghiu256/Gemma4-e2b-qwen-glm-distill-lora" \ --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": "ertghiu256/Gemma4-e2b-qwen-glm-distill-lora", "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" } } ] } ] }' - Unsloth Desktop
- Docker Model Runner
How to use ertghiu256/Gemma4-e2b-qwen-glm-distill-lora with Docker Model Runner:
docker model run hf.co/ertghiu256/Gemma4-e2b-qwen-glm-distill-lora
| {{ bos_token }}{%- macro strip_thinking(text) -%} | |
| {%- set ns = namespace(result='') -%} | |
| {%- for part in text.split('<channel|>') -%} | |
| {%- if '<|channel>' in part -%} | |
| {%- set ns.result = ns.result + part.split('<|channel>')[0] -%} | |
| {%- else -%} | |
| {%- set ns.result = ns.result + part -%} | |
| {%- endif -%} | |
| {%- endfor -%} | |
| {{- ns.result | trim -}} | |
| {%- endmacro -%} | |
| {%- set thinking = enable_thinking is defined and enable_thinking -%} | |
| {%- set loop_messages = messages -%} | |
| {%- if messages[0]['role'] in ['system', 'developer'] or thinking -%} | |
| {{ '<|turn>system | |
| ' }} | |
| {%- if thinking -%} | |
| {{ '<|think|> | |
| ' }} | |
| {%- endif -%} | |
| {%- if messages[0]['role'] in ['system', 'developer'] -%} | |
| {{ messages[0]['content'] | trim }} | |
| {%- set loop_messages = messages[1:] -%} | |
| {%- endif -%} | |
| {{ '<turn|> | |
| ' }} | |
| {%- endif -%} | |
| {%- for message in loop_messages -%} | |
| {%- if (message['role'] == 'user') != (loop.index0 % 2 == 0) -%} | |
| {{ raise_exception("Conversation roles must alternate user/assistant/user/assistant/...") }} | |
| {%- endif -%} | |
| {%- if (message['role'] == 'assistant') -%} | |
| {%- set role = "model" -%} | |
| {%- else -%} | |
| {%- set role = message['role'] -%} | |
| {%- endif -%} | |
| {{ '<|turn>' + role + ' | |
| ' }} | |
| {%- if message['content'] is string -%} | |
| {%- if role == "model" -%} | |
| {{ strip_thinking(message['content']) }} | |
| {%- else -%} | |
| {{ message['content'] | trim }} | |
| {%- endif -%} | |
| {%- elif message['content'] is iterable -%} | |
| {%- for item in message['content'] -%} | |
| {%- if item['type'] == 'audio' -%} | |
| {{ '<|audio|>' }} | |
| {%- elif item['type'] == 'image' -%} | |
| {{ '<|image|>' }} | |
| {%- elif item['type'] == 'video' -%} | |
| {{ '<|video|>' }} | |
| {%- elif item['type'] == 'text' -%} | |
| {%- if role == "model" -%} | |
| {{ strip_thinking(item['text']) }} | |
| {%- else -%} | |
| {{ item['text'] | trim }} | |
| {%- endif -%} | |
| {%- endif -%} | |
| {%- endfor -%} | |
| {%- else -%} | |
| {{ raise_exception("Invalid content type") }} | |
| {%- endif -%} | |
| {{ '<turn|> | |
| ' }} | |
| {%- endfor -%} | |
| {%- if add_generation_prompt -%} | |
| {{'<|turn>model | |
| '}} | |
| {%- endif -%} | |