Instructions to use MBZUAI/CoME-VL with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MBZUAI/CoME-VL with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="MBZUAI/CoME-VL")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("MBZUAI/CoME-VL", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use MBZUAI/CoME-VL with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "MBZUAI/CoME-VL" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "MBZUAI/CoME-VL", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/MBZUAI/CoME-VL
- SGLang
How to use MBZUAI/CoME-VL 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 "MBZUAI/CoME-VL" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "MBZUAI/CoME-VL", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'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 "MBZUAI/CoME-VL" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "MBZUAI/CoME-VL", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use MBZUAI/CoME-VL with Docker Model Runner:
docker model run hf.co/MBZUAI/CoME-VL
| run_name: multitask_train | |
| seed: 6198 | |
| epoch: null | |
| dry_run: false | |
| model: | |
| d_model: 3584 | |
| n_heads: 28 | |
| n_kv_heads: 4 | |
| qkv_bias: true | |
| clip_qkv: null | |
| n_layers: 28 | |
| mlp_ratio: 4 | |
| mlp_hidden_size: 37888 | |
| activation_type: swiglu | |
| block_type: sequential | |
| block_group_size: 1 | |
| rope: true | |
| rope_full_precision: true | |
| rope_theta: 1000000.0 | |
| vision_backbone: | |
| image_model_type: siglip | |
| image_default_input_size: | |
| - 384 | |
| - 384 | |
| image_patch_size: 16 | |
| image_pos_patch_size: 16 | |
| image_emb_dim: 1152 | |
| image_num_heads: 16 | |
| image_num_key_value_heads: 16 | |
| image_num_layers: 27 | |
| image_head_dim: 72 | |
| image_mlp_dim: 4304 | |
| image_mlp_activations: gelu_pytorch_tanh | |
| image_dropout_rate: 0.0 | |
| image_num_pos: 576 | |
| image_norm_eps: 1.0e-06 | |
| attention_dropout: 0.0 | |
| residual_dropout: 0.0 | |
| initializer_range: 0.02 | |
| fsdp_wrap: false | |
| resize_mode: siglip | |
| vision_backbone2: | |
| image_model_type: dino | |
| image_default_input_size: | |
| - 224 | |
| - 224 | |
| image_patch_size: 16 | |
| image_pos_patch_size: 16 | |
| image_emb_dim: 1024 | |
| image_num_heads: 16 | |
| image_num_key_value_heads: 16 | |
| image_num_layers: 24 | |
| image_head_dim: 64 | |
| image_mlp_dim: 4096 | |
| image_mlp_activations: gelu | |
| image_dropout_rate: 0.0 | |
| image_num_pos: 785 | |
| image_norm_eps: 1.0e-05 | |
| attention_dropout: 0.0 | |
| residual_dropout: 0.0 | |
| initializer_range: 0.02 | |
| fsdp_wrap: false | |
| resize_mode: dino | |
| vit_load_path: /molmo_code/data/pretrained_image_encoders/siglip2-so400m-16-384.pt | |
| vit_load_path2: /molmo_code/data/molmo/pretrained_image_encoders/dinov3-large-224.pt | |
| llm_load_path: /weka/oe-training-default/mm-olmo/pretrained_llms/qwen2-7b.pt | |
| low_cpu_fsdp: true | |
| attention_type: sdpa | |
| float32_attention: true | |
| attention_dropout: 0.0 | |
| attention_layer_norm: false | |
| residual_dropout: 0.1 | |
| response_residual_dropout: 0.0 | |
| embedding_dropout: 0.0 | |
| layer_norm_type: rms | |
| layer_norm_with_affine: true | |
| layer_norm_eps: 1.0e-06 | |
| attention_layer_norm_with_affine: true | |
| max_sequence_length: 4096 | |
| max_position_embeddings: null | |
| include_bias: false | |
| bias_for_layer_norm: null | |
| scale_logits: false | |
| vocab_size: 152064 | |
| embedding_size: 152064 | |
| additional_vocab_size: 128 | |
| new_embedding_init_range: 0.02 | |
| weight_tying: false | |
| init_device: null | |
| init_fn: normal | |
| init_std: 0.02 | |
| init_cutoff_factor: null | |
| norm_after: false | |
| precision: amp_bf16 | |
| max_crops: 12 | |
| crop_mode: overlap-and-resize-c2 | |
| use_col_tokens: true | |
| prompt_type: uber_model | |
| system_prompt_kind: demo_or_style | |
| message_formatting: role | |
| always_start_with_space: true | |
| multi_annotation_weighting: root_subsegments | |
| default_inference_len: 65 | |
| overlap_margins: | |
| - 4 | |
| - 4 | |
| pad_value: 0.0 | |
| image_padding_embed: pad_and_partial_pad | |
| fix_image_padding: true | |
| vit_layers: | |
| - -1 | |
| vit_layers2: | |
| - -1 | |
| image_pooling_h: 2 | |
| image_pooling_w: 2 | |
| image_pooling_2d: attention_meanq | |
| image_projector: mlp | |
| image_projector2: mlp | |
| image_feature_dropout: 0.0 | |
| initializer_range: 0.02 | |
| normalize_input_embeds: false | |
| use_position_ids: true | |
| head_dim: null | |
| tokenizer: | |
| identifier: Qwen/Qwen2-7B | |
| tokenizer_dir: null | |
| pad_tokenizer: true | |
| moe_num_experts: 8 | |
| moe_top_k: 2 | |
| moe_mlp_impl: sparse | |
| moe_log_expert_assignment: false | |
| moe_shared_expert: false | |
| moe_lbl_in_fp32: false | |
| moe_interleave: false | |
| moe_loss_weight: 0.1 | |
| moe_zloss_weight: null | |
| moe_dropless: true | |
| moe_capacity_factor: 1.25 | |
| allow_resume: true | |
| ft_llm: true | |
| ft_vit: true | |
| ft_vit2: false | |
| ft_connector: true | |
| ft_embedding: lm_head | |
| optimizer: | |
| name: adamw | |
| learning_rate: 0.0001 | |
| weight_decay: 0.01 | |
| betas: | |
| - 0.9 | |
| - 0.95 | |
| eps: 1.0e-05 | |
| connector_learning_rate: 1.0e-05 | |
| vit_learning_rate: 1.0e-05 | |
| llm_learning_rate: 1.0e-05 | |
| connector_weight_decay: 0.0 | |
| vit_weight_decay: 0.0 | |
| llm_weight_decay: 0.0 | |
| connector_betas: | |
| - 0.9 | |
| - 0.95 | |
| vit_betas: | |
| - 0.9 | |
| - 0.95 | |
| llm_betas: | |
| - 0.9 | |
| - 0.95 | |
| connector_eps: 1.0e-06 | |
| vit_eps: 1.0e-06 | |
| llm_eps: 1.0e-06 | |
| metrics_log_interval: 20 | |
| scheduler: | |
| name: multimodal | |
| units: steps | |
| t_warmup: 100 | |
| t_max: null | |
| alpha_f: 0.1 | |
| connector_t_warmup: 200 | |
| vit_t_warmup: 200 | |
| llm_t_warmup: 200 | |
| grad_clip_warmup_steps: null | |
| grad_clip_warmup_factor: null | |
| warmup_min_lr: 0.0 | |
| data: | |
| dataset: null | |
| mixture: null | |
| root_size_mixture: | |
| - rate: 0.6 | |
| mixture: | |
| refcoco: null | |
| adv_refcoco: null | |
| pixmo_docs_charts: null | |
| pixmo_docs_tables: null | |
| pixmo_docs_other: null | |
| pixmo_docs_diagrams: null | |
| - rate: 0.4 | |
| mixture: | |
| pointing_eval: null | |
| pixmo_count_counting: null | |
| pixmo_points: null | |
| pixmo_count: null | |
| pixmo_points_counting: null | |
| split: train | |
| seed: 50189 | |
| shuffle_messages: true | |
| pad: to_max | |
| sequence_length: 2304 | |
| shuffle: true | |
| for_inference: false | |
| multi_modal: torch | |
| num_workers: 2 | |
| drop_last: true | |
| pin_memory: true | |
| prefetch_factor: null | |
| persistent_workers: false | |
| timeout: 0 | |
| restore_dataloader: true | |
| fast_forward_batches: null | |
| evaluators: [] | |
| eval_interval: 12000 | |
| inf_eval_interval: 12000 | |
| inf_evaluators: | |
| - label: pixmo_docs_charts:validation | |
| data: | |
| dataset: pixmo_docs_charts | |
| mixture: null | |
| root_size_mixture: null | |
| split: validation | |
| seed: null | |
| shuffle_messages: true | |
| pad: to_max | |
| sequence_length: 1792 | |
| shuffle: true | |
| for_inference: true | |
| multi_modal: torch | |
| num_workers: 2 | |
| drop_last: true | |
| pin_memory: true | |
| prefetch_factor: null | |
| persistent_workers: true | |
| timeout: 0 | |
| device_eval_batch_size: null | |
| subset_num_batches: null | |
| max_examples: 2048 | |
| max_new_tokens: 256 | |
| mm_evaluator: | |
| n_to_log: 0 | |
| num_wandb_examples: 32 | |
| save_predictions: null | |
| save_tokens: false | |
| save_full_predictions: false | |
| vqa_eval: ansl,em | |
| pointing_eval: false | |
| count_eval: false | |
| point_count_eval: false | |
| android_eval: false | |
| clock_eval: false | |
| clock_bench_eval: false | |
| math_vista_eval: false | |
| save_dir: null | |
| save_to_checkpoint_dir: false | |
| eval_name: null | |
| skip_if_metrics_cached: true | |
| save_folder: /molmo_ckpt/final | |
| remote_save_folder: null | |
| canceled_check_interval: 50 | |
| save_interval: 30000 | |
| save_interval_unsharded: 1000 | |
| save_interval_ephemeral: null | |
| save_num_checkpoints_to_keep: 0 | |
| save_num_unsharded_checkpoints_to_keep: 1 | |
| save_overwrite: true | |
| force_save_unsharded: false | |
| no_pre_train_checkpoint: true | |
| initial_model_checkpoint: /molmo_ckpt/step24000-unsharded | |
| load_model_config: null | |
| load_path: null | |
| load_path_sharded_checkpointer: null | |
| reset_optimizer_state: false | |
| reset_trainer_state: false | |
| save_dataloader_state: false | |
| reset_dataloader_state: false | |
| sharded_checkpointer: torch_legacy | |
| max_duration: 30000 | |
| global_train_batch_size: 24 | |
| device_train_batch_size: 3 | |
| device_train_microbatch_size: 3 | |
| device_eval_batch_size: 3 | |
| eval_subset_num_batches: 1 | |
| eval_on_load: false | |
| device_inf_eval_batch_size: 3 | |
| inf_eval_subset_num_batches: -1 | |
| device_train_grad_accum: 1 | |
| max_grad_norm: 1.0 | |
| multi_component_grad_norm: true | |
| batch_divisor: global_batch | |
| max_grad_norm_ratio: null | |
| precision: amp_bf16 | |
| wandb: | |
| project: molmo-1 | |
| entity: ankanderia2-mbzuai | |
| group: null | |
| name: multitask_train | |
| tags: | |
| - watching | |
| log_artifacts: false | |
| rank_zero_only: true | |
| log_interval: 20 | |
| speed_monitor: | |
| window_size: 20 | |
| gpu_flops_available: null | |
| console_log_interval: 20 | |
| gen1_gc_interval: 1 | |
| compile: null | |
| fsdp: | |
| use_orig_params: true | |
| sharding_strategy: FULL_SHARD | |
| wrapping_strategy: by_block_and_size | |
| precision: float | |
| hybrid_sharding_num_model_replicas: null | |
| softmax_auxiliary_loss: true | |
| softmax_auxiliary_loss_scale: 0.0001 | |
| time_limit: null | |
| extra_steps_after_cancel: 10 | |
| python_profiling: false | |
| torch_profiling: false | |
| stop_at: 30000 | |
| stop_after: null | |
| activation_checkpointing: whole_layer | |
| fused_loss: null | |