huihui_ai/exaone3.5-abliterated

EXAONE 3.5 is a collection of instruction-tuned bilingual (English and Korean) generative models ranging from 2.4B to 32B parameters, developed and released by LG AI Research.

2.4b 7.8b

18 9 hours ago

9 Tags
... /
model
44b4d6364049 · 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.base_model.0.name
    EXAONE 3.5 7.8B Instruct
  • general.base_model.0.organization
    LGAI EXAONE
  • general.base_model.0.repo_url
    https://huggingface.co/LGAI-EXAONE/EXAONE-3.5-7.8B-Instruct
  • general.base_model.count
    1
  • general.basename
    EXAONE-3.5
  • general.file_type
    15
  • general.finetune
    Instruct-abliterated
  • general.languages
    [en, ko]
  • general.license
    other
  • general.license.link
    LICENSE
  • general.license.name
    exaone
  • general.name
    EXAONE 3.5 7.8B Instruct Abliterated
  • general.quantization_version
    2
  • general.size_label
    7.8B
  • general.tags
    [lg-ai, exaone, exaone-3.5, abliterated, uncensored, ...]
  • general.type
    model
  • 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
  • Name
    Type
    Shape
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    Q4_K
    [4096, 102400]
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    Q4_K
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  • blk.0.attn_norm.weight
    F32
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  • blk.0.attn_output.weight
    Q4_K
    [4096, 4096]
  • blk.0.attn_q.weight
    Q4_K
    [4096, 4096]
  • blk.0.attn_v.weight
    Q6_K
    [4096, 1024]
  • blk.0.ffn_down.weight
    Q6_K
    [14336, 4096]
  • blk.0.ffn_gate.weight
    Q4_K
    [4096, 14336]
  • blk.0.ffn_norm.weight
    F32
    [4096]
  • blk.0.ffn_up.weight
    Q4_K
    [4096, 14336]
    • blk.1
  • blk.1.attn_k.weight
    Q4_K
    [4096, 1024]
  • blk.1.attn_norm.weight
    F32
    [4096]
  • blk.1.attn_output.weight
    Q4_K
    [4096, 4096]
  • blk.1.attn_q.weight
    Q4_K
    [4096, 4096]
  • blk.1.attn_v.weight
    Q6_K
    [4096, 1024]
  • blk.1.ffn_down.weight
    Q6_K
    [14336, 4096]
  • blk.1.ffn_gate.weight
    Q4_K
    [4096, 14336]
  • blk.1.ffn_norm.weight
    F32
    [4096]
  • blk.1.ffn_up.weight
    Q4_K
    [4096, 14336]
    • blk.2
  • blk.2.attn_k.weight
    Q4_K
    [4096, 1024]
  • blk.2.attn_norm.weight
    F32
    [4096]
  • blk.2.attn_output.weight
    Q4_K
    [4096, 4096]
  • blk.2.attn_q.weight
    Q4_K
    [4096, 4096]
  • blk.2.attn_v.weight
    Q6_K
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  • blk.2.ffn_down.weight
    Q6_K
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  • blk.2.ffn_gate.weight
    Q4_K
    [4096, 14336]
  • blk.2.ffn_norm.weight
    F32
    [4096]
  • blk.2.ffn_up.weight
    Q4_K
    [4096, 14336]
    • blk.3
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    Q4_K
    [4096, 1024]
  • blk.3.attn_norm.weight
    F32
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  • blk.3.attn_output.weight
    Q4_K
    [4096, 4096]
  • blk.3.attn_q.weight
    Q4_K
    [4096, 4096]
  • blk.3.attn_v.weight
    Q6_K
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    Q6_K
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    Q4_K
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  • blk.3.ffn_norm.weight
    F32
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  • blk.3.ffn_up.weight
    Q4_K
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    • blk.4
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    Q4_K
    [4096, 1024]
  • blk.4.attn_norm.weight
    F32
    [4096]
  • blk.4.attn_output.weight
    Q4_K
    [4096, 4096]
  • blk.4.attn_q.weight
    Q4_K
    [4096, 4096]
  • blk.4.attn_v.weight
    Q4_K
    [4096, 1024]
  • blk.4.ffn_down.weight
    Q4_K
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  • blk.4.ffn_gate.weight
    Q4_K
    [4096, 14336]
  • blk.4.ffn_norm.weight
    F32
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  • blk.4.ffn_up.weight
    Q4_K
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    Q4_K
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  • blk.5.attn_norm.weight
    F32
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    Q4_K
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  • blk.5.attn_q.weight
    Q4_K
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  • blk.5.attn_v.weight
    Q4_K
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    Q4_K
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  • blk.5.ffn_gate.weight
    Q4_K
    [4096, 14336]
  • blk.5.ffn_norm.weight
    F32
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  • blk.5.ffn_up.weight
    Q4_K
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    • blk.6
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    Q4_K
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  • blk.6.attn_norm.weight
    F32
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  • blk.6.attn_output.weight
    Q4_K
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  • blk.6.attn_q.weight
    Q4_K
    [4096, 4096]
  • blk.6.attn_v.weight
    Q6_K
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  • blk.6.ffn_down.weight
    Q6_K
    [14336, 4096]
  • blk.6.ffn_gate.weight
    Q4_K
    [4096, 14336]
  • blk.6.ffn_norm.weight
    F32
    [4096]
  • blk.6.ffn_up.weight
    Q4_K
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    • blk.7
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    Q4_K
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    F32
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  • blk.7.attn_output.weight
    Q4_K
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  • blk.7.attn_q.weight
    Q4_K
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  • blk.7.attn_v.weight
    Q4_K
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    Q4_K
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    Q4_K
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    F32
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    Q4_K
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    F32
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    Q4_K
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    Q4_K
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    Q4_K
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    Q4_K
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    Q4_K
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    F32
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    Q4_K
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    Q4_K
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    F32
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    Q4_K
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    Q4_K
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  • blk.9.attn_v.weight
    Q6_K
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  • blk.9.ffn_down.weight
    Q6_K
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  • blk.9.ffn_gate.weight
    Q4_K
    [4096, 14336]
  • blk.9.ffn_norm.weight
    F32
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  • blk.9.ffn_up.weight
    Q4_K
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    Q4_K
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    F32
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    Q4_K
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  • blk.10.attn_q.weight
    Q4_K
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  • blk.10.attn_v.weight
    Q4_K
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    Q4_K
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    Q4_K
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  • blk.10.ffn_norm.weight
    F32
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    Q4_K
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    Q4_K
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    F32
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    Q4_K
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    Q4_K
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    Q4_K
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    Q4_K
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    Q4_K
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    Q6_K
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    Q6_K
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    Q4_K
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    F32
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    Q4_K
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    F32
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    F32
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    Q4_K
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    F32
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    Q4_K
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    Q4_K
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    Q4_K
    [4096, 14336]
  • blk.25.ffn_norm.weight
    F32
    [4096]
  • blk.25.ffn_up.weight
    Q4_K
    [4096, 14336]
    • blk.26
  • blk.26.attn_k.weight
    Q4_K
    [4096, 1024]
  • blk.26.attn_norm.weight
    F32
    [4096]
  • blk.26.attn_output.weight
    Q4_K
    [4096, 4096]
  • blk.26.attn_q.weight
    Q4_K
    [4096, 4096]
  • blk.26.attn_v.weight
    Q4_K
    [4096, 1024]
  • blk.26.ffn_down.weight
    Q4_K
    [14336, 4096]
  • blk.26.ffn_gate.weight
    Q4_K
    [4096, 14336]
  • blk.26.ffn_norm.weight
    F32
    [4096]
  • blk.26.ffn_up.weight
    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]