vanilj/smaug-llama-3-70b-instruct

This model was built using a new Smaug recipe for improving performance on real world multi-turn conversations applied to meta-llama/Meta-Llama-3-70B-Instruct.

519 7 months ago

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model
f5f93e3d8573 · 43GB
    Metadata
  • general.architecture
    llama
  • general.file_type
    15
  • general.name
    Smaug-Llama-3-70B-Instruct
  • general.quantization_version
    2
  • general.source.url
    https://huggingface.co/abacusai/Smaug-Llama-3-70B-Instruct
  • general.url
    https://huggingface.co/mradermacher/Smaug-Llama-3-70B-Instruct-GGUF
  • llama.attention.head_count
    64
  • llama.attention.head_count_kv
    8
  • llama.attention.layer_norm_rms_epsilon
    1e-05
  • llama.block_count
    80
  • llama.context_length
    8192
  • llama.embedding_length
    8192
  • llama.feed_forward_length
    28672
  • llama.rope.dimension_count
    128
  • llama.rope.freq_base
    500000
  • llama.vocab_size
    128256
  • mradermacher.convert_type
    hfhfix
  • mradermacher.quantize_version
    2
  • mradermacher.quantized_at
    2024-05-19T14:30:34+02:00
  • mradermacher.quantized_by
    mradermacher
  • mradermacher.quantized_on
    db3
  • tokenizer.ggml.bos_token_id
    128000
  • tokenizer.ggml.eos_token_id
    128001
  • tokenizer.ggml.merges
    [Ġ Ġ, Ġ ĠĠĠ, ĠĠ ĠĠ, ĠĠĠ Ġ, i n, ...]
  • tokenizer.ggml.model
    gpt2
  • tokenizer.ggml.padding_token_id
    128001
  • tokenizer.ggml.pre
    llama3
  • tokenizer.ggml.token_type
    [1, 1, 1, 1, 1, ...]
  • tokenizer.ggml.tokens
    [!, ", #, $, %, ...]
    • Tensor
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    Q6_K
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    Q6_K
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    F32
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    F32
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    Q4_K
    [8192, 8192]
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    Q4_K
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    Q6_K
    [8192, 1024]
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    Q6_K
    [28672, 8192]
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    Q4_K
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  • blk.1.ffn_norm.weight
    F32
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    Q4_K
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  • blk.2.attn_norm.weight
    F32
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    Q4_K
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    Q4_K
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    Q6_K
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    Q6_K
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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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    Q4_K
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    Q4_K
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    Q6_K
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    Q4_K
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    Q6_K
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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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    F32
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    F32
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