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
File size: 2,335 Bytes
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"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"
}
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