Instructions to use NYUAD-ComNets/Gemma4_meme_classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NYUAD-ComNets/Gemma4_meme_classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="NYUAD-ComNets/Gemma4_meme_classification") 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("NYUAD-ComNets/Gemma4_meme_classification") model = AutoModelForMultimodalLM.from_pretrained("NYUAD-ComNets/Gemma4_meme_classification", 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 NYUAD-ComNets/Gemma4_meme_classification with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "NYUAD-ComNets/Gemma4_meme_classification" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "NYUAD-ComNets/Gemma4_meme_classification", "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/NYUAD-ComNets/Gemma4_meme_classification
- SGLang
How to use NYUAD-ComNets/Gemma4_meme_classification 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 "NYUAD-ComNets/Gemma4_meme_classification" \ --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": "NYUAD-ComNets/Gemma4_meme_classification", "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 "NYUAD-ComNets/Gemma4_meme_classification" \ --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": "NYUAD-ComNets/Gemma4_meme_classification", "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 NYUAD-ComNets/Gemma4_meme_classification with Docker Model Runner:
docker model run hf.co/NYUAD-ComNets/Gemma4_meme_classification
Upload model trained with Unsloth
Browse filesUpload model trained with Unsloth 2x faster
- adapter_config.json +44 -0
- adapter_model.safetensors +3 -0
adapter_config.json
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{
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"alora_invocation_tokens": null,
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"alpha_pattern": {},
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"arrow_config": null,
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"auto_mapping": {
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"base_model_class": "Gemma4ForConditionalGeneration",
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"parent_library": "transformers.models.gemma4.modeling_gemma4",
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"unsloth_fixed": true
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},
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"base_model_name_or_path": "unsloth/gemma-4-e4b-it-unsloth-bnb-4bit",
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"bias": "none",
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"corda_config": null,
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"ensure_weight_tying": false,
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"eva_config": null,
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"exclude_modules": null,
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"fan_in_fan_out": false,
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"inference_mode": true,
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"init_lora_weights": true,
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"layer_replication": null,
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"layers_pattern": null,
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"layers_to_transform": null,
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"loftq_config": {},
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"lora_alpha": 32,
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"lora_bias": false,
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"lora_dropout": 0,
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"lora_ga_config": null,
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"megatron_config": null,
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"megatron_core": "megatron.core",
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"modules_to_save": null,
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"peft_type": "LORA",
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"peft_version": "0.19.1",
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"qalora_group_size": 16,
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"r": 32,
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"rank_pattern": {},
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"revision": null,
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"target_modules": "(?:(?:.*?(?:vision|image|visual|patch|language|text).*?(?:self_attn|attention|attn|mixer|mlp|feed_forward|ffn|dense|mixer).*?(?:k_proj|q_proj|v_proj|o_proj|gate_proj|up_proj|down_proj|per_layer_input_gate|per_layer_projection|linear|embedding_projection|relative_k_proj))|(?:\\bmodel\\.layers\\.[\\d]{1,}\\.(?:self_attn|attention|attn|mixer|mlp|feed_forward|ffn|dense|mixer)\\.(?:(?:k_proj|q_proj|v_proj|o_proj|gate_proj|up_proj|down_proj|per_layer_input_gate|per_layer_projection|linear|embedding_projection|relative_k_proj))))|(?:.*?\\baudio_tower\\.(?:.*\\.)?q_proj\\.linear)|(?:.*?\\baudio_tower\\.(?:.*\\.)?k_proj\\.linear)|(?:.*?\\baudio_tower\\.(?:.*\\.)?v_proj\\.linear)|(?:.*?\\baudio_tower\\.(?:.*\\.)?relative_k_proj)|(?:.*?\\baudio_tower\\.(?:.*\\.)?post\\.linear)|(?:.*?\\baudio_tower\\.(?:.*\\.)?ffw_layer_1\\.linear)|(?:.*?\\baudio_tower\\.(?:.*\\.)?ffw_layer_2\\.linear)|(?:.*?\\baudio_tower\\.(?:.*\\.)?linear_start\\.linear)|(?:.*?\\baudio_tower\\.(?:.*\\.)?linear_end\\.linear)|(?:.*?\\baudio_tower\\.(?:.*\\.)?input_proj_linear)|(?:.*?\\baudio_tower\\.(?:.*\\.)?output_proj)|(?:.*?\\bembed_audio\\.embedding_projection)|(?:.*?\\bembed_vision\\.embedding_projection)|(?:.*?\\bvision_tower\\.patch_embedder\\.input_proj)",
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"target_parameters": null,
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"task_type": "CAUSAL_LM",
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"trainable_token_indices": null,
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"use_bdlora": null,
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"use_dora": false,
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"use_qalora": false,
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"use_rslora": false
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}
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adapter_model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:6ac863f91df9bcc67bec3a740b49efd8fdf93c8e6387fc7d572ab788887d7783
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size 386731424
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