about
A fourth merge between a L3 70b model (Dolphin 2.9.1) and a L3.1 70b base model (Tess 3) inspired by https://huggingface.co/sophosympatheia/New-Dawn-Llama-3.1-70B-v1.1 and https://huggingface.co/jukofyork/Dusk-Miqu-70B .
This time, I double epsilon from 0.05 to 0.1, on the top of the doubled density from 0.25 to 0.5.
benchs
- PPL 512 Wikitext Eng : 3.74 (decent)
- ARC-C : 62.55 (very good)
- ARC-E : 77.20 (average+)
tests
Let's see if it can hold long context (on testing):
- At 10k, it holds coherence.
- At 20k, it holds coherence.
- At 28k, it holds coherence.
- Step validated. I will try the 0.25 density / 0.1 epsilon combo with 0.24, just to be sure I'm not missing something.
credits
Credits go to Jukofyork and Sophosympatheia, as well as to the Arcee/Mergekit folks and models authors of course.
merge
This is a merge of pre-trained language models created using mergekit.
Merge Details
Merge Method
This model was merged using the Linear DELLA merge method using migtissera/Tess-3-Llama-3.1-70B as a base.
Models Merged
The following models were included in the merge:
Configuration
The following YAML configuration was used to produce this model:
merge_method: della_linear
base_model: migtissera/Tess-3-Llama-3.1-70B
models:
- model: cognitivecomputations/dolphin-2.9.1-llama-3-70b
parameters:
weight:
- filter: q_proj
value: [0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0]
- filter: k_proj
value: [0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0]
- filter: v_proj
value: [0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0]
- filter: o_proj
value: [0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0]
- filter: input_layernorm
value: [0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0]
- filter: up_proj
value: [0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0]
- filter: gate_proj
value: [0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0]
- filter: down_proj
value: [0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0]
- filter: post_attention_layernorm
value: [0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0]
- value: 0
density: 0.5
epsilon: 0.1
lambda: 1.0
- model: migtissera/Tess-3-Llama-3.1-70B
parameters:
weight: 1.0
density:
- filter: q_proj
value: [1, 1, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 1, 1]
- filter: k_proj
value: [1, 1, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 1, 1]
- filter: v_proj
value: [1, 1, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 1, 1]
- filter: o_proj
value: [1, 1, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 1, 1]
- filter: input_layernorm
value: [1, 1, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 1, 1]
- filter: up_proj
value: [1, 1, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 1, 1]
- filter: gate_proj
value: [1, 1, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 1, 1]
- filter: down_proj
value: [1, 1, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 1, 1]
- filter: post_attention_layernorm
value: [1, 1, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 1, 1]
- value: 0.5
epsilon:
- filter: q_proj
value: [0, 0, 0.05, 0.05, 0.07, 0.1, 0.07, 0.05, 0.05, 0, 0]
- filter: k_proj
value: [0, 0, 0.05, 0.05, 0.07, 0.1, 0.07, 0.05, 0.05, 0, 0]
- filter: v_proj
value: [0, 0, 0.05, 0.05, 0.07, 0.1, 0.07, 0.05, 0.05, 0, 0]
- filter: o_proj
value: [0, 0, 0.05, 0.05, 0.07, 0.1, 0.07, 0.05, 0.05, 0, 0]
- filter: input_layernorm
value: [0, 0, 0.05, 0.05, 0.07, 0.1, 0.07, 0.05, 0.05, 0, 0]
- filter: up_proj
value: [0, 0, 0.05, 0.05, 0.07, 0.1, 0.07, 0.05, 0.05, 0, 0]
- filter: gate_proj
value: [0, 0, 0.05, 0.05, 0.07, 0.1, 0.07, 0.05, 0.05, 0, 0]
- filter: down_proj
value: [0, 0, 0.05, 0.05, 0.07, 0.1, 0.07, 0.05, 0.05, 0, 0]
- filter: post_attention_layernorm
value: [0, 0, 0.05, 0.05, 0.07, 0.1, 0.07, 0.05, 0.05, 0, 0]
- value: 0.1
lambda: 1.0
dtype: bfloat16
out_dtype: bfloat16
parameters:
int8_mask: true
normalize: true
rescale: true
filter_wise: false
chat_template: auto
tokenizer:
source: union
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