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YAML Metadata Warning: The pipeline tag "conversational" is not in the official list: text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, feature-extraction, text-generation, text2text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, audio-text-to-text, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-retrieval, time-series-forecasting, text-to-video, image-text-to-text, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, image-feature-extraction, video-text-to-text, keypoint-detection, any-to-any, other

BigWeave v15 103b

The BigWeave models aim to experimentally identify merge settings for increasing model performance. The version number merely tracks various attempts and is not a quality indicator. Only results demonstrating good performance are retained and shared.

Prompting Format

Mistral, Vicuna and Alpaca.

Merge process

This is a self-merge of 152334H/miqu-1-70b-sf. By conducting exl2 measurements, we identify the most relevant layers. These layers are then duplicated in pairs to ensure overlaps.

Merge configuration:

slices:
  - sources:
    - model: 152334H/miqu-1-70b-sf
      layer_range: [0,3]
  - sources:
    - model: 152334H/miqu-1-70b-sf
      layer_range: [1,5]
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    - model: 152334H/miqu-1-70b-sf
      layer_range: [3,7]
  - sources:
    - model: 152334H/miqu-1-70b-sf
      layer_range: [5,9]
  - sources:
    - model: 152334H/miqu-1-70b-sf
      layer_range: [7,18]
  - sources:
    - model: 152334H/miqu-1-70b-sf
      layer_range: [16,21]
  - sources:
    - model: 152334H/miqu-1-70b-sf
      layer_range: [19,27]
  - sources:
    - model: 152334H/miqu-1-70b-sf
      layer_range: [25,30]
  - sources:
    - model: 152334H/miqu-1-70b-sf
      layer_range: [28,32]
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    - model: 152334H/miqu-1-70b-sf
      layer_range: [30,34]
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    - model: 152334H/miqu-1-70b-sf
      layer_range: [32,36]
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    - model: 152334H/miqu-1-70b-sf
      layer_range: [34,38]
  - sources:
    - model: 152334H/miqu-1-70b-sf
      layer_range: [36,40]
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    - model: 152334H/miqu-1-70b-sf
      layer_range: [38,42]
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    - model: 152334H/miqu-1-70b-sf
      layer_range: [40,44]
  - sources:
    - model: 152334H/miqu-1-70b-sf
      layer_range: [42,46]
  - sources:
    - model: 152334H/miqu-1-70b-sf
      layer_range: [44,48]
  - sources:
    - model: 152334H/miqu-1-70b-sf
      layer_range: [46,51]
  - sources:
    - model: 152334H/miqu-1-70b-sf
      layer_range: [49,77]
  - sources:
    - model: 152334H/miqu-1-70b-sf
      layer_range: [75,79]
  - sources:
    - model: 152334H/miqu-1-70b-sf
      layer_range: [77,80]
merge_method: passthrough
dtype: float16

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	71.67
AI2 Reasoning Challenge (25-Shot)	69.71
HellaSwag (10-Shot)	86.41
MMLU (5-Shot)	71.25
TruthfulQA (0-shot)	66.10
Winogrande (5-shot)	80.35
GSM8k (5-shot)	56.18

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Safetensors

Model size

103B params

Tensor type

FP16

Inference Examples

Text Generation

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Model tree for llmixer/BigWeave-v15-103b

Quantizations

2 models

Collection including llmixer/BigWeave-v15-103b

BigWeave Viable

Collection

All viable models from the BigWeave series, excluding quantized versions • 7 items • Updated Feb 15

Evaluation results

normalized accuracy on AI2 Reasoning Challenge (25-Shot)
test set Open LLM Leaderboard

69.710
normalized accuracy on HellaSwag (10-Shot)
validation set Open LLM Leaderboard

86.410
accuracy on MMLU (5-Shot)
test set Open LLM Leaderboard

71.250
mc2 on TruthfulQA (0-shot)
validation set Open LLM Leaderboard

66.100
accuracy on Winogrande (5-shot)
validation set Open LLM Leaderboard

80.350
accuracy on GSM8k (5-shot)
test set Open LLM Leaderboard

56.180

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