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
461.3K Pulls Updated 10 days ago
5041ba427842 · 26GB
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general.architecturellama
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general.basenamedolphin-2.7-mixtral
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general.dataset.0.nameDolphin
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general.dataset.0.organizationCognitivecomputations
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general.dataset.0.repo_urlhttps://huggingface.co/cognitivecomputations/dolphin
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general.dataset.1.nameAiroboros 2.2.1
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general.dataset.1.organizationJondurbin
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general.dataset.1.repo_urlhttps://huggingface.co/jondurbin/airoboros-2.2.1
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general.dataset.1.version2.2.1
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general.dataset.2.nameDolphin Coder
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general.dataset.2.organizationCognitivecomputations
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general.dataset.2.repo_urlhttps://huggingface.co/cognitivecomputations/dolphin-coder
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general.dataset.3.nameOpenhermes
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general.dataset.3.organizationTeknium
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general.dataset.3.repo_urlhttps://huggingface.co/teknium/openhermes
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general.dataset.4.nameMagicoder OSS Instruct 75K
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general.dataset.4.organizationIse Uiuc
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general.dataset.4.repo_urlhttps://huggingface.co/ise-uiuc/Magicoder-OSS-Instruct-75K
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general.dataset.5.nameMagicoder Evol Instruct 110K
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general.dataset.5.organizationIse Uiuc
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general.dataset.5.repo_urlhttps://huggingface.co/ise-uiuc/Magicoder-Evol-Instruct-110K
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general.dataset.6.nameCapybara
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general.dataset.6.organizationLDJnr
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general.dataset.6.repo_urlhttps://huggingface.co/LDJnr/Capybara
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general.dataset.count7
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general.file_type2
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general.languages[en]
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general.licenseapache-2.0
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general.nameDolphin 2.7 Mixtral 8x7b
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general.quantization_version2
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general.size_label8x7B
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general.typemodel
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llama.attention.head_count32
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llama.attention.head_count_kv8
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llama.attention.layer_norm_rms_epsilon1e-05
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llama.block_count32
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llama.context_length32768
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llama.embedding_length4096
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llama.expert_count8
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llama.expert_used_count2
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llama.feed_forward_length14336
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llama.rope.dimension_count128
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llama.rope.freq_base1e+06
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llama.vocab_size32002
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tokenizer.ggml.add_bos_tokentrue
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tokenizer.ggml.add_eos_tokenfalse
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tokenizer.ggml.bos_token_id1
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tokenizer.ggml.modelllama
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tokenizer.ggml.predefault
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tokenizer.ggml.scores[-1000, -1000, -1000, 0, 0, ...]
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tokenizer.ggml.token_type[3, 3, 3, 6, 6, ...]
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tokenizer.ggml.tokens[<unk>, <s>, </s>, <0x00>, <0x01>, ...]
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NameTypeShape
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blk.27.attn_k.weightQ8_0[4096, 1024]
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blk.27.attn_norm.weightF32[4096]
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blk.27.attn_output.weightQ4_0[4096, 4096]
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blk.27.attn_q.weightQ4_0[4096, 4096]
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blk.27.attn_v.weightQ8_0[4096, 1024]
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blk.27.ffn_down_exps.weightQ4_0[14336, 4096, 8]
-
blk.27.ffn_gate_exps.weightQ4_0[4096, 14336, 8]
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blk.27.ffn_gate_inp.weightF32[4096, 8]
-
blk.27.ffn_norm.weightF32[4096]
-
blk.27.ffn_up_exps.weightQ4_0[4096, 14336, 8]
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blk.28.attn_k.weightQ8_0[4096, 1024]
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blk.28.attn_norm.weightF32[4096]
-
blk.28.attn_output.weightQ4_0[4096, 4096]
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blk.28.attn_q.weightQ4_0[4096, 4096]
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blk.28.attn_v.weightQ8_0[4096, 1024]
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blk.28.ffn_down_exps.weightQ4_0[14336, 4096, 8]
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blk.28.ffn_gate_exps.weightQ4_0[4096, 14336, 8]
-
blk.28.ffn_gate_inp.weightF32[4096, 8]
-
blk.28.ffn_norm.weightF32[4096]
-
blk.28.ffn_up_exps.weightQ4_0[4096, 14336, 8]
-
blk.29.attn_k.weightQ8_0[4096, 1024]
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blk.29.attn_norm.weightF32[4096]
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blk.29.attn_output.weightQ4_0[4096, 4096]
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blk.29.attn_q.weightQ4_0[4096, 4096]
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blk.29.attn_v.weightQ8_0[4096, 1024]
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blk.29.ffn_down_exps.weightQ4_0[14336, 4096, 8]
-
blk.29.ffn_gate_exps.weightQ4_0[4096, 14336, 8]
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blk.29.ffn_gate_inp.weightF32[4096, 8]
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blk.29.ffn_norm.weightF32[4096]
-
blk.29.ffn_up_exps.weightQ4_0[4096, 14336, 8]
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blk.30.attn_k.weightQ8_0[4096, 1024]
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blk.30.attn_norm.weightF32[4096]
-
blk.30.attn_output.weightQ4_0[4096, 4096]
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blk.30.attn_q.weightQ4_0[4096, 4096]
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blk.30.attn_v.weightQ8_0[4096, 1024]
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blk.30.ffn_down_exps.weightQ4_0[14336, 4096, 8]
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blk.30.ffn_gate_exps.weightQ4_0[4096, 14336, 8]
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blk.30.ffn_gate_inp.weightF32[4096, 8]
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blk.30.ffn_norm.weightF32[4096]
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blk.30.ffn_up_exps.weightQ4_0[4096, 14336, 8]
-
blk.31.attn_k.weightQ8_0[4096, 1024]
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blk.31.attn_norm.weightF32[4096]
-
blk.31.attn_output.weightQ4_0[4096, 4096]
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blk.31.attn_q.weightQ4_0[4096, 4096]
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blk.31.attn_v.weightQ8_0[4096, 1024]
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blk.31.ffn_down_exps.weightQ4_0[14336, 4096, 8]
-
blk.31.ffn_gate_exps.weightQ4_0[4096, 14336, 8]
-
blk.31.ffn_gate_inp.weightF32[4096, 8]
-
blk.31.ffn_norm.weightF32[4096]
-
blk.31.ffn_up_exps.weightQ4_0[4096, 14336, 8]
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output.weightQ6_K[4096, 32002]
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output_norm.weightF32[4096]
Metadata
Tensor
blk.0
blk.1
blk.2
blk.3
blk.4
blk.5
blk.6
blk.7
blk.8
blk.9
blk.10
blk.11
blk.12
blk.13
blk.14
blk.15
blk.16
blk.17
blk.18
blk.19
blk.20
blk.21
blk.22
blk.23
blk.24
blk.25
blk.26
blk.27
blk.28
blk.29
blk.30
blk.31