BigWeave Viable
Collection
All viable models from the BigWeave series, excluding quantized versions
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7 items
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Updated
YAML Metadata
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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 v20 110b
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 merge of 152334H/miqu-1-70b-sf and lizpreciatior/lzlv_70b_fp16_hf. By conducting exl2 measurements, we identify the least important layers of lzlv. These least important layers are extended with layers in-between to create longer series of consecutive layers. These slices are then inserted into miqu.
Merge configuration:
slices:
- sources:
- model: 152334H/miqu-1-70b-sf
layer_range: [0, 1]
- model: lizpreciatior/lzlv_70b_fp16_hf
layer_range: [0, 1]
parameters:
weight: 0
- sources:
- model: 152334H/miqu-1-70b-sf
layer_range: [1,26]
- sources:
- model: lizpreciatior/lzlv_70b_fp16_hf
layer_range: [9,44]
- sources:
- model: 152334H/miqu-1-70b-sf
layer_range: [27,52]
- sources:
- model: lizpreciatior/lzlv_70b_fp16_hf
layer_range: [45,60]
- sources:
- model: 152334H/miqu-1-70b-sf
layer_range: [53,79]
- sources:
- model: 152334H/miqu-1-70b-sf
layer_range: [79, 80]
- model: lizpreciatior/lzlv_70b_fp16_hf
layer_range: [79, 80]
parameters:
weight: 0
merge_method: linear
parameters:
weight: 1.0
dtype: float16
tokenizer_source: 152334H/miqu-1-70b-sf
Open LLM Leaderboard Evaluation Results
Detailed results can be found here
| Metric | Value |
|---|---|
| Avg. | 68.03 |
| AI2 Reasoning Challenge (25-Shot) | 68.17 |
| HellaSwag (10-Shot) | 88.54 |
| MMLU (5-Shot) | 70.51 |
| TruthfulQA (0-shot) | 62.47 |
| Winogrande (5-shot) | 82.08 |
| GSM8k (5-shot) | 36.39 |
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Model tree for llmixer/BigWeave-v20-110b
Collection including llmixer/BigWeave-v20-110b
Evaluation results
- normalized accuracy on AI2 Reasoning Challenge (25-Shot)test set Open LLM Leaderboard68.170
- normalized accuracy on HellaSwag (10-Shot)validation set Open LLM Leaderboard88.540
- accuracy on MMLU (5-Shot)test set Open LLM Leaderboard70.510
- mc2 on TruthfulQA (0-shot)validation set Open LLM Leaderboard62.470
- accuracy on Winogrande (5-shot)validation set Open LLM Leaderboard82.080
- accuracy on GSM8k (5-shot)test set Open LLM Leaderboard36.390