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lunarflu
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goliath-120b

Text Generation
Safetensors
English
llama
Merge
conversational
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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, audio-text-to-text, 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-ranking, 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, visual-document-retrieval, any-to-any, other

Goliath 120B

Credit to alpindale, the original model author!

An auto-regressive causal LM created by combining 2x finetuned Llama-2 70B into one.

Please check out the quantized formats provided by @TheBloke and @Panchovix:

  • GGUF (llama.cpp)
  • GPTQ (KoboldAI, TGW, Aphrodite)
  • AWQ (TGW, Aphrodite, vLLM)
  • Exllamav2 (TGW, KoboldAI)

Prompting Format

Both Vicuna and Alpaca will work, but due the initial and final layers belonging primarily to Xwin, the original author expects Vicuna to work the best.

Merge process

The models used in the merge are Xwin and Euryale.

The layer ranges used are as follows:

- range 0, 16
  Xwin
- range 8, 24
  Euryale
- range 17, 32
  Xwin
- range 25, 40
  Euryale
- range 33, 48
  Xwin
- range 41, 56
  Euryale
- range 49, 64
  Xwin
- range 57, 72
  Euryale
- range 65, 80
  Xwin

Screenshots

image/png

Benchmarks

Coming soon.

Acknowledgements

Credits goes to @chargoddard for developing the framework used to merge the model - mergekit.

Special thanks to @Undi95 for helping with the merge ratios.

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