| 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 | |