exaone-deep

EXAONE Deep exhibits superior capabilities in various reasoning tasks including math and coding benchmarks, ranging from 2.4B to 32B parameters developed and released by LG AI Research.

2.4b 7.8b 32b

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model
383cc0823946 · 4.8GB
    Metadata
  • exaone.attention.head_count
    32
  • exaone.attention.head_count_kv
    8
  • exaone.attention.layer_norm_rms_epsilon
    1e-05
  • exaone.block_count
    32
  • exaone.context_length
    32768
  • exaone.embedding_length
    4096
  • exaone.feed_forward_length
    14336
  • exaone.rope.dimension_count
    128
  • exaone.rope.freq_base
    1e+06
  • general.architecture
    exaone
  • general.file_type
    Q4_K_M
  • tokenizer.ggml.bos_token_id
    1
  • tokenizer.ggml.eos_token_id
    361
  • tokenizer.ggml.merges
    [t h, Ġ a, Ġ í, i n, Ġ th, ...]
  • tokenizer.ggml.model
    gpt2
  • tokenizer.ggml.padding_token_id
    0
  • tokenizer.ggml.pre
    exaone
  • tokenizer.ggml.token_type
    [3, 3, 3, 3, 4, ...]
  • tokenizer.ggml.tokens
    [[PAD], [BOS], [EOS], [UNK], , ...]
  • tokenizer.ggml.unknown_token_id
    3
    • Tensor
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    Q4_K
    [4096, 14336]
    • blk.27
  • blk.27.attn_k.weight
    Q4_K
    [4096, 1024]
  • blk.27.attn_norm.weight
    F32
    [4096]
  • blk.27.attn_output.weight
    Q4_K
    [4096, 4096]
  • blk.27.attn_q.weight
    Q4_K
    [4096, 4096]
  • blk.27.attn_v.weight
    Q6_K
    [4096, 1024]
  • blk.27.ffn_down.weight
    Q6_K
    [14336, 4096]
  • blk.27.ffn_gate.weight
    Q4_K
    [4096, 14336]
  • blk.27.ffn_norm.weight
    F32
    [4096]
  • blk.27.ffn_up.weight
    Q4_K
    [4096, 14336]
    • blk.28
  • blk.28.attn_k.weight
    Q4_K
    [4096, 1024]
  • blk.28.attn_norm.weight
    F32
    [4096]
  • blk.28.attn_output.weight
    Q4_K
    [4096, 4096]
  • blk.28.attn_q.weight
    Q4_K
    [4096, 4096]
  • blk.28.attn_v.weight
    Q6_K
    [4096, 1024]
  • blk.28.ffn_down.weight
    Q6_K
    [14336, 4096]
  • blk.28.ffn_gate.weight
    Q4_K
    [4096, 14336]
  • blk.28.ffn_norm.weight
    F32
    [4096]
  • blk.28.ffn_up.weight
    Q4_K
    [4096, 14336]
    • blk.29
  • blk.29.attn_k.weight
    Q4_K
    [4096, 1024]
  • blk.29.attn_norm.weight
    F32
    [4096]
  • blk.29.attn_output.weight
    Q4_K
    [4096, 4096]
  • blk.29.attn_q.weight
    Q4_K
    [4096, 4096]
  • blk.29.attn_v.weight
    Q6_K
    [4096, 1024]
  • blk.29.ffn_down.weight
    Q6_K
    [14336, 4096]
  • blk.29.ffn_gate.weight
    Q4_K
    [4096, 14336]
  • blk.29.ffn_norm.weight
    F32
    [4096]
  • blk.29.ffn_up.weight
    Q4_K
    [4096, 14336]
    • blk.30
  • blk.30.attn_k.weight
    Q4_K
    [4096, 1024]
  • blk.30.attn_norm.weight
    F32
    [4096]
  • blk.30.attn_output.weight
    Q4_K
    [4096, 4096]
  • blk.30.attn_q.weight
    Q4_K
    [4096, 4096]
  • blk.30.attn_v.weight
    Q6_K
    [4096, 1024]
  • blk.30.ffn_down.weight
    Q6_K
    [14336, 4096]
  • blk.30.ffn_gate.weight
    Q4_K
    [4096, 14336]
  • blk.30.ffn_norm.weight
    F32
    [4096]
  • blk.30.ffn_up.weight
    Q4_K
    [4096, 14336]
    • blk.31
  • blk.31.attn_k.weight
    Q4_K
    [4096, 1024]
  • blk.31.attn_norm.weight
    F32
    [4096]
  • blk.31.attn_output.weight
    Q4_K
    [4096, 4096]
  • blk.31.attn_q.weight
    Q4_K
    [4096, 4096]
  • blk.31.attn_v.weight
    Q6_K
    [4096, 1024]
  • blk.31.ffn_down.weight
    Q6_K
    [14336, 4096]
  • blk.31.ffn_gate.weight
    Q4_K
    [4096, 14336]
  • blk.31.ffn_norm.weight
    F32
    [4096]
  • blk.31.ffn_up.weight
    Q4_K
    [4096, 14336]
  • output.weight
    Q6_K
    [4096, 102400]
  • rope_freqs.weight
    F32
    [64]
  • output_norm.weight
    F32
    [4096]