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merge_method: linear # use linear so we can include multiple models, albeit at a zero weight
parameters:
weight: 1.0 # weight everything as 1 unless specified otherwise - linear with one model weighted at 1 is a no-op like passthrough
slices:
- sources:
- model: cognitivecomputations/dolphin-2.9-llama3-8b # embed_tokens comes along with the ride with whatever is the first layer
layer_range: [0, 1]
- model: NousResearch/Meta-Llama-3-8B-Instruct # add dummy second model with 0 weight so tokenizer-based merge routine is invoked for embed_tokens
layer_range: [0, 1]
parameters:
weight: 0
- sources:
- model: cognitivecomputations/dolphin-2.9-llama3-8b
layer_range: [1, 24]
- sources:
- model: NousResearch/Meta-Llama-3-8B-Instruct
layer_range: [8, 24]
parameters:
scale:
- filter: o_proj
value: 0.0
- filter: down_proj
value: 0.0
- value: 1.0
- sources:
- model: cognitivecomputations/dolphin-2.9-llama3-8b
layer_range: [24, 31]
- sources: # same as above, but for lm_head with the last layer
- model: cognitivecomputations/dolphin-2.9-llama3-8b
layer_range: [31, 32]
- model: NousResearch/Meta-Llama-3-8B-Instruct
layer_range: [31, 32]
parameters:
weight: 0
dtype: bfloat16
tokenizer_source: model:cognitivecomputations/dolphin-2.9-llama3-8b # keep exact tokenizer used by dolphin - or you could use `union` if you add all of the input models to the first/last slice
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