Text Generation
Transformers
Safetensors
ncp_olmo3
bfloat16
causal-lm
conceptlm
olmo3
research
custom_code
Instructions to use ArchSpace-Collection/NCP_Olmo3_Stage1_Step1200000 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ArchSpace-Collection/NCP_Olmo3_Stage1_Step1200000 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="ArchSpace-Collection/NCP_Olmo3_Stage1_Step1200000", trust_remote_code=True)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("ArchSpace-Collection/NCP_Olmo3_Stage1_Step1200000", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use ArchSpace-Collection/NCP_Olmo3_Stage1_Step1200000 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "ArchSpace-Collection/NCP_Olmo3_Stage1_Step1200000" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "ArchSpace-Collection/NCP_Olmo3_Stage1_Step1200000", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/ArchSpace-Collection/NCP_Olmo3_Stage1_Step1200000
- SGLang
How to use ArchSpace-Collection/NCP_Olmo3_Stage1_Step1200000 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 "ArchSpace-Collection/NCP_Olmo3_Stage1_Step1200000" \ --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": "ArchSpace-Collection/NCP_Olmo3_Stage1_Step1200000", "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 "ArchSpace-Collection/NCP_Olmo3_Stage1_Step1200000" \ --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": "ArchSpace-Collection/NCP_Olmo3_Stage1_Step1200000", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use ArchSpace-Collection/NCP_Olmo3_Stage1_Step1200000 with Docker Model Runner:
docker model run hf.co/ArchSpace-Collection/NCP_Olmo3_Stage1_Step1200000
| { | |
| "add_bias_linear": false, | |
| "add_qkv_bias": false, | |
| "apply_residual_connection_post_layernorm": false, | |
| "architectures": [ | |
| "NCPOlmo3ForCausalLM" | |
| ], | |
| "attention_dropout": 0.0, | |
| "auto_map": { | |
| "AutoConfig": "configuration_ncp_olmo3.NCPOlmo3Config", | |
| "AutoModelForCausalLM": "modeling_ncp_olmo3.NCPOlmo3ForCausalLM" | |
| }, | |
| "checkpoint_step": 1200000, | |
| "concept_chunk_size": 4, | |
| "concept_shift_feature": true, | |
| "conceptlm_backbone": "olmo3", | |
| "conceptlm_chunk_dualpath_alpha_init": 0.5, | |
| "conceptlm_chunk_merge_method": "meanpooling", | |
| "conceptlm_chunk_size": 4, | |
| "conceptlm_decoder_layers": 16, | |
| "conceptlm_enable_chunk_dualpath_smoothing": false, | |
| "conceptlm_encoder_layers": 16, | |
| "conceptlm_fusion_alpha_init": 1.0, | |
| "conceptlm_fusion_norm_alpha_init": 0.1, | |
| "conceptlm_hlm_attention_mode": "backbone_window", | |
| "conceptlm_hlm_ffn_hidden_size": null, | |
| "conceptlm_hlm_loss_weight": 1.0, | |
| "conceptlm_layer_norm_option": "normed_add", | |
| "conceptlm_shift_feature": true, | |
| "conceptlm_special_layers": 8, | |
| "conceptlm_v21_compile_dd_routes": true, | |
| "conceptlm_v21_compile_residual_flow_routes": true, | |
| "conceptlm_v21_concept_read_encoder_first_n": -1, | |
| "conceptlm_v21_dd_concept_self_dd": true, | |
| "conceptlm_v21_dd_concept_self_dd_every_n_layers": 1, | |
| "conceptlm_v21_dd_concept_self_dd_hidden_size": 0, | |
| "conceptlm_v21_dd_concept_self_dd_use_layernorm": false, | |
| "conceptlm_v21_dd_encoder_self_dd": true, | |
| "conceptlm_v21_dd_encoder_self_dd_every_n_layers": 1, | |
| "conceptlm_v21_dd_encoder_self_dd_hidden_size": 0, | |
| "conceptlm_v21_dd_encoder_self_dd_use_layernorm": false, | |
| "conceptlm_v21_dd_self_dd_mode": "dd", | |
| "conceptlm_v21_dd_two_route_add": true, | |
| "conceptlm_v21_dd_two_route_add_beta_init": 0.05, | |
| "conceptlm_v21_dd_two_route_add_concept_hidden_size": 0, | |
| "conceptlm_v21_dd_two_route_add_concept_route_first_n": -1, | |
| "conceptlm_v21_dd_two_route_add_concept_source": "final", | |
| "conceptlm_v21_dd_two_route_add_concept_use_layernorm": true, | |
| "conceptlm_v21_dd_two_route_add_decoder_hidden_size": 0, | |
| "conceptlm_v21_dd_two_route_add_decoder_use_layernorm": false, | |
| "conceptlm_v21_dd_two_route_add_decoder_use_softmax": true, | |
| "conceptlm_v21_dd_two_route_add_disable_decoder_dd": false, | |
| "conceptlm_v21_dd_two_route_add_enable_final_concept_route": true, | |
| "conceptlm_v21_dd_two_route_add_enable_raw_concept_route": false, | |
| "conceptlm_v21_dd_two_route_add_every_n_layers": 1, | |
| "conceptlm_v21_dd_two_route_add_use_softmax": false, | |
| "conceptlm_v21_decoder_read_encoder_first_n": -1, | |
| "conceptlm_v21_enable_concept_read_encoder": true, | |
| "conceptlm_v21_enable_decoder_read_concept": true, | |
| "conceptlm_v21_enable_decoder_read_encoder": true, | |
| "conceptlm_v21_enable_full_residual_flow": false, | |
| "conceptlm_v21_final_read_concept_gate": true, | |
| "conceptlm_v21_final_read_concept_gate_init_final": 0.5, | |
| "conceptlm_v21_final_read_concept_gate_reg_weight": 0.0, | |
| "conceptlm_v21_final_read_concept_gate_target_final": 0.5, | |
| "conceptlm_v21_residual_flow_beta_init": 0.02, | |
| "conceptlm_v21_residual_flow_route_hidden_size": 0, | |
| "conceptlm_v21_residual_flow_route_use_softmax": true, | |
| "conceptlm_v21_residual_flow_shared_source_norm": true, | |
| "conceptlm_v21_residual_flow_source_use_layernorm": true, | |
| "conceptlm_v22_vq_codebook_size": 128, | |
| "conceptlm_v22_vq_commitment_cost": 0.25, | |
| "conceptlm_v22_vq_detach_hlm_target": true, | |
| "conceptlm_v22_vq_highlevel_lr_mult": 1.0, | |
| "conceptlm_v22_vq_hlm_loss_type": "mse", | |
| "conceptlm_v22_vq_lr_mult": 1.0, | |
| "conceptlm_v22_vq_merge_mode": "raw_logits", | |
| "conceptlm_v22_vq_num_codebooks": 32, | |
| "conceptlm_vq_loss_weight": 1.0, | |
| "custom_model_loader_required": false, | |
| "ffn_hidden_size": 11008, | |
| "fp32_residual_connection": false, | |
| "gated_linear_unit": true, | |
| "hidden_dropout": 0.0, | |
| "hidden_size": 4096, | |
| "kv_channels": 128, | |
| "layernorm_epsilon": 1e-06, | |
| "layernorm_zero_centered_gamma": false, | |
| "max_position_embeddings": 8192, | |
| "max_sequence_length": 8192, | |
| "model_type": "ncp_olmo3", | |
| "normalization": "RMSNorm", | |
| "num_attention_heads": 32, | |
| "num_hidden_layers": 40, | |
| "num_layers": 32, | |
| "num_query_groups": 32, | |
| "params_dtype": "bfloat16", | |
| "position_embedding_type": "rope", | |
| "qk_layernorm": true, | |
| "rotary_base": 500000, | |
| "rotary_interleaved": false, | |
| "rotary_percent": 1.0, | |
| "standalone_hf_backend": true, | |
| "standalone_hf_backend_status": "parity_candidate", | |
| "torch_dtype": "bfloat16", | |
| "transformers_version": "5.8.0", | |
| "untie_embeddings_and_output_weights": true, | |
| "use_cache": true, | |
| "vocab_size": 100278, | |
| "weight_key_format": "native_megatron_state_dict", | |
| "window_attn_skip_freq": 4, | |
| "window_size": [ | |
| 4096, | |
| 0 | |
| ] | |
| } | |