Zero-Shot Image Classification
Transformers
Safetensors
sentence-transformers
mllama
image-text-to-text
mmeb
text-generation-inference
Instructions to use intfloat/mmE5-mllama-11b-instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use intfloat/mmE5-mllama-11b-instruct with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-image-classification", model="intfloat/mmE5-mllama-11b-instruct") pipe( "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png", candidate_labels=["animals", "humans", "landscape"], )# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("intfloat/mmE5-mllama-11b-instruct") model = AutoModelForMultimodalLM.from_pretrained("intfloat/mmE5-mllama-11b-instruct", device_map="auto") - sentence-transformers
How to use intfloat/mmE5-mllama-11b-instruct with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("intfloat/mmE5-mllama-11b-instruct") sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - Notebooks
- Google Colab
- Kaggle
Download lora/adapter_config.json from intfloat/mmE5-mllama-11b-instruct: direct link, hf CLI and curl.
- Browser
- Download file 876 Bytes
-
https://huggingface.co/intfloat/mmE5-mllama-11b-instruct/resolve/main/lora/adapter_config.json
- Command line
-
hf download hf://intfloat/mmE5-mllama-11b-instruct/lora/adapter_config.json
-
curl -L -o adapter_config.json https://huggingface.co/intfloat/mmE5-mllama-11b-instruct/resolve/main/lora/adapter_config.json
876 Bytes
| { | |
| "alpha_pattern": {}, | |
| "auto_mapping": { | |
| "base_model_class": "MllamaForConditionalGeneration", | |
| "parent_library": "transformers.models.mllama.modeling_mllama" | |
| }, | |
| "base_model_name_or_path": "meta-llama/Llama-3.2-11B-Vision", | |
| "bias": "none", | |
| "fan_in_fan_out": false, | |
| "inference_mode": true, | |
| "init_lora_weights": "gaussian", | |
| "layer_replication": null, | |
| "layers_pattern": null, | |
| "layers_to_transform": null, | |
| "loftq_config": {}, | |
| "lora_alpha": 64, | |
| "lora_dropout": 0.1, | |
| "megatron_config": null, | |
| "megatron_core": "megatron.core", | |
| "modules_to_save": null, | |
| "peft_type": "LORA", | |
| "r": 8, | |
| "rank_pattern": {}, | |
| "revision": null, | |
| "target_modules": [ | |
| "out_proj", | |
| "gate_up_proj", | |
| "down_proj", | |
| "q_proj", | |
| "k_proj", | |
| "v_proj", | |
| "qkv_proj", | |
| "o_proj" | |
| ], | |
| "task_type": null, | |
| "use_dora": true, | |
| "use_rslora": false | |
| } |