Text Generation
Transformers
Safetensors
English
nvidia
nemotron
two-tower
nvfp4
modelopt
atlas
8-bit precision
Instructions to use rafaelcaricio/nemotron-twotower-nvfp4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rafaelcaricio/nemotron-twotower-nvfp4 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="rafaelcaricio/nemotron-twotower-nvfp4")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("rafaelcaricio/nemotron-twotower-nvfp4", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use rafaelcaricio/nemotron-twotower-nvfp4 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "rafaelcaricio/nemotron-twotower-nvfp4" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "rafaelcaricio/nemotron-twotower-nvfp4", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/rafaelcaricio/nemotron-twotower-nvfp4
- SGLang
How to use rafaelcaricio/nemotron-twotower-nvfp4 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 "rafaelcaricio/nemotron-twotower-nvfp4" \ --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": "rafaelcaricio/nemotron-twotower-nvfp4", "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 "rafaelcaricio/nemotron-twotower-nvfp4" \ --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": "rafaelcaricio/nemotron-twotower-nvfp4", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use rafaelcaricio/nemotron-twotower-nvfp4 with Docker Model Runner:
docker model run hf.co/rafaelcaricio/nemotron-twotower-nvfp4
Download config.json from rafaelcaricio/nemotron-twotower-nvfp4: direct link, hf CLI and curl.
- Browser
- Download file 10.4 kB
-
https://huggingface.co/rafaelcaricio/nemotron-twotower-nvfp4/resolve/main/config.json
- Command line
-
hf download hf://rafaelcaricio/nemotron-twotower-nvfp4/config.json
-
curl -L -o config.json https://huggingface.co/rafaelcaricio/nemotron-twotower-nvfp4/resolve/main/config.json
10.4 kB
Invalid JSON:Unexpected token 'I', ...",
Infinity
"... is not valid JSON
| { | |
| "architectures": [ | |
| "NemotronHTwoTowerForCausalLM" | |
| ], | |
| "attention_bias": false, | |
| "attention_dropout": 0.0, | |
| "auto_map": { | |
| "AutoConfig": "configuration_nemotron_h.NemotronHConfig", | |
| "AutoModelForCausalLM": "modeling_nemotron_twotower.NemotronHTwoTowerForCausalLM" | |
| }, | |
| "bos_token_id": 1, | |
| "chunk_size": 128, | |
| "conv_kernel": 4, | |
| "dtype": "bfloat16", | |
| "eos_token_id": 2, | |
| "expand": 2, | |
| "head_dim": 128, | |
| "hidden_dropout": 0.0, | |
| "hidden_size": 2688, | |
| "hybrid_override_pattern": "MEMEM*EMEMEM*EMEMEM*EMEMEM*EMEMEM*EMEMEMEM*EMEMEMEME", | |
| "initializer_range": 0.02, | |
| "intermediate_size": 1856, | |
| "layer_norm_epsilon": 1e-05, | |
| "mamba_head_dim": 64, | |
| "mamba_hidden_act": "silu", | |
| "mamba_num_heads": 64, | |
| "mamba_proj_bias": false, | |
| "max_position_embeddings": 262144, | |
| "mlp_bias": false, | |
| "mlp_hidden_act": "relu2", | |
| "model_type": "nemotron_h", | |
| "moe_intermediate_size": 1856, | |
| "moe_shared_expert_intermediate_size": 3712, | |
| "n_group": 1, | |
| "n_groups": 8, | |
| "n_routed_experts": 128, | |
| "n_shared_experts": 1, | |
| "norm_eps": 1e-05, | |
| "norm_topk_prob": true, | |
| "num_attention_heads": 32, | |
| "num_experts_per_tok": 6, | |
| "num_hidden_layers": 52, | |
| "num_key_value_heads": 2, | |
| "num_logits_to_keep": 1, | |
| "pad_token_id": 0, | |
| "partial_rotary_factor": 1.0, | |
| "rescale_prenorm_residual": true, | |
| "residual_in_fp32": false, | |
| "rope_theta": 10000, | |
| "routed_scaling_factor": 2.5, | |
| "sliding_window": null, | |
| "ssm_state_size": 128, | |
| "tie_word_embeddings": false, | |
| "time_step_floor": 0.0001, | |
| "time_step_limit": [ | |
| 0.0, | |
| Infinity | |
| ], | |
| "time_step_max": 0.1, | |
| "time_step_min": 0.001, | |
| "topk_group": 1, | |
| "transformers_version": "4.57.1", | |
| "use_bias": false, | |
| "use_cache": true, | |
| "use_conv_bias": true, | |
| "use_mamba_kernels": true, | |
| "vocab_size": 131072, | |
| "quantization_config": { | |
| "config_groups": { | |
| "group_0": { | |
| "input_activations": { | |
| "dynamic": false, | |
| "num_bits": 4, | |
| "type": "float", | |
| "group_size": 16 | |
| }, | |
| "weights": { | |
| "dynamic": false, | |
| "num_bits": 4, | |
| "type": "float", | |
| "group_size": 16 | |
| }, | |
| "targets": [ | |
| "Linear" | |
| ] | |
| } | |
| }, | |
| "ignore": [ | |
| "context_lm_head", | |
| "context_tower.embeddings", | |
| "context_tower.layers.0*", | |
| "context_tower.layers.1.mixer.gate", | |
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| "context_tower.layers.10.mixer.shared_experts*", | |
| "context_tower.layers.11*", | |
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| "context_tower.layers.13.mixer.shared_experts*", | |
| "context_tower.layers.14*", | |
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| "context_tower.layers.15.mixer.shared_experts*", | |
| "context_tower.layers.16*", | |
| "context_tower.layers.17.mixer.gate", | |
| "context_tower.layers.17.mixer.shared_experts*", | |
| "context_tower.layers.18*", | |
| "context_tower.layers.19*", | |
| "context_tower.layers.2.*", | |
| "context_tower.layers.20.mixer.gate", | |
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| "context_tower.layers.5.*", | |
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| "context_tower.layers.6.mixer.gate", | |
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| "context_tower.layers.7*", | |
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| "denoiser_tower.embeddings", | |
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| "denoiser_tower.layers.2.*", | |
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| "denoiser_tower.layers.49.mixer.gate", | |
| "denoiser_tower.layers.49.mixer.shared_experts*", | |
| "denoiser_tower.layers.5.*", | |
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| "denoiser_tower.layers.51.mixer.gate", | |
| "denoiser_tower.layers.51.mixer.shared_experts*", | |
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| "denoiser_tower.layers.9*", | |
| "lm_head", | |
| "t_block*", | |
| "t_embedder*" | |
| ], | |
| "quant_algo": "NVFP4", | |
| "producer": { | |
| "name": "modelopt", | |
| "version": "0.46.0.dev129+g973cb09cb" | |
| }, | |
| "quant_method": "modelopt" | |
| } | |
| } |