Instructions to use wolfram/miquliz-120b-v2.0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use wolfram/miquliz-120b-v2.0 with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("wolfram/miquliz-120b-v2.0", dtype="auto") - Notebooks
- Google Colab
- Kaggle
File size: 1,090 Bytes
99cc4f5 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 | merge_method: linear
parameters:
weight: 1.0
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, 20]
- sources:
- model: lizpreciatior/lzlv_70b_fp16_hf
layer_range: [10, 30]
- sources:
- model: 152334H/miqu-1-70b-sf
layer_range: [20, 40]
- sources:
- model: lizpreciatior/lzlv_70b_fp16_hf
layer_range: [30, 50]
- sources:
- model: 152334H/miqu-1-70b-sf
layer_range: [40, 60]
- sources:
- model: lizpreciatior/lzlv_70b_fp16_hf
layer_range: [50, 70]
- sources:
- model: 152334H/miqu-1-70b-sf
layer_range: [60, 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
dtype: float16
tokenizer_source: model:152334H/miqu-1-70b-sf
|