DiamondGotCat/
Zeta-1:f16

26 3 months ago

Zeta-1 is a lightweight 405M LLM for conversational tasks.

... /
model
4492fe16b4e6 · 813MB
    Metadata
  • general.architecture
    gptneox
  • general.file_type
    F16
  • gptneox.attention.head_count
    16
  • gptneox.attention.layer_norm_epsilon
    1e-05
  • gptneox.block_count
    24
  • gptneox.context_length
    2048
  • gptneox.embedding_length
    1024
  • gptneox.feed_forward_length
    4096
  • gptneox.rope.dimension_count
    16
  • gptneox.use_parallel_residual
    true
  • tokenizer.ggml.add_bos_token
    false
  • tokenizer.ggml.add_eos_token
    false
  • tokenizer.ggml.bos_token_id
    0
  • tokenizer.ggml.eos_token_id
    0
  • tokenizer.ggml.merges
    [Ġ Ġ, Ġ t, Ġ a, h e, i n, ...]
  • tokenizer.ggml.model
    gpt2
  • tokenizer.ggml.padding_token_id
    0
  • tokenizer.ggml.pre
    olmo
  • tokenizer.ggml.token_type
    [3, 3, 1, 1, 1, ...]
  • tokenizer.ggml.tokens
    [<|endoftext|>, <|padding|>, !, ", #, ...]
  • tokenizer.ggml.unknown_token_id
    0
  • mradermacher.convert_type
    hf
  • mradermacher.quantize_version
    2
  • mradermacher.quantized_at
    2025-04-01T16:05:14+02:00
  • mradermacher.quantized_by
    mradermacher
  • mradermacher.quantized_on
    leia
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  • blk.22.ffn_down.weight
    F16
    [4096, 1024]
  • blk.22.ffn_norm.bias
    F32
    [1024]
  • blk.22.ffn_norm.weight
    F32
    [1024]
  • blk.22.ffn_up.bias
    F32
    [4096]
  • blk.22.ffn_up.weight
    F16
    [1024, 4096]
    • blk.23
  • blk.23.attn_norm.bias
    F32
    [1024]
  • blk.23.attn_norm.weight
    F32
    [1024]
  • blk.23.attn_output.bias
    F32
    [1024]
  • blk.23.attn_output.weight
    F16
    [1024, 1024]
  • blk.23.attn_qkv.bias
    F32
    [3072]
  • blk.23.attn_qkv.weight
    F16
    [1024, 3072]
  • blk.23.ffn_down.bias
    F32
    [1024]
  • blk.23.ffn_down.weight
    F16
    [4096, 1024]
  • blk.23.ffn_norm.bias
    F32
    [1024]
  • blk.23.ffn_norm.weight
    F32
    [1024]
  • blk.23.ffn_up.bias
    F32
    [4096]
  • blk.23.ffn_up.weight
    F16
    [1024, 4096]
  • output.weight
    F16
    [1024, 50304]
  • output_norm.bias
    F32
    [1024]
  • output_norm.weight
    F32
    [1024]