Yi-55B
Collection
A collection of Yi 55B models with both 32K and 200K context lenght
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5 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
This is a retired model since it was merged with a Capybara, which has been trained wrong with a missing eos_token. Check out the new model: 1
SG Raccoon 55B
The first 55B auto-regressive causal LM created by combining 2x finetuned Yi 34b with 200K context into one.
Prompting Format
SYSTEM: <ANY SYSTEM CONTEXT>
USER:
ASSISTANT:
Merge process
The models used in the merge are Tess-M-v1.3 and Nous-Capybara-34B.
The layer ranges used are as follows:
- model: migtissera/Tess-M-v1.3
layer_range: [0, 14]
- model: NousResearch/Nous-Capybara-34B
layer_range: [7, 21]
- model: migtissera/Tess-M-v1.3
layer_range: [15, 29]
- model: NousResearch/Nous-Capybara-34B
layer_range: [22, 36]
- model: migtissera/Tess-M-v1.3
layer_range: [30, 44]
- model: NousResearch/Nous-Capybara-34B
layer_range: [37, 51]
- model: migtissera/Tess-M-v1.3
layer_range: [45, 59]
Tips
Being a Yi model, try disabling the BOS token and/or running a lower temperature with MinP (and no other samplers) if output doesn't seem right. Yi tends to run "hot" by default.
Sometimes the model "spells out" the stop token as like Capybara, so you may need to add as an additional stopping condition.
Benchmarks
Coming soon.
Acknowledgements
Special thanks to MSS for sponsoring this project
@chargoddard for developing the framework used to merge the model - mergekit.
Great thanks to @Undi95 for helping figuring out model merge options
Also credits to the 01-ai team for their amazing models
This merged model is inspired by Goliath 120B
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