avr/
sfr-embedding-mistral:q4_k_m

29.3K 1 year ago

An embedding model created by Salesforce Research that you can use for semantic search. Currently the best open source embedding model on MTEB.

... /
model
6927cdc0f1f5 · 4.4GB
    Metadata
  • general.architecture
    llama
  • general.file_type
    Q4_K_M
  • llama.attention.head_count
    32
  • llama.attention.head_count_kv
    8
  • llama.attention.layer_norm_rms_epsilon
    1e-05
  • llama.block_count
    32
  • llama.context_length
    32768
  • llama.embedding_length
    4096
  • llama.feed_forward_length
    14336
  • llama.rope.dimension_count
    128
  • llama.rope.freq_base
    10000
  • tokenizer.ggml.add_bos_token
    true
  • tokenizer.ggml.add_eos_token
    false
  • tokenizer.ggml.bos_token_id
    1
  • tokenizer.ggml.eos_token_id
    2
  • tokenizer.ggml.model
    llama
  • tokenizer.ggml.scores
    [0, 0, 0, 0, 0, ...]
  • tokenizer.ggml.token_type
    [2, 3, 3, 6, 6, ...]
  • tokenizer.ggml.tokens
    [<unk>, <s>, </s>, <0x00>, <0x01>, ...]
  • tokenizer.ggml.unknown_token_id
    0
    • Tensor
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    Q4_K
    [4096, 32000]
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    Q4_K
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  • blk.0.attn_norm.weight
    F32
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    Q4_K
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  • 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
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  • blk.0.ffn_gate.weight
    Q4_K
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  • blk.0.ffn_norm.weight
    F32
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  • blk.0.ffn_up.weight
    Q4_K
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    • blk.1
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  • blk.1.attn_norm.weight
    F32
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    Q4_K
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  • blk.1.attn_q.weight
    Q4_K
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  • blk.1.attn_v.weight
    Q6_K
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  • blk.1.ffn_down.weight
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  • blk.1.ffn_gate.weight
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  • blk.1.ffn_norm.weight
    F32
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  • blk.1.ffn_up.weight
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    • blk.2
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    • blk.3
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    [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, 32000]
  • output_norm.weight
    F32
    [4096]