nemotron-3-super:120b

91 an hour ago

NVIDIA Nemotron 3 Super is a 120B open MoE model activating just 12B parameters to deliver maximum compute efficiency and accuracy for complex multi-agent applications.

tools thinking cloud 120b
... /
model
0fc53cc990a2 · 87GB
    Metadata
  • general.architecture
    nemotron_h_moe
  • general.file_type
    Q4_K_M
  • nemotron_h_moe.attention.head_count
    32
  • nemotron_h_moe.attention.head_count_kv
    [0, 0, 0, 0, 0, ...]
  • nemotron_h_moe.attention.key_length
    128
  • nemotron_h_moe.attention.layer_norm_epsilon
    1e-05
  • nemotron_h_moe.attention.layer_norm_rms_epsilon
    1e-05
  • nemotron_h_moe.attention.value_length
    128
  • nemotron_h_moe.block_count
    88
  • nemotron_h_moe.context_length
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  • nemotron_h_moe.embedding_length
    4096
  • nemotron_h_moe.expert_count
    512
  • nemotron_h_moe.expert_feed_forward_length
    2688
  • nemotron_h_moe.expert_group_count
    1
  • nemotron_h_moe.expert_group_used_count
    1
  • nemotron_h_moe.expert_shared_count
    1
  • nemotron_h_moe.expert_shared_feed_forward_length
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  • nemotron_h_moe.expert_used_count
    22
  • nemotron_h_moe.expert_weights_norm
    true
  • nemotron_h_moe.expert_weights_scale
    5
  • nemotron_h_moe.feed_forward_length
    [0, 2688, 0, 2688, 0, ...]
  • nemotron_h_moe.rope.dimension_count
    128
  • nemotron_h_moe.rope.freq_base
    10000
  • nemotron_h_moe.ssm.conv_kernel
    4
  • nemotron_h_moe.ssm.group_count
    8
  • nemotron_h_moe.ssm.inner_size
    8192
  • nemotron_h_moe.ssm.state_size
    128
  • nemotron_h_moe.ssm.time_step_rank
    128
  • tokenizer.ggml.add_bos_token
    false
  • tokenizer.ggml.add_eos_token
    false
  • tokenizer.ggml.add_padding_token
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  • tokenizer.ggml.add_unknown_token
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  • tokenizer.ggml.bos_token_id
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  • tokenizer.ggml.eos_token_id
    11
  • tokenizer.ggml.eos_token_ids
    [2, 11]
  • tokenizer.ggml.merges
    [Ġ Ġ, Ġ t, e r, i n, Ġ ĠĠĠ, ...]
  • tokenizer.ggml.model
    gpt2
  • tokenizer.ggml.padding_token_id
    11
  • tokenizer.ggml.pre
    default
  • tokenizer.ggml.scores
    [0, 1, 2, 3, 4, ...]
  • tokenizer.ggml.token_type
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  • tokenizer.ggml.tokens
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  • mtp.layers.1.mixer.experts.495.down_proj.weight
    Q5_0
    [2688, 1024]
  • mtp.layers.1.mixer.experts.495.up_proj.weight
    Q4_K
    [1024, 2688]
  • mtp.layers.1.mixer.experts.496.down_proj.weight
    Q5_0
    [2688, 1024]
  • mtp.layers.1.mixer.experts.496.up_proj.weight
    Q4_K
    [1024, 2688]
  • mtp.layers.1.mixer.experts.497.down_proj.weight
    Q5_0
    [2688, 1024]
  • mtp.layers.1.mixer.experts.497.up_proj.weight
    Q4_K
    [1024, 2688]
  • mtp.layers.1.mixer.experts.498.down_proj.weight
    Q5_0
    [2688, 1024]
  • mtp.layers.1.mixer.experts.498.up_proj.weight
    Q4_K
    [1024, 2688]
  • mtp.layers.1.mixer.experts.499.down_proj.weight
    Q5_0
    [2688, 1024]
  • mtp.layers.1.mixer.experts.499.up_proj.weight
    Q4_K
    [1024, 2688]
  • mtp.layers.1.mixer.experts.500.down_proj.weight
    Q5_0
    [2688, 1024]
  • mtp.layers.1.mixer.experts.500.up_proj.weight
    Q4_K
    [1024, 2688]
  • mtp.layers.1.mixer.experts.501.down_proj.weight
    Q5_0
    [2688, 1024]
  • mtp.layers.1.mixer.experts.501.up_proj.weight
    Q4_K
    [1024, 2688]
  • mtp.layers.1.mixer.experts.502.down_proj.weight
    Q5_0
    [2688, 1024]
  • mtp.layers.1.mixer.experts.502.up_proj.weight
    Q4_K
    [1024, 2688]
  • mtp.layers.1.mixer.experts.503.down_proj.weight
    Q5_0
    [2688, 1024]
  • mtp.layers.1.mixer.experts.503.up_proj.weight
    Q4_K
    [1024, 2688]
  • mtp.layers.1.mixer.experts.504.down_proj.weight
    Q5_0
    [2688, 1024]
  • mtp.layers.1.mixer.experts.504.up_proj.weight
    Q4_K
    [1024, 2688]
  • mtp.layers.1.mixer.experts.505.down_proj.weight
    Q5_0
    [2688, 1024]
  • mtp.layers.1.mixer.experts.505.up_proj.weight
    Q4_K
    [1024, 2688]
  • mtp.layers.1.mixer.experts.506.down_proj.weight
    Q5_0
    [2688, 1024]
  • mtp.layers.1.mixer.experts.506.up_proj.weight
    Q4_K
    [1024, 2688]
  • mtp.layers.1.mixer.experts.507.down_proj.weight
    Q5_0
    [2688, 1024]
  • mtp.layers.1.mixer.experts.507.up_proj.weight
    Q4_K
    [1024, 2688]
  • mtp.layers.1.mixer.experts.508.down_proj.weight
    Q5_0
    [2688, 1024]
  • mtp.layers.1.mixer.experts.508.up_proj.weight
    Q4_K
    [1024, 2688]
  • mtp.layers.1.mixer.experts.509.down_proj.weight
    Q5_0
    [2688, 1024]
  • mtp.layers.1.mixer.experts.509.up_proj.weight
    Q4_K
    [1024, 2688]
  • mtp.layers.1.mixer.experts.510.down_proj.weight
    Q5_0
    [2688, 1024]
  • mtp.layers.1.mixer.experts.510.up_proj.weight
    Q4_K
    [1024, 2688]
  • mtp.layers.1.mixer.experts.511.down_proj.weight
    Q5_0
    [2688, 1024]
  • mtp.layers.1.mixer.experts.511.up_proj.weight
    Q4_K
    [1024, 2688]
  • output.weight
    Q6_K
    [4096, 131072]
  • output_norm.weight
    F32
    [4096]