mistral-large

Mistral Large 2 is Mistral's new flagship model that is significantly more capable in code generation, mathematics, and reasoning with 128k context window and support for dozens of languages.

tools 123b

110.8K 4 weeks ago

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model
914093065095 · 45GB
    Metadata
  • general.architecture
    llama
  • general.basename
    Mistral
  • general.file_type
    10
  • general.finetune
    Instruct
  • general.languages
    [en, fr, de, es, it, ...]
  • general.license
    other
  • general.license.link
    https://mistral.ai/licenses/MRL-0.1.md
  • general.license.name
    mrl
  • general.name
    Mistral Large Instruct 2411
  • general.quantization_version
    2
  • general.size_label
    Large
  • general.type
    model
  • general.version
    2411
  • llama.attention.head_count
    96
  • 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
    88
  • llama.context_length
    131072
  • llama.embedding_length
    12288
  • llama.feed_forward_length
    28672
  • llama.rope.dimension_count
    128
  • llama.rope.freq_base
    1e+06
  • llama.vocab_size
    32768
  • tokenizer.ggml.add_bos_token
    true
  • tokenizer.ggml.add_eos_token
    false
  • tokenizer.ggml.add_space_prefix
    false
  • tokenizer.ggml.bos_token_id
    1
  • tokenizer.ggml.eos_token_id
    2
  • tokenizer.ggml.model
    llama
  • tokenizer.ggml.pre
    default
  • tokenizer.ggml.scores
    [-1000, -1000, -1000, -1000, -1000, ...]
  • tokenizer.ggml.token_type
    [3, 3, 3, 3, 3, ...]
  • tokenizer.ggml.tokens
    [<unk>, <s>, </s>, [INST], [/INST], ...]
  • tokenizer.ggml.unknown_token_id
    0
    • Tensor
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    Type
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    Q2_K
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    Q3_K
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    Q2_K
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    Q3_K
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  • blk.83.ffn_down.weight
    Q3_K
    [28672, 12288]
  • blk.83.ffn_gate.weight
    Q2_K
    [12288, 28672]
  • blk.83.ffn_norm.weight
    F32
    [12288]
  • blk.83.ffn_up.weight
    Q2_K
    [12288, 28672]
    • blk.84
  • blk.84.attn_k.weight
    Q2_K
    [12288, 1024]
  • blk.84.attn_norm.weight
    F32
    [12288]
  • blk.84.attn_output.weight
    Q3_K
    [12288, 12288]
  • blk.84.attn_q.weight
    Q2_K
    [12288, 12288]
  • blk.84.attn_v.weight
    Q4_K
    [12288, 1024]
  • blk.84.ffn_down.weight
    Q3_K
    [28672, 12288]
  • blk.84.ffn_gate.weight
    Q2_K
    [12288, 28672]
  • blk.84.ffn_norm.weight
    F32
    [12288]
  • blk.84.ffn_up.weight
    Q2_K
    [12288, 28672]
    • blk.85
  • blk.85.attn_k.weight
    Q2_K
    [12288, 1024]
  • blk.85.attn_norm.weight
    F32
    [12288]
  • blk.85.attn_output.weight
    Q3_K
    [12288, 12288]
  • blk.85.attn_q.weight
    Q2_K
    [12288, 12288]
  • blk.85.attn_v.weight
    Q4_K
    [12288, 1024]
  • blk.85.ffn_down.weight
    Q3_K
    [28672, 12288]
  • blk.85.ffn_gate.weight
    Q2_K
    [12288, 28672]
  • blk.85.ffn_norm.weight
    F32
    [12288]
  • blk.85.ffn_up.weight
    Q2_K
    [12288, 28672]
    • blk.86
  • blk.86.attn_k.weight
    Q2_K
    [12288, 1024]
  • blk.86.attn_norm.weight
    F32
    [12288]
  • blk.86.attn_output.weight
    Q3_K
    [12288, 12288]
  • blk.86.attn_q.weight
    Q2_K
    [12288, 12288]
  • blk.86.attn_v.weight
    Q4_K
    [12288, 1024]
  • blk.86.ffn_down.weight
    Q3_K
    [28672, 12288]
  • blk.86.ffn_gate.weight
    Q2_K
    [12288, 28672]
  • blk.86.ffn_norm.weight
    F32
    [12288]
  • blk.86.ffn_up.weight
    Q2_K
    [12288, 28672]
    • blk.87
  • blk.87.attn_k.weight
    Q2_K
    [12288, 1024]
  • blk.87.attn_norm.weight
    F32
    [12288]
  • blk.87.attn_output.weight
    Q3_K
    [12288, 12288]
  • blk.87.attn_q.weight
    Q2_K
    [12288, 12288]
  • blk.87.attn_v.weight
    Q4_K
    [12288, 1024]
  • blk.87.ffn_down.weight
    Q3_K
    [28672, 12288]
  • blk.87.ffn_gate.weight
    Q2_K
    [12288, 28672]
  • blk.87.ffn_norm.weight
    F32
    [12288]
  • blk.87.ffn_up.weight
    Q2_K
    [12288, 28672]
  • output.weight
    Q6_K
    [12288, 32768]
  • output_norm.weight
    F32
    [12288]