Edit model card

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, 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]
  - sources:
    - 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]
  - sources:
    - model: 152334H/miqu-1-70b-sf
      layer_range: [30,34]
  - sources:
    - model: 152334H/miqu-1-70b-sf
      layer_range: [32,36]
  - sources:
    - model: 152334H/miqu-1-70b-sf
      layer_range: [34,38]
  - sources:
    - model: 152334H/miqu-1-70b-sf
      layer_range: [36,40]
  - sources:
    - model: 152334H/miqu-1-70b-sf
      layer_range: [38,42]
  - sources:
    - 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

Downloads last month: 86

Safetensors

Model size

103B params

Tensor type

FP16

Inference Examples

Text Generation

This model does not have enough activity to be deployed to Inference API (serverless) yet. Increase its social visibility and check back later, or deploy to Inference Endpoints (dedicated) instead.

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

View on Papers With Code