cogito

Cogito v1 Preview is a family of hybrid reasoning models by Deep Cogito that outperform the best available open models of the same size, including counterparts from LLaMA, DeepSeek, and Qwen across most standard benchmarks.

tools 3b 8b 14b 32b 70b

39.3K 13 days ago

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model
75e23a78e375 · 4.9GB
    Metadata
  • general.architecture
    llama
  • general.file_type
    Q4_K_M
  • llama.attention.head_count
    32
  • llama.attention.head_count_kv
    8
  • llama.attention.key_length
    128
  • llama.attention.layer_norm_rms_epsilon
    1e-05
  • llama.attention.value_length
    128
  • llama.block_count
    32
  • llama.context_length
    131072
  • llama.embedding_length
    4096
  • llama.feed_forward_length
    14336
  • llama.rope.dimension_count
    128
  • llama.rope.freq_base
    500000
  • llama.vocab_size
    128256
  • tokenizer.ggml.bos_token_id
    128000
  • tokenizer.ggml.eos_token_id
    128009
  • tokenizer.ggml.merges
    [Ġ Ġ, Ġ ĠĠĠ, ĠĠ ĠĠ, ĠĠĠ Ġ, i n, ...]
  • tokenizer.ggml.model
    gpt2
  • tokenizer.ggml.pre
    llama-bpe
  • tokenizer.ggml.token_type
    [1, 1, 1, 1, 1, ...]
  • tokenizer.ggml.tokens
    [!, ", #, $, %, ...]
    • Tensor
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    [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, 128256]
  • rope_freqs.weight
    F32
    [64]
  • output_norm.weight
    F32
    [4096]