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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, 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, other

BigWeave v12 90B

The BigWeave models aim to identify merge settings equaling or surpassing the performance of Goliath-120b. The version number merely tracks various attempts and is not a quality indicator. Only results demonstrating good performance are retained and shared.

This version is a DARE-TIES merge of two passthrough merges: Xwin-LM-70b-v0.1 + Euryale-1.3-70b (BigWeave v6) and Platypus2-70b-instruct + WinterGoddess-1.4x-70b (BigWeave v8). Both models individually show strong performance, and the merged model achieves even lower perplexity than each model separately.

The 90b size allows for 4bit quants to fit into 48GB of VRAM.

Prompting Format

Vicuna and Alpaca.

Merge process

The models used in the merge are Xwin-LM-70b-v0.1, Euryale-1.3-70b, Platypus2-70b-instruct and WinterGoddess-1.4x-70b.

Merge configuration: ``` slices: - sources: - model: Xwin-LM/Xwin-LM-70B-V0.1 layer_range: [0,12] - sources: - model: Sao10K/Euryale-1.3-L2-70B layer_range: [9,14] - sources: - model: Xwin-LM/Xwin-LM-70B-V0.1 layer_range: [12,62] - sources: - model: Sao10K/Euryale-1.3-L2-70B layer_range: [54,71] - sources: - model: Xwin-LM/Xwin-LM-70B-V0.1 layer_range: [62,80] merge_method: passthrough dtype: float16

slices: - sources: - model: garage-bAInd/Platypus2-70B-instruct layer_range: [0,12] - sources: - model: Sao10K/WinterGoddess-1.4x-70B-L2 layer_range: [9,14] - sources: - model: garage-bAInd/Platypus2-70B-instruct layer_range: [12,62] - sources: - model: Sao10/WinterGoddess-1.4x-70B-L2 layer_range: [54,71] - sources: - model: garage-bAInd/Platypus2-70B-instruct layer_range: [62,80] merge_method: passthrough dtype: float16

models: - model: llmixer/BigWeave-v8-90b parameters: weight: 0.5 density: 0.25 merge_method: dare_ties base_model: llmixer/BigWeave-v6-90b dtype: float16


# Acknowledgements
[@Xwin-LM](https://huggingface.co/Xwin-LM) For creating Xwin

[@Sao10K](https://huggingface.co/Sao10K) For creating Euryale and WinterGoddess

[@garage-bAInd](https://huggingface.co/garage-bAInd) For creating Platypus2

[@alpindale](https://huggingface.co/alpindale) For creating the original Goliath

[@chargoddard](https://huggingface.co/chargoddard) For developing [mergekit](https://github.com/cg123/mergekit).

# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_llmixer__BigWeave-v12-90b)

|             Metric              |Value|
|---------------------------------|----:|
|Avg.                             |69.19|
|AI2 Reasoning Challenge (25-Shot)|68.09|
|HellaSwag (10-Shot)              |87.70|
|MMLU (5-Shot)                    |69.41|
|TruthfulQA (0-shot)              |61.35|
|Winogrande (5-shot)              |81.22|
|GSM8k (5-shot)                   |47.38|

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Model size

87.8B params

Tensor type

FP16

Text Generation

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Collection including llmixer/BigWeave-v12-90b

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

68.090
normalized accuracy on HellaSwag (10-Shot)
validation set Open LLM Leaderboard

87.700
accuracy on MMLU (5-Shot)
test set Open LLM Leaderboard

69.410
mc2 on TruthfulQA (0-shot)
validation set Open LLM Leaderboard

61.350
accuracy on Winogrande (5-shot)
validation set Open LLM Leaderboard

81.220
accuracy on GSM8k (5-shot)
test set Open LLM Leaderboard

47.380

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