TCYZ/ emotions2:8m

1 2 days ago

TCYZ'nin 2. Duyguları analiz eden modeli.

8m
... /
model
b7dff8ca443d · 34MB
    Metadata
  • general.architecture
    llama
  • general.file_type
    F32
  • llama.attention.head_count
    8
  • llama.attention.head_count_kv
    8
  • llama.attention.layer_norm_rms_epsilon
    1e-06
  • llama.block_count
    8
  • llama.context_length
    128
  • llama.embedding_length
    256
  • llama.feed_forward_length
    1024
  • llama.rope.dimension_count
    32
  • llama.rope.freq_base
    10000
  • tokenizer.ggml.bos_token_id
    1
  • tokenizer.ggml.eos_token_id
    2
  • tokenizer.ggml.merges
    []
  • tokenizer.ggml.model
    gpt2
  • tokenizer.ggml.padding_token_id
    0
  • tokenizer.ggml.scores
    [0, 0, 0, 0, 0, ...]
  • tokenizer.ggml.token_type
    [3, 3, 3, 3, 1, ...]
  • tokenizer.ggml.tokens
    [<pad>, <bos>, <eos>, <unk>, Ġ, ...]
  • emotion.labels
    [iğrenmiş, korkmuş, kızgın, mutlu, üzgün, ...]
  • emotion.prompt_format
    duygu: {label}:
    • Tensor
  • token_embd.weight
    F32
    [256, 51]
    • blk.0
  • blk.0.attn_k.weight
    F32
    [256, 256]
  • blk.0.attn_norm.weight
    F32
    [256]
  • blk.0.attn_output.weight
    F32
    [256, 256]
  • blk.0.attn_q.weight
    F32
    [256, 256]
  • blk.0.attn_v.weight
    F32
    [256, 256]
  • blk.0.ffn_down.weight
    F32
    [1024, 256]
  • blk.0.ffn_gate.weight
    F32
    [256, 1024]
  • blk.0.ffn_norm.weight
    F32
    [256]
  • blk.0.ffn_up.weight
    F32
    [256, 1024]
    • blk.1
  • blk.1.attn_k.weight
    F32
    [256, 256]
  • blk.1.attn_norm.weight
    F32
    [256]
  • blk.1.attn_output.weight
    F32
    [256, 256]
  • blk.1.attn_q.weight
    F32
    [256, 256]
  • blk.1.attn_v.weight
    F32
    [256, 256]
  • blk.1.ffn_down.weight
    F32
    [1024, 256]
  • blk.1.ffn_gate.weight
    F32
    [256, 1024]
  • blk.1.ffn_norm.weight
    F32
    [256]
  • blk.1.ffn_up.weight
    F32
    [256, 1024]
    • blk.2
  • blk.2.attn_k.weight
    F32
    [256, 256]
  • blk.2.attn_norm.weight
    F32
    [256]
  • blk.2.attn_output.weight
    F32
    [256, 256]
  • blk.2.attn_q.weight
    F32
    [256, 256]
  • blk.2.attn_v.weight
    F32
    [256, 256]
  • blk.2.ffn_down.weight
    F32
    [1024, 256]
  • blk.2.ffn_gate.weight
    F32
    [256, 1024]
  • blk.2.ffn_norm.weight
    F32
    [256]
  • blk.2.ffn_up.weight
    F32
    [256, 1024]
    • blk.3
  • blk.3.attn_k.weight
    F32
    [256, 256]
  • blk.3.attn_norm.weight
    F32
    [256]
  • blk.3.attn_output.weight
    F32
    [256, 256]
  • blk.3.attn_q.weight
    F32
    [256, 256]
  • blk.3.attn_v.weight
    F32
    [256, 256]
  • blk.3.ffn_down.weight
    F32
    [1024, 256]
  • blk.3.ffn_gate.weight
    F32
    [256, 1024]
  • blk.3.ffn_norm.weight
    F32
    [256]
  • blk.3.ffn_up.weight
    F32
    [256, 1024]
    • blk.4
  • blk.4.attn_k.weight
    F32
    [256, 256]
  • blk.4.attn_norm.weight
    F32
    [256]
  • blk.4.attn_output.weight
    F32
    [256, 256]
  • blk.4.attn_q.weight
    F32
    [256, 256]
  • blk.4.attn_v.weight
    F32
    [256, 256]
  • blk.4.ffn_down.weight
    F32
    [1024, 256]
  • blk.4.ffn_gate.weight
    F32
    [256, 1024]
  • blk.4.ffn_norm.weight
    F32
    [256]
  • blk.4.ffn_up.weight
    F32
    [256, 1024]
    • blk.5
  • blk.5.attn_k.weight
    F32
    [256, 256]
  • blk.5.attn_norm.weight
    F32
    [256]
  • blk.5.attn_output.weight
    F32
    [256, 256]
  • blk.5.attn_q.weight
    F32
    [256, 256]
  • blk.5.attn_v.weight
    F32
    [256, 256]
  • blk.5.ffn_down.weight
    F32
    [1024, 256]
  • blk.5.ffn_gate.weight
    F32
    [256, 1024]
  • blk.5.ffn_norm.weight
    F32
    [256]
  • blk.5.ffn_up.weight
    F32
    [256, 1024]
    • blk.6
  • blk.6.attn_k.weight
    F32
    [256, 256]
  • blk.6.attn_norm.weight
    F32
    [256]
  • blk.6.attn_output.weight
    F32
    [256, 256]
  • blk.6.attn_q.weight
    F32
    [256, 256]
  • blk.6.attn_v.weight
    F32
    [256, 256]
  • blk.6.ffn_down.weight
    F32
    [1024, 256]
  • blk.6.ffn_gate.weight
    F32
    [256, 1024]
  • blk.6.ffn_norm.weight
    F32
    [256]
  • blk.6.ffn_up.weight
    F32
    [256, 1024]
    • blk.7
  • blk.7.attn_k.weight
    F32
    [256, 256]
  • blk.7.attn_norm.weight
    F32
    [256]
  • blk.7.attn_output.weight
    F32
    [256, 256]
  • blk.7.attn_q.weight
    F32
    [256, 256]
  • blk.7.attn_v.weight
    F32
    [256, 256]
  • blk.7.ffn_down.weight
    F32
    [1024, 256]
  • blk.7.ffn_gate.weight
    F32
    [256, 1024]
  • blk.7.ffn_norm.weight
    F32
    [256]
  • blk.7.ffn_up.weight
    F32
    [256, 1024]
  • output.weight
    F32
    [256, 51]
  • output_norm.weight
    F32
    [256]