Text Generation
Transformers
Safetensors
eva01
3d
mesh
multimodal
qwen3-vl
pointllm-200
conversational
Instructions to use SEELE-AI/EVA01-2B-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SEELE-AI/EVA01-2B-Instruct with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="SEELE-AI/EVA01-2B-Instruct") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("SEELE-AI/EVA01-2B-Instruct", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use SEELE-AI/EVA01-2B-Instruct with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "SEELE-AI/EVA01-2B-Instruct" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "SEELE-AI/EVA01-2B-Instruct", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/SEELE-AI/EVA01-2B-Instruct
- SGLang
How to use SEELE-AI/EVA01-2B-Instruct 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 "SEELE-AI/EVA01-2B-Instruct" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "SEELE-AI/EVA01-2B-Instruct", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'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 "SEELE-AI/EVA01-2B-Instruct" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "SEELE-AI/EVA01-2B-Instruct", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use SEELE-AI/EVA01-2B-Instruct with Docker Model Runner:
docker model run hf.co/SEELE-AI/EVA01-2B-Instruct
| { | |
| "base_model_name_or_path": null, | |
| "is_lora": false, | |
| "mesh_und_connector_dropout": 0.0, | |
| "mesh_und_connector_file": "mesh_und_connector.safetensors", | |
| "mesh_und_connector_hidden_dims": [ | |
| 1024, | |
| 2048 | |
| ], | |
| "mesh_und_encoder_file": "mesh_und_encoder.safetensors", | |
| "mesh_und_encoder_type": "frozen_mesh_und_transformer", | |
| "mesh_und_pointnum": 8192, | |
| "mesh_und_token": "<|mesh_und_pad|>", | |
| "mesh_und_token_id": 151684, | |
| "mesh_und_token_len": 513, | |
| "mesh_und_use_color": true, | |
| "model_type": "eva01", | |
| "qwen3vl_config": { | |
| "architectures": [ | |
| "Qwen3VLForConditionalGeneration" | |
| ], | |
| "dtype": "bfloat16", | |
| "image_token_id": 151655, | |
| "model_type": "qwen3_vl", | |
| "text_config": { | |
| "attention_bias": false, | |
| "attention_dropout": 0.0, | |
| "bos_token_id": 151643, | |
| "dtype": "bfloat16", | |
| "eos_token_id": 151645, | |
| "head_dim": 128, | |
| "hidden_act": "silu", | |
| "hidden_size": 2048, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 6144, | |
| "max_position_embeddings": 262144, | |
| "model_type": "qwen3_vl_text", | |
| "num_attention_heads": 16, | |
| "num_hidden_layers": 28, | |
| "num_key_value_heads": 8, | |
| "rms_norm_eps": 1e-06, | |
| "rope_scaling": { | |
| "mrope_interleaved": true, | |
| "mrope_section": [ | |
| 24, | |
| 20, | |
| 20 | |
| ], | |
| "rope_type": "default" | |
| }, | |
| "rope_theta": 5000000, | |
| "tie_word_embeddings": true, | |
| "use_cache": true, | |
| "vocab_size": 151936 | |
| }, | |
| "tie_word_embeddings": true, | |
| "transformers_version": "4.57.0", | |
| "use_cache": false, | |
| "video_token_id": 151656, | |
| "vision_config": { | |
| "deepstack_visual_indexes": [ | |
| 5, | |
| 11, | |
| 17 | |
| ], | |
| "depth": 24, | |
| "dtype": "bfloat16", | |
| "hidden_act": "gelu_pytorch_tanh", | |
| "hidden_size": 1024, | |
| "in_channels": 3, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 4096, | |
| "model_type": "qwen3_vl", | |
| "num_heads": 16, | |
| "num_position_embeddings": 2304, | |
| "out_hidden_size": 2048, | |
| "patch_size": 16, | |
| "spatial_merge_size": 2, | |
| "temporal_patch_size": 2 | |
| }, | |
| "vision_end_token_id": 151653, | |
| "vision_start_token_id": 151652 | |
| }, | |
| "training_recipe": "alignment_then_instruction_tuning", | |
| "transformers_version": "5.8.1" | |
| } | |