Instructions to use naver-hyperclovax/HyperCLOVAX-SEED-Think-32B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use naver-hyperclovax/HyperCLOVAX-SEED-Think-32B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="naver-hyperclovax/HyperCLOVAX-SEED-Think-32B", trust_remote_code=True) messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("naver-hyperclovax/HyperCLOVAX-SEED-Think-32B", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use naver-hyperclovax/HyperCLOVAX-SEED-Think-32B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "naver-hyperclovax/HyperCLOVAX-SEED-Think-32B" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "naver-hyperclovax/HyperCLOVAX-SEED-Think-32B", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/naver-hyperclovax/HyperCLOVAX-SEED-Think-32B
- SGLang
How to use naver-hyperclovax/HyperCLOVAX-SEED-Think-32B 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 "naver-hyperclovax/HyperCLOVAX-SEED-Think-32B" \ --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": "naver-hyperclovax/HyperCLOVAX-SEED-Think-32B", "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 "naver-hyperclovax/HyperCLOVAX-SEED-Think-32B" \ --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": "naver-hyperclovax/HyperCLOVAX-SEED-Think-32B", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use naver-hyperclovax/HyperCLOVAX-SEED-Think-32B with Docker Model Runner:
docker model run hf.co/naver-hyperclovax/HyperCLOVAX-SEED-Think-32B
| { | |
| "anyres": false, | |
| "architectures": [ | |
| "HyperCLOVAXVisionV2ForConditionalGeneration" | |
| ], | |
| "eos_token_id": 128001, | |
| "hidden_size": 5120, | |
| "ignore_index": -100, | |
| "image_end_token_id": 128057, | |
| "image_start_token_id": 128056, | |
| "image_token_id": 128060, | |
| "max_num_grids": -1, | |
| "audio_projector_type": "mlp", | |
| "vision_projector_type": "linear", | |
| "model_type": "hyperclovax_vision_v2", | |
| "num_queries_vis_abstractor": -1, | |
| "pad_token_id": 0, | |
| "possible_resolutions": [], | |
| "proj_pos_emb": true, | |
| "proj_prenorm": false, | |
| "text_config": { | |
| "architectures": [ | |
| "HyperCLOVAXForCausalLM" | |
| ], | |
| "attention_bias": false, | |
| "attention_dropout": 0.0, | |
| "attention_multiplier": 0.08838834764831845, | |
| "bos_token_id": 128000, | |
| "embedding_multiplier": 1.0, | |
| "eos_token_id": 128001, | |
| "head_dim": 128, | |
| "hidden_act": "silu", | |
| "hidden_size": 5120, | |
| "initializer_range": 0.006, | |
| "intermediate_size": 24192, | |
| "logits_scaling": 1.0, | |
| "max_position_embeddings": 131072, | |
| "mlp_bias": false, | |
| "model_type": "hyperclovax", | |
| "num_attention_heads": 40, | |
| "num_hidden_layers": 72, | |
| "num_key_value_heads": 8, | |
| "pad_token_id": 0, | |
| "pretraining_tp": 1, | |
| "resid_pdrop": 0.2, | |
| "residual_multiplier": 1.0, | |
| "rms_norm_eps": 1e-05, | |
| "rope_scaling": null, | |
| "rope_theta": 50000000, | |
| "tie_word_embeddings": false, | |
| "use_cache": true, | |
| "use_post_norm": false, | |
| "vocab_size": 128256 | |
| }, | |
| "torch_dtype": "bfloat16", | |
| "transformers_version": "4.58.0", | |
| "unpad": false, | |
| "use_1x1_grid": false, | |
| "video_first_last_frames_slows": null, | |
| "video_max_num_frames": null, | |
| "video_num_queries_fast": null, | |
| "video_num_queries_slow": null, | |
| "video_end_token_id": 128059, | |
| "video_start_token_id": 128058, | |
| "video_token_id": 128061, | |
| "vision_config": { | |
| "anyres": false, | |
| "architectures": [ | |
| "Qwen2_5_VisionTransformerPretrainedModel" | |
| ], | |
| "depth": 32, | |
| "fullatt_block_indexes": [ | |
| 7, | |
| 15, | |
| 23, | |
| 31 | |
| ], | |
| "hidden_act": "silu", | |
| "hidden_size": 1280, | |
| "in_channels": 3, | |
| "in_chans": 3, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 3456, | |
| "max_num_grids": -1, | |
| "model_type": "qwen2_5_vl_vision", | |
| "num_heads": 16, | |
| "out_hidden_size": 5120, | |
| "patch_size": 14, | |
| "spatial_merge_size": 2, | |
| "spatial_patch_size": 14, | |
| "temporal_patch_size": 2, | |
| "tokens_per_second": 2, | |
| "window_size": 112 | |
| }, | |
| "vision_feature_layer": -2, | |
| "vision_input_chunk_size": null | |
| } | |