Image-Text-to-Text
Safetensors
ColPali
sentence-transformers
sauerkrautlm-colpali
mistral3
document-retrieval
vision-language-model
multi-vector
late-interaction
visual-retrieval
ministral
pixtral
mistral
mteb
vidore
conversational
Instructions to use VAGOsolutions/SauerkrautLM-ColMinistral3-3b-v0.1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- ColPali
How to use VAGOsolutions/SauerkrautLM-ColMinistral3-3b-v0.1 with ColPali:
# No code snippets available yet for this library. # To use this model, check the repository files and the library's documentation. # Want to help? PRs adding snippets are welcome at: # https://github.com/huggingface/huggingface.js
- sentence-transformers
How to use VAGOsolutions/SauerkrautLM-ColMinistral3-3b-v0.1 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("VAGOsolutions/SauerkrautLM-ColMinistral3-3b-v0.1") 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
File size: 1,762 Bytes
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"architectures": [
"ColMinistral3"
],
"dtype": "bfloat16",
"image_token_index": 10,
"model_type": "mistral3",
"multimodal_projector_bias": false,
"projector_hidden_act": "gelu",
"spatial_merge_size": 2,
"text_config": {
"attention_dropout": 0.0,
"dtype": "bfloat16",
"head_dim": 128,
"hidden_act": "silu",
"hidden_size": 3072,
"initializer_range": 0.02,
"intermediate_size": 9216,
"max_position_embeddings": 262144,
"model_type": "ministral3",
"num_attention_heads": 32,
"num_hidden_layers": 26,
"num_key_value_heads": 8,
"rms_norm_eps": 1e-05,
"rope_parameters": {
"beta_fast": 32.0,
"beta_slow": 1.0,
"factor": 16.0,
"llama_4_scaling_beta": 0.1,
"mscale": 1.0,
"mscale_all_dim": 1.0,
"original_max_position_embeddings": 16384,
"rope_theta": 1000000.0,
"rope_type": "yarn",
"type": "yarn"
},
"sliding_window": null,
"tie_word_embeddings": true,
"use_cache": true,
"vocab_size": 131072
},
"transformers_version": "5.0.0rc0",
"vision_config": {
"attention_dropout": 0.0,
"dtype": "bfloat16",
"head_dim": 64,
"hidden_act": "silu",
"hidden_size": 1024,
"image_size": 1540,
"initializer_range": 0.02,
"intermediate_size": 4096,
"model_type": "pixtral",
"num_attention_heads": 16,
"num_channels": 3,
"num_hidden_layers": 24,
"patch_size": 14,
"rope_parameters": {
"rope_theta": 10000.0,
"rope_type": "default"
}
},
"vision_feature_layer": -1,
"base_model": "mistralai/Ministral-3-3B-Reasoning-2512",
"dim": 128,
"mask_non_image_embeddings": false,
"_name_or_path": "VAGOsolutions/SauerkrautLM-ColMinistral3-3b-v0.1"
} |