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, other
BigAurelian v0.5 120b 32k
A Goliath-120b style frankenmerge of aurelian-v0.5-70b-32K and WinterGoddess-1.4x-70b. The goal is to have similar performance with an extended context size. Important: Use a positional embeddings compression factor (compress_pos_emb) of 8 when loading this model.
Prompting Format
Llama2 and Alpaca.
Merge process
The models used in the merge are aurelian-v0.5-70b-32K and WinterGoddess-1.4x-70b.
The layer mix:
- range 0, 16
aurelian
- range 8, 24
WinterGoddess
- range 17, 32
aurelian
- range 25, 40
WinterGoddess
- range 33, 48
aurelian
- range 41, 56
WinterGoddess
- range 49, 64
aurelian
- range 57, 72
WinterGoddess
- range 65, 80
aurelian
Acknowledgements
@grimulkan For creating aurelian-v0.5-70b-32K
@Sao10K For creating WinterGoddess
@alpindale For creating the original Goliath
@chargoddard For developing mergekit.
- Downloads last month
- 9
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.