Instructions to use ravilution/MolmoWeb-8B-8bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use ravilution/MolmoWeb-8B-8bit with MLX:
# Make sure mlx-vlm is installed # pip install --upgrade mlx-vlm from mlx_vlm import load, generate from mlx_vlm.prompt_utils import apply_chat_template from mlx_vlm.utils import load_config # Load the model model, processor = load("ravilution/MolmoWeb-8B-8bit") config = load_config("ravilution/MolmoWeb-8B-8bit") # Prepare input image = ["http://images.cocodataset.org/val2017/000000039769.jpg"] prompt = "Describe this image." # Apply chat template formatted_prompt = apply_chat_template( processor, config, prompt, num_images=1 ) # Generate output output = generate(model, processor, formatted_prompt, image) print(output) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
File size: 2,814 Bytes
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"adapter_config": {
"attention_dropout": 0.0,
"attn_implementation": "sdpa",
"float32_attention": true,
"head_dim": 72,
"hidden_act": "silu",
"hidden_size": 1152,
"image_feature_dropout": 0.0,
"initializer_range": 0.02,
"intermediate_size": 12288,
"model_type": "molmo2",
"num_attention_heads": 16,
"num_key_value_heads": 16,
"pooling_attention_mask": true,
"residual_dropout": 0.0,
"text_hidden_size": 4096,
"vit_layers": [
-3,
-9
]
},
"architectures": [
"Molmo2ForConditionalGeneration"
],
"auto_map": {
"AutoConfig": "configuration_molmo2.Molmo2Config",
"AutoModelForImageTextToText": "modeling_molmo2.Molmo2ForConditionalGeneration"
},
"dtype": "float32",
"eos_token_id": 151645,
"frame_end_token_id": 151944,
"frame_start_token_id": 151943,
"image_col_id": 151939,
"image_end_token_id": 151937,
"image_high_res_id": 151938,
"image_low_res_id": 151942,
"image_patch_id": 151938,
"image_start_token_id": 151936,
"initializer_range": 0.02,
"low_res_image_start_token_id": 151940,
"model_type": "molmo2",
"quantization": {
"group_size": 64,
"bits": 8,
"mode": "affine"
},
"quantization_config": {
"group_size": 64,
"bits": 8,
"mode": "affine"
},
"text_config": {
"additional_vocab_size": 128,
"attention_dropout": 0.0,
"attn_implementation": "sdpa",
"embedding_dropout": 0.0,
"head_dim": 128,
"hidden_act": "silu",
"hidden_size": 4096,
"initializer_range": 0.02,
"intermediate_size": 12288,
"layer_norm_eps": 1e-06,
"max_position_embeddings": 10240,
"model_type": "molmo2_text",
"norm_after": false,
"num_attention_heads": 32,
"num_hidden_layers": 36,
"num_key_value_heads": 8,
"qk_norm_type": "qwen3",
"qkv_bias": false,
"residual_dropout": 0.0,
"rope_scaling": null,
"rope_scaling_layers": null,
"rope_theta": 1000000.0,
"use_cache": true,
"use_qk_norm": true,
"vocab_size": 151936
},
"tie_word_embeddings": false,
"transformers_version": "4.47.0",
"use_cache": true,
"use_frame_special_tokens": false,
"vision_config": {},
"vit_config": {
"attention_dropout": 0.0,
"attn_implementation": "sdpa",
"float32_attention": true,
"head_dim": 72,
"hidden_act": "gelu_pytorch_tanh",
"hidden_size": 1152,
"image_default_input_size": [
378,
378
],
"image_num_pos": 729,
"image_patch_size": 14,
"initializer_range": 0.02,
"intermediate_size": 4304,
"layer_norm_eps": 1e-06,
"model_type": "molmo2",
"num_attention_heads": 16,
"num_hidden_layers": 27,
"num_key_value_heads": 16,
"residual_dropout": 0.0
},
"bos_token_id": 151645,
"pad_token_id": 151643
} |