dolphin-mixtral

Uncensored, 8x7b and 8x22b fine-tuned models based on the Mixtral mixture of experts models that excels at coding tasks. Created by Eric Hartford.

8x7b 8x22b

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model
5e241ec72b81 · 27GB
    Metadata
  • general.architecture
    llama
  • general.basename
    dolphin-2.6-mixtral
  • general.dataset.0.name
    Dolphin
  • general.dataset.0.organization
    Ehartford
  • general.dataset.0.repo_url
    https://huggingface.co/ehartford/dolphin
  • general.dataset.1.name
    Airoboros 2.2.1
  • general.dataset.1.organization
    Jondurbin
  • general.dataset.1.repo_url
    https://huggingface.co/jondurbin/airoboros-2.2.1
  • general.dataset.1.version
    2.2.1
  • general.dataset.2.name
    Dolphin Coder
  • general.dataset.2.organization
    Ehartford
  • general.dataset.2.repo_url
    https://huggingface.co/ehartford/dolphin-coder
  • general.dataset.3.name
    Openhermes
  • general.dataset.3.organization
    Teknium
  • general.dataset.3.repo_url
    https://huggingface.co/teknium/openhermes
  • general.dataset.4.name
    Magicoder OSS Instruct 75K
  • general.dataset.4.organization
    Ise Uiuc
  • general.dataset.4.repo_url
    https://huggingface.co/ise-uiuc/Magicoder-OSS-Instruct-75K
  • general.dataset.5.name
    Magicoder Evol Instruct 110K
  • general.dataset.5.organization
    Ise Uiuc
  • general.dataset.5.repo_url
    https://huggingface.co/ise-uiuc/Magicoder-Evol-Instruct-110K
  • general.dataset.6.name
    Capybara
  • general.dataset.6.organization
    LDJnr
  • general.dataset.6.repo_url
    https://huggingface.co/LDJnr/Capybara
  • general.dataset.count
    7
  • general.file_type
    14
  • general.languages
    [en]
  • general.license
    apache-2.0
  • general.name
    Dolphin 2.6 Mixtral 8x7b
  • general.quantization_version
    2
  • general.size_label
    8x7B
  • general.type
    model
  • llama.attention.head_count
    32
  • llama.attention.head_count_kv
    8
  • llama.attention.layer_norm_rms_epsilon
    1e-05
  • llama.block_count
    32
  • llama.context_length
    32768
  • llama.embedding_length
    4096
  • llama.expert_count
    8
  • llama.expert_used_count
    2
  • llama.feed_forward_length
    14336
  • llama.rope.dimension_count
    128
  • llama.rope.freq_base
    1e+06
  • llama.vocab_size
    32002
  • tokenizer.ggml.add_bos_token
    true
  • tokenizer.ggml.add_eos_token
    false
  • tokenizer.ggml.bos_token_id
    1
  • tokenizer.ggml.eos_token_id
    32000
  • tokenizer.ggml.model
    llama
  • tokenizer.ggml.pre
    default
  • tokenizer.ggml.scores
    [-1000, -1000, -1000, 0, 0, ...]
  • tokenizer.ggml.token_type
    [3, 3, 3, 6, 6, ...]
  • tokenizer.ggml.tokens
    [<unk>, <s>, </s>, <0x00>, <0x01>, ...]
    • Tensor
  • Name
    Type
    Shape
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    Q4_K
    [4096, 32002]
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    Q8_0
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  • blk.0.attn_norm.weight
    F32
    [4096]
  • blk.0.attn_output.weight
    Q5_K
    [4096, 4096]
  • blk.0.attn_q.weight
    Q4_K
    [4096, 4096]
  • blk.0.attn_v.weight
    Q8_0
    [4096, 1024]
  • blk.0.ffn_down_exps.weight
    Q5_K
    [14336, 4096, 8]
  • blk.0.ffn_gate_exps.weight
    Q4_K
    [4096, 14336, 8]
  • blk.0.ffn_gate_inp.weight
    F32
    [4096, 8]
  • blk.0.ffn_norm.weight
    F32
    [4096]
  • blk.0.ffn_up_exps.weight
    Q4_K
    [4096, 14336, 8]
    • blk.1
  • blk.1.attn_k.weight
    Q8_0
    [4096, 1024]
  • blk.1.attn_norm.weight
    F32
    [4096]
  • blk.1.attn_output.weight
    Q5_K
    [4096, 4096]
  • blk.1.attn_q.weight
    Q4_K
    [4096, 4096]
  • blk.1.attn_v.weight
    Q8_0
    [4096, 1024]
  • blk.1.ffn_down_exps.weight
    Q5_K
    [14336, 4096, 8]
  • blk.1.ffn_gate_exps.weight
    Q4_K
    [4096, 14336, 8]
  • blk.1.ffn_gate_inp.weight
    F32
    [4096, 8]
  • blk.1.ffn_norm.weight
    F32
    [4096]
  • blk.1.ffn_up_exps.weight
    Q4_K
    [4096, 14336, 8]
    • blk.2
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    Q8_0
    [4096, 1024]
  • blk.2.attn_norm.weight
    F32
    [4096]
  • blk.2.attn_output.weight
    Q5_K
    [4096, 4096]
  • blk.2.attn_q.weight
    Q4_K
    [4096, 4096]
  • blk.2.attn_v.weight
    Q8_0
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  • blk.2.ffn_down_exps.weight
    Q5_K
    [14336, 4096, 8]
  • blk.2.ffn_gate_exps.weight
    Q4_K
    [4096, 14336, 8]
  • blk.2.ffn_gate_inp.weight
    F32
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  • blk.2.ffn_norm.weight
    F32
    [4096]
  • blk.2.ffn_up_exps.weight
    Q4_K
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    • blk.3
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    Q8_0
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  • blk.3.attn_norm.weight
    F32
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  • blk.3.attn_output.weight
    Q5_K
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  • blk.3.attn_q.weight
    Q4_K
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  • blk.3.attn_v.weight
    Q8_0
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    Q5_K
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  • blk.3.ffn_gate_exps.weight
    Q4_K
    [4096, 14336, 8]
  • blk.3.ffn_gate_inp.weight
    F32
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  • blk.3.ffn_norm.weight
    F32
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  • blk.3.ffn_up_exps.weight
    Q4_K
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    Q8_0
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  • blk.4.attn_norm.weight
    F32
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    Q5_K
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    Q4_K
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  • blk.4.attn_v.weight
    Q8_0
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    Q4_K
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  • blk.4.ffn_gate_exps.weight
    Q4_K
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  • blk.4.ffn_gate_inp.weight
    F32
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  • blk.4.ffn_norm.weight
    F32
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  • blk.4.ffn_up_exps.weight
    Q4_K
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    • blk.5
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    Q8_0
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  • blk.5.attn_norm.weight
    F32
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  • blk.5.attn_output.weight
    Q5_K
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  • blk.5.attn_q.weight
    Q4_K
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  • blk.5.attn_v.weight
    Q8_0
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  • blk.5.ffn_down_exps.weight
    Q4_K
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  • blk.5.ffn_gate_exps.weight
    Q4_K
    [4096, 14336, 8]
  • blk.5.ffn_gate_inp.weight
    F32
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  • blk.5.ffn_norm.weight
    F32
    [4096]
  • blk.5.ffn_up_exps.weight
    Q4_K
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    Q8_0
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    F32
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    Q5_K
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    Q4_K
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  • blk.6.attn_v.weight
    Q8_0
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    Q4_K
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    Q4_K
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    F32
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    Q4_K
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    Q8_0
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    Q4_K
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    Q4_K
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  • blk.7.ffn_gate_inp.weight
    F32
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  • blk.7.ffn_norm.weight
    F32
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    Q4_K
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    Q8_0
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    F32
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    Q5_K
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  • blk.8.attn_q.weight
    Q4_K
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  • blk.8.attn_v.weight
    Q8_0
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  • blk.8.ffn_down_exps.weight
    Q4_K
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  • blk.8.ffn_gate_exps.weight
    Q4_K
    [4096, 14336, 8]
  • blk.8.ffn_gate_inp.weight
    F32
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  • blk.8.ffn_norm.weight
    F32
    [4096]
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    Q4_K
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    Q8_0
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    F32
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    Q4_K
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    Q8_0
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    Q4_K
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    Q4_K
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    Q8_0
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    Q4_K
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    F32
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    F32
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    F32
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    Q4_K
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    F32
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    Q4_K
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    F32
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    F32
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    Q4_K
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    F32
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    Q4_K
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    Q4_K
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    F32
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    F32
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    Q4_K
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    F32
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    F32
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    F32
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    Q4_K
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    Q8_0
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    F32
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    F32
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    F32
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    Q4_K
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    F32
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    F32
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    F32
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    F32
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    F32
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    F32
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