Instructions to use mtgv/MobileVLM-3B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mtgv/MobileVLM-3B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="mtgv/MobileVLM-3B")# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("mtgv/MobileVLM-3B", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use mtgv/MobileVLM-3B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "mtgv/MobileVLM-3B" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "mtgv/MobileVLM-3B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/mtgv/MobileVLM-3B
- SGLang
How to use mtgv/MobileVLM-3B 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 "mtgv/MobileVLM-3B" \ --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": "mtgv/MobileVLM-3B", "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 "mtgv/MobileVLM-3B" \ --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": "mtgv/MobileVLM-3B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use mtgv/MobileVLM-3B with Docker Model Runner:
docker model run hf.co/mtgv/MobileVLM-3B
| { | |
| "_mlp_class": "LLaMAMLP", | |
| "_name_or_path": "mtgv/MobileVLM-3B", | |
| "_norm_class": "RMSNorm", | |
| "architectures": [ | |
| "MobileLlamaForCausalLM" | |
| ], | |
| "bias": false, | |
| "block_size": 2048, | |
| "bos_token_id": 1, | |
| "eos_token_id": 2, | |
| "freeze_mm_mlp_adapter": false, | |
| "gelu_approximate": "none", | |
| "hidden_act": "silu", | |
| "hidden_size": 2560, | |
| "image_aspect_ratio": "pad", | |
| "image_grid_pinpoints": null, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 6912, | |
| "lm_head_bias": false, | |
| "max_position_embeddings": 2048, | |
| "mm_hidden_size": 1024, | |
| "mm_projector_type": "ldpnet", | |
| "mm_use_im_patch_token": false, | |
| "mm_use_im_start_end": false, | |
| "mm_vision_select_feature": "patch", | |
| "mm_vision_select_layer": -2, | |
| "mm_vision_tower": "openai/clip-vit-large-patch14-336", | |
| "model_type": "mobilevlm", | |
| "n_embd": 2560, | |
| "n_head": 16, | |
| "n_layer": 32, | |
| "n_query_groups": 16, | |
| "name": "huggy_llama_3b", | |
| "norm_eps": 1e-06, | |
| "num_attention_heads": 32, | |
| "num_hidden_layers": 32, | |
| "num_key_value_heads": 32, | |
| "pad_token_id": 0, | |
| "padded_vocab_size": 32000, | |
| "padding_multiple": 64, | |
| "parallel_residual": false, | |
| "pretraining_tp": 1, | |
| "rms_norm_eps": 1e-06, | |
| "rope_base": 10000, | |
| "rope_condense_ratio": 1, | |
| "rope_scaling": null, | |
| "rope_theta": 10000.0, | |
| "rotary_percentage": 1.0, | |
| "shared_attention_norm": false, | |
| "tie_word_embeddings": false, | |
| "torch_dtype": "bfloat16", | |
| "transformers_version": "4.33.1", | |
| "tune_mm_mlp_adapter": false, | |
| "use_cache": true, | |
| "use_mm_proj": true, | |
| "vision_tower_type": "clip", | |
| "vocab_size": 32000 | |
| } | |