JollyLlama/
Megrez-3B-Instruct:Q8_0

81 7 months ago

Megrez-3B-Instruct is a large language model trained by Infinigence AI. Megrez-3B aims to provide a fast inference, compact, and powerful edge-side intelligent solution through software-hardware co-design.

... /
model
cc41d24d1c43 · 3.1GB
    Metadata
  • general.architecture
    llama
  • general.file_type
    Q8_0
  • llama.attention.head_count
    40
  • llama.attention.head_count_kv
    10
  • llama.attention.layer_norm_rms_epsilon
    1e-05
  • llama.block_count
    32
  • llama.context_length
    32768
  • llama.embedding_length
    2560
  • llama.feed_forward_length
    7168
  • llama.rope.dimension_count
    64
  • llama.rope.freq_base
    5e+06
  • llama.vocab_size
    122880
  • tokenizer.ggml.add_bos_token
    false
  • tokenizer.ggml.eos_token_id
    120005
  • tokenizer.ggml.merges
    [Ġ Ġ, ĠĠ ĠĠ, Ġ t, i n, Ġ a, ...]
  • tokenizer.ggml.model
    gpt2
  • tokenizer.ggml.padding_token_id
    120002
  • tokenizer.ggml.pre
    megrez
  • tokenizer.ggml.token_type
    [1, 1, 1, 1, 1, ...]
  • tokenizer.ggml.tokens
    [!, ", #, $, %, ...]
  • quantize.imatrix.chunks_count
    133
  • quantize.imatrix.dataset
    /training_dir/calibration_datav3.txt
  • quantize.imatrix.entries_count
    224
  • quantize.imatrix.file
    /models_out/Megrez-3B-Instruct-GGUF/Megrez-3B-Instruct.imatrix
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    Q8_0
    [2560, 640]
  • blk.26.ffn_down.weight
    Q8_0
    [7168, 2560]
  • blk.26.ffn_gate.weight
    Q8_0
    [2560, 7168]
  • blk.26.ffn_norm.weight
    F32
    [2560]
  • blk.26.ffn_up.weight
    Q8_0
    [2560, 7168]
    • blk.27
  • blk.27.attn_k.weight
    Q8_0
    [2560, 640]
  • blk.27.attn_norm.weight
    F32
    [2560]
  • blk.27.attn_output.weight
    Q8_0
    [2560, 2560]
  • blk.27.attn_q.weight
    Q8_0
    [2560, 2560]
  • blk.27.attn_v.weight
    Q8_0
    [2560, 640]
  • blk.27.ffn_down.weight
    Q8_0
    [7168, 2560]
  • blk.27.ffn_gate.weight
    Q8_0
    [2560, 7168]
  • blk.27.ffn_norm.weight
    F32
    [2560]
  • blk.27.ffn_up.weight
    Q8_0
    [2560, 7168]
    • blk.28
  • blk.28.attn_k.weight
    Q8_0
    [2560, 640]
  • blk.28.attn_norm.weight
    F32
    [2560]
  • blk.28.attn_output.weight
    Q8_0
    [2560, 2560]
  • blk.28.attn_q.weight
    Q8_0
    [2560, 2560]
  • blk.28.attn_v.weight
    Q8_0
    [2560, 640]
  • blk.28.ffn_down.weight
    Q8_0
    [7168, 2560]
  • blk.28.ffn_gate.weight
    Q8_0
    [2560, 7168]
  • blk.28.ffn_norm.weight
    F32
    [2560]
  • blk.28.ffn_up.weight
    Q8_0
    [2560, 7168]
    • blk.29
  • blk.29.attn_k.weight
    Q8_0
    [2560, 640]
  • blk.29.attn_norm.weight
    F32
    [2560]
  • blk.29.attn_output.weight
    Q8_0
    [2560, 2560]
  • blk.29.attn_q.weight
    Q8_0
    [2560, 2560]
  • blk.29.attn_v.weight
    Q8_0
    [2560, 640]
  • blk.29.ffn_down.weight
    Q8_0
    [7168, 2560]
  • blk.29.ffn_gate.weight
    Q8_0
    [2560, 7168]
  • blk.29.ffn_norm.weight
    F32
    [2560]
  • blk.29.ffn_up.weight
    Q8_0
    [2560, 7168]
    • blk.30
  • blk.30.attn_k.weight
    Q8_0
    [2560, 640]
  • blk.30.attn_norm.weight
    F32
    [2560]
  • blk.30.attn_output.weight
    Q8_0
    [2560, 2560]
  • blk.30.attn_q.weight
    Q8_0
    [2560, 2560]
  • blk.30.attn_v.weight
    Q8_0
    [2560, 640]
  • blk.30.ffn_down.weight
    Q8_0
    [7168, 2560]
  • blk.30.ffn_gate.weight
    Q8_0
    [2560, 7168]
  • blk.30.ffn_norm.weight
    F32
    [2560]
  • blk.30.ffn_up.weight
    Q8_0
    [2560, 7168]
    • blk.31
  • blk.31.attn_k.weight
    Q8_0
    [2560, 640]
  • blk.31.attn_norm.weight
    F32
    [2560]
  • blk.31.attn_output.weight
    Q8_0
    [2560, 2560]
  • blk.31.attn_q.weight
    Q8_0
    [2560, 2560]
  • blk.31.attn_v.weight
    Q8_0
    [2560, 640]
  • blk.31.ffn_down.weight
    Q8_0
    [7168, 2560]
  • blk.31.ffn_gate.weight
    Q8_0
    [2560, 7168]
  • blk.31.ffn_norm.weight
    F32
    [2560]
  • blk.31.ffn_up.weight
    Q8_0
    [2560, 7168]
  • output.weight
    Q8_0
    [2560, 122880]
  • output_norm.weight
    F32
    [2560]