sroecker/nuextract-tiny-v1.5

NuExtract-tiny-v1.5 is a fine-tuning of Qwen/Qwen2.5-0.5B, trained on a private high-quality dataset for structured information extraction. It supports long documents and several languages (English, French, Spanish, German, Portuguese, and Italian).

164 6 weeks ago

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model
ebee6e5e0f8a · 994MB
    Metadata
  • general.architecture
    qwen2
  • general.base_model.0.name
    Qwen2.5 0.5B
  • general.base_model.0.organization
    Qwen
  • general.base_model.0.repo_url
    https://huggingface.co/Qwen/Qwen2.5-0.5B
  • general.base_model.count
    1
  • general.basename
    NuExtract-tiny
  • general.file_type
    1
  • general.languages
    [multilingual]
  • general.license
    mit
  • general.name
    NuExtract Tiny v1.5
  • general.organization
    Numind
  • general.quantization_version
    2
  • general.size_label
    494M
  • general.tags
    [nlp, text-generation]
  • general.type
    model
  • general.version
    v1.5
  • qwen2.attention.head_count
    14
  • qwen2.attention.head_count_kv
    2
  • qwen2.attention.layer_norm_rms_epsilon
    1e-06
  • qwen2.block_count
    24
  • qwen2.context_length
    32768
  • qwen2.embedding_length
    896
  • qwen2.feed_forward_length
    4864
  • qwen2.rope.freq_base
    1e+06
  • tokenizer.ggml.add_bos_token
    false
  • tokenizer.ggml.bos_token_id
    151643
  • tokenizer.ggml.eos_token_id
    151643
  • tokenizer.ggml.merges
    [Ġ Ġ, ĠĠ ĠĠ, i n, Ġ t, ĠĠĠĠ ĠĠĠĠ, ...]
  • tokenizer.ggml.model
    gpt2
  • tokenizer.ggml.padding_token_id
    151643
  • tokenizer.ggml.pre
    qwen2
  • tokenizer.ggml.token_type
    [1, 1, 1, 1, 1, ...]
  • tokenizer.ggml.tokens
    [!, ", #, $, %, ...]
    • Tensor
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    Type
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    F16
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    [128]
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    [896]
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    F16
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    [128]
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    F16
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    [4864, 896]
  • blk.21.ffn_gate.weight
    F16
    [896, 4864]
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    F32
    [896]
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    F16
    [896, 4864]
    • blk.22
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    F32
    [128]
  • blk.22.attn_k.weight
    F16
    [896, 128]
  • blk.22.attn_norm.weight
    F32
    [896]
  • blk.22.attn_output.weight
    F16
    [896, 896]
  • blk.22.attn_q.bias
    F32
    [896]
  • blk.22.attn_q.weight
    F16
    [896, 896]
  • blk.22.attn_v.bias
    F32
    [128]
  • blk.22.attn_v.weight
    F16
    [896, 128]
  • blk.22.ffn_down.weight
    F16
    [4864, 896]
  • blk.22.ffn_gate.weight
    F16
    [896, 4864]
  • blk.22.ffn_norm.weight
    F32
    [896]
  • blk.22.ffn_up.weight
    F16
    [896, 4864]
    • blk.23
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    F32
    [128]
  • blk.23.attn_k.weight
    F16
    [896, 128]
  • blk.23.attn_norm.weight
    F32
    [896]
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    F16
    [896, 896]
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    F32
    [896]
  • blk.23.attn_q.weight
    F16
    [896, 896]
  • blk.23.attn_v.bias
    F32
    [128]
  • blk.23.attn_v.weight
    F16
    [896, 128]
  • blk.23.ffn_down.weight
    F16
    [4864, 896]
  • blk.23.ffn_gate.weight
    F16
    [896, 4864]
  • blk.23.ffn_norm.weight
    F32
    [896]
  • blk.23.ffn_up.weight
    F16
    [896, 4864]
  • output_norm.weight
    F32
    [896]