Instructions to use anasmkh/molmo2_quantized with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use anasmkh/molmo2_quantized with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="anasmkh/molmo2_quantized", trust_remote_code=True)# Load model directly from transformers import AutoModelForImageTextToText model = AutoModelForImageTextToText.from_pretrained("anasmkh/molmo2_quantized", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use anasmkh/molmo2_quantized with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "anasmkh/molmo2_quantized" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "anasmkh/molmo2_quantized", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/anasmkh/molmo2_quantized
- SGLang
How to use anasmkh/molmo2_quantized 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 "anasmkh/molmo2_quantized" \ --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": "anasmkh/molmo2_quantized", "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 "anasmkh/molmo2_quantized" \ --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": "anasmkh/molmo2_quantized", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use anasmkh/molmo2_quantized with Docker Model Runner:
docker model run hf.co/anasmkh/molmo2_quantized
File size: 3,182 Bytes
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"adapter_config": {
"attention_dropout": 0.0,
"attn_implementation": "sdpa",
"dtype": "float16",
"float32_attention": true,
"head_dim": 72,
"hidden_act": "silu",
"hidden_size": 1152,
"image_feature_dropout": 0.0,
"initializer_range": 0.02,
"intermediate_size": 9728,
"model_type": "molmo2",
"num_attention_heads": 16,
"num_key_value_heads": 16,
"pooling_attention_mask": true,
"residual_dropout": 0.0,
"text_hidden_size": 2560,
"vit_layers": [
-3,
-9
]
},
"architectures": [
"Molmo2ForConditionalGeneration"
],
"auto_map": {
"AutoConfig": "configuration_molmo2.Molmo2Config",
"AutoModelForImageTextToText": "modeling_molmo2.Molmo2ForConditionalGeneration"
},
"dtype": "float16",
"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_config": {
"_load_in_4bit": true,
"_load_in_8bit": false,
"bnb_4bit_compute_dtype": "float16",
"bnb_4bit_quant_storage": "uint8",
"bnb_4bit_quant_type": "nf4",
"bnb_4bit_use_double_quant": true,
"llm_int8_enable_fp32_cpu_offload": false,
"llm_int8_has_fp16_weight": false,
"llm_int8_skip_modules": [
"lm_head",
"embed_tokens",
"vision_backbone",
"image_projector"
],
"llm_int8_threshold": 6.0,
"load_in_4bit": true,
"load_in_8bit": false,
"quant_method": "bitsandbytes"
},
"text_config": {
"additional_vocab_size": 128,
"attention_dropout": 0.0,
"attn_implementation": "sdpa",
"dtype": "float16",
"embedding_dropout": 0.0,
"head_dim": 128,
"hidden_act": "silu",
"hidden_size": 2560,
"initializer_range": 0.02,
"intermediate_size": 9728,
"layer_norm_eps": 1e-06,
"max_position_embeddings": 36864,
"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": 5000000.0,
"use_cache": true,
"use_qk_norm": true,
"vocab_size": 151936
},
"tie_word_embeddings": false,
"transformers_version": "4.57.1",
"use_cache": true,
"use_frame_special_tokens": false,
"vit_config": {
"attention_dropout": 0.0,
"attn_implementation": "sdpa",
"dtype": "float16",
"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
}
}
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