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

15.6K 10 days ago

13 Tags
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model
d4994f8326a5 · 8.3GB
    Metadata
  • general.architecture
    exaone
  • general.file_type
    Q8_0
  • 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
  • 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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    [4096, 14336]
    • blk.27
  • blk.27.attn_k.weight
    Q8_0
    [4096, 1024]
  • blk.27.attn_norm.weight
    F32
    [4096]
  • blk.27.attn_output.weight
    Q8_0
    [4096, 4096]
  • blk.27.attn_q.weight
    Q8_0
    [4096, 4096]
  • blk.27.attn_v.weight
    Q8_0
    [4096, 1024]
  • blk.27.ffn_down.weight
    Q8_0
    [14336, 4096]
  • blk.27.ffn_gate.weight
    Q8_0
    [4096, 14336]
  • blk.27.ffn_norm.weight
    F32
    [4096]
  • blk.27.ffn_up.weight
    Q8_0
    [4096, 14336]
    • blk.28
  • blk.28.attn_k.weight
    Q8_0
    [4096, 1024]
  • blk.28.attn_norm.weight
    F32
    [4096]
  • blk.28.attn_output.weight
    Q8_0
    [4096, 4096]
  • blk.28.attn_q.weight
    Q8_0
    [4096, 4096]
  • blk.28.attn_v.weight
    Q8_0
    [4096, 1024]
  • blk.28.ffn_down.weight
    Q8_0
    [14336, 4096]
  • blk.28.ffn_gate.weight
    Q8_0
    [4096, 14336]
  • blk.28.ffn_norm.weight
    F32
    [4096]
  • blk.28.ffn_up.weight
    Q8_0
    [4096, 14336]
    • blk.29
  • blk.29.attn_k.weight
    Q8_0
    [4096, 1024]
  • blk.29.attn_norm.weight
    F32
    [4096]
  • blk.29.attn_output.weight
    Q8_0
    [4096, 4096]
  • blk.29.attn_q.weight
    Q8_0
    [4096, 4096]
  • blk.29.attn_v.weight
    Q8_0
    [4096, 1024]
  • blk.29.ffn_down.weight
    Q8_0
    [14336, 4096]
  • blk.29.ffn_gate.weight
    Q8_0
    [4096, 14336]
  • blk.29.ffn_norm.weight
    F32
    [4096]
  • blk.29.ffn_up.weight
    Q8_0
    [4096, 14336]
    • blk.30
  • blk.30.attn_k.weight
    Q8_0
    [4096, 1024]
  • blk.30.attn_norm.weight
    F32
    [4096]
  • blk.30.attn_output.weight
    Q8_0
    [4096, 4096]
  • blk.30.attn_q.weight
    Q8_0
    [4096, 4096]
  • blk.30.attn_v.weight
    Q8_0
    [4096, 1024]
  • blk.30.ffn_down.weight
    Q8_0
    [14336, 4096]
  • blk.30.ffn_gate.weight
    Q8_0
    [4096, 14336]
  • blk.30.ffn_norm.weight
    F32
    [4096]
  • blk.30.ffn_up.weight
    Q8_0
    [4096, 14336]
    • blk.31
  • blk.31.attn_k.weight
    Q8_0
    [4096, 1024]
  • blk.31.attn_norm.weight
    F32
    [4096]
  • blk.31.attn_output.weight
    Q8_0
    [4096, 4096]
  • blk.31.attn_q.weight
    Q8_0
    [4096, 4096]
  • blk.31.attn_v.weight
    Q8_0
    [4096, 1024]
  • blk.31.ffn_down.weight
    Q8_0
    [14336, 4096]
  • blk.31.ffn_gate.weight
    Q8_0
    [4096, 14336]
  • blk.31.ffn_norm.weight
    F32
    [4096]
  • blk.31.ffn_up.weight
    Q8_0
    [4096, 14336]
  • output.weight
    Q8_0
    [4096, 102400]
  • rope_freqs.weight
    F32
    [64]
  • output_norm.weight
    F32
    [4096]