Qwen2.5 models are pretrained on Alibaba's latest large-scale dataset, encompassing up to 18 trillion tokens. The model supports up to 128K tokens and has multilingual support.

tools 0.5b 1.5b 3b 7b 14b 32b 72b

2.5M 3 months ago

5c7eda501c76 · 385MB
    Metadata
  • general.architecture
    qwen2
  • general.basename
    Qwen2.5
  • general.file_type
    14
  • general.languages
    [en]
  • general.license
    apache-2.0
  • general.license.link
    https://huggingface.co/Qwen/Qwen2.5-0.5B/blob/main/LICENSE
  • general.name
    Qwen2.5 0.5B
  • general.quantization_version
    2
  • general.size_label
    0.5B
  • general.tags
    [text-generation]
  • general.type
    model
  • 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
    151645
  • 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
  • Name
    Type
    Shape
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    F32
    [128]
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    Q5_0
    [896, 128]
  • blk.0.attn_norm.weight
    F32
    [896]
  • blk.0.attn_output.weight
    Q5_0
    [896, 896]
  • blk.0.attn_q.bias
    F32
    [896]
  • blk.0.attn_q.weight
    Q5_0
    [896, 896]
  • blk.0.attn_v.bias
    F32
    [128]
  • blk.0.attn_v.weight
    Q5_1
    [896, 128]
  • blk.0.ffn_down.weight
    Q5_K
    [4864, 896]
  • blk.0.ffn_gate.weight
    Q5_0
    [896, 4864]
  • blk.0.ffn_norm.weight
    F32
    [896]
  • blk.0.ffn_up.weight
    Q5_0
    [896, 4864]
  • blk.1
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    F32
    [128]
  • blk.1.attn_k.weight
    Q5_0
    [896, 128]
  • blk.1.attn_norm.weight
    F32
    [896]
  • blk.1.attn_output.weight
    Q5_0
    [896, 896]
  • blk.1.attn_q.bias
    F32
    [896]
  • blk.1.attn_q.weight
    Q5_0
    [896, 896]
  • blk.1.attn_v.bias
    F32
    [128]
  • blk.1.attn_v.weight
    Q5_1
    [896, 128]
  • blk.1.ffn_down.weight
    Q5_K
    [4864, 896]
  • blk.1.ffn_gate.weight
    Q5_0
    [896, 4864]
  • blk.1.ffn_norm.weight
    F32
    [896]
  • blk.1.ffn_up.weight
    Q5_0
    [896, 4864]
  • blk.2
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    F32
    [128]
  • blk.2.attn_k.weight
    Q5_0
    [896, 128]
  • blk.2.attn_norm.weight
    F32
    [896]
  • blk.2.attn_output.weight
    Q5_0
    [896, 896]
  • blk.2.attn_q.bias
    F32
    [896]
  • blk.2.attn_q.weight
    Q5_0
    [896, 896]
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    F32
    [128]
  • blk.2.attn_v.weight
    Q5_0
    [896, 128]
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    Q4_K
    [4864, 896]
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    F32
    [896]
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    [128]
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    F32
    [896]
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    Q5_0
    [896, 896]
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    F32
    [896]
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    Q5_0
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    F32
    [128]
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    Q5_0
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    Q4_K
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    Q5_0
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    F32
    [896]
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    Q5_0
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    [128]
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    F32
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    Q5_0
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    Q5_0
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    F32
    [128]
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    Q5_0
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    Q4_K
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    Q5_0
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    F32
    [896]
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    Q5_0
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    [128]
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    F32
    [896]
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    Q5_0
    [896, 896]
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    F32
    [896]
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    Q5_0
    [896, 896]
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    F32
    [128]
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    Q5_0
    [896, 128]
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    Q4_K
    [4864, 896]
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    Q5_0
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    F32
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    Q5_0
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    Q4_K
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    F32
    [896]
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    Q5_0
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    F32
    [128]
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    Q5_0
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    F32
    [896]
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    Q5_0
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    F32
    [896]
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    Q5_0
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    F32
    [128]
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    Q5_0
    [896, 128]
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    Q4_K
    [4864, 896]
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    Q5_0
    [896, 4864]
  • blk.7.ffn_norm.weight
    F32
    [896]
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    Q5_0
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    F32
    [128]
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    F32
    [896]
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    Q5_0
    [896, 896]
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    F32
    [896]
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    Q5_0
    [896, 896]
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    F32
    [128]
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    Q5_0
    [896, 128]
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    Q4_K
    [4864, 896]
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    Q5_0
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    F32
    [896]
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    Q5_0
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    F32
    [128]
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    F32
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    F32
    [128]
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    F32
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    F32
    [128]
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    Q4_K
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    F32
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    Q5_0
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    F32
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  • blk.21.attn_norm.weight
    F32
    [896]
  • blk.21.attn_output.weight
    Q5_0
    [896, 896]
  • blk.21.attn_q.bias
    F32
    [896]
  • blk.21.attn_q.weight
    Q5_0
    [896, 896]
  • blk.21.attn_v.bias
    F32
    [128]
  • blk.21.attn_v.weight
    Q5_0
    [896, 128]
  • blk.21.ffn_down.weight
    Q4_K
    [4864, 896]
  • blk.21.ffn_gate.weight
    Q5_0
    [896, 4864]
  • blk.21.ffn_norm.weight
    F32
    [896]
  • blk.21.ffn_up.weight
    Q5_0
    [896, 4864]
  • blk.22
  • blk.22.attn_k.bias
    F32
    [128]
  • blk.22.attn_k.weight
    Q5_0
    [896, 128]
  • blk.22.attn_norm.weight
    F32
    [896]
  • blk.22.attn_output.weight
    Q5_0
    [896, 896]
  • blk.22.attn_q.bias
    F32
    [896]
  • blk.22.attn_q.weight
    Q5_0
    [896, 896]
  • blk.22.attn_v.bias
    F32
    [128]
  • blk.22.attn_v.weight
    Q5_0
    [896, 128]
  • blk.22.ffn_down.weight
    Q4_K
    [4864, 896]
  • blk.22.ffn_gate.weight
    Q5_0
    [896, 4864]
  • blk.22.ffn_norm.weight
    F32
    [896]
  • blk.22.ffn_up.weight
    Q5_0
    [896, 4864]
  • blk.23
  • blk.23.attn_k.bias
    F32
    [128]
  • blk.23.attn_k.weight
    Q5_0
    [896, 128]
  • blk.23.attn_norm.weight
    F32
    [896]
  • blk.23.attn_output.weight
    Q5_0
    [896, 896]
  • blk.23.attn_q.bias
    F32
    [896]
  • blk.23.attn_q.weight
    Q5_0
    [896, 896]
  • blk.23.attn_v.bias
    F32
    [128]
  • blk.23.attn_v.weight
    Q5_0
    [896, 128]
  • blk.23.ffn_down.weight
    Q4_K
    [4864, 896]
  • blk.23.ffn_gate.weight
    Q5_0
    [896, 4864]
  • blk.23.ffn_norm.weight
    F32
    [896]
  • blk.23.ffn_up.weight
    Q5_0
    [896, 4864]
  • output_norm.weight
    F32
    [896]