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--- |
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base_model: |
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- v000000/HaloMaidRP-v1.32-15B-Ruby |
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- v000000/HaloMaidRP-v1.32-15B-Sapphire |
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library_name: transformers |
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tags: |
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- mergekit |
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- merge |
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- llama |
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--- |
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# Llama3-15B-HaloMaidRP-v1.33 |
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# merge |
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This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). |
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## Merge Details and Merge Method |
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This model was merged using an iterative merging process. |
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### Models Merged |
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The following models were included in the merge: |
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* [grimjim/Llama-3-Instruct-abliteration-LoRA-8B](https://huggingface.co/grimjim/Llama-3-Instruct-abliteration-LoRA-8B) |
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* [UCLA-AGI/Llama-3-Instruct-8B-SPPO-Iter3](https://huggingface.co/UCLA-AGI/Llama-3-Instruct-8B-SPPO-Iter3) |
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* [NeverSleep/Llama-3-Lumimaid-8B-v0.1-OAS](https://huggingface.co/NeverSleep/Llama-3-Lumimaid-8B-v0.1-OAS) |
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* [maldv/llama-3-fantasy-writer-8b](https://huggingface.co/maldv/llama-3-fantasy-writer-8b) |
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* [tokyotech-llm/Llama-3-Swallow-8B-v0.1](https://huggingface.co/tokyotech-llm/Llama-3-Swallow-8B-v0.1) |
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* [Sao10K/L3-8B-Stheno-v3.2](https://huggingface.co/Sao10K/L3-8B-Stheno-v3.2) |
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* [ZeusLabs/L3-Aethora-15B-V2](https://huggingface.co/ZeusLabs/L3-Aethora-15B-V2) |
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### Configuration |
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The following YAML configuration was used to produce this model: |
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# Recipe |
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```yaml |
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#1. Take a collection of RP and Storywriter 8b models and merge them. |
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dtype: float32 |
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merge_method: linear |
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weight: 0.15 |
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parameters: |
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- model: tokyotech-llm/Llama-3-Swallow-8B-v0.1 |
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weight: 0.4 |
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parameters: |
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- model: NeverSleep/Llama-3-Lumimaid-8B-v0.1-OAS |
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weight: 0.1 |
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parameters: |
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- model: maldv/llama-3-fantasy-writer-8b |
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weight: 0.6 |
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parameters: |
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- model: Nitral-AI/Hathor_Respawn-L3-8B-v0.8 |
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#2. Use task-arithmetic to learn the vector directions from the RP-Mix onto Llama-3-SPPO which is the smartest 8B model imo, this way we can preserve Meta's multi-bullion dollar tuning. |
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models: |
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dtype: float32 |
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normalize: false |
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parameters: |
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base_model: UCLA-AGI/Llama-3-Instruct-8B-SPPO-Iter3 |
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merge_method: task_arithmetic |
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weight: 0.35 |
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parameters: |
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- model: rpmix-part1 |
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weight: 1.0 |
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parameters: |
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- model: UCLA-AGI/Llama-3-Instruct-8B-SPPO-Iter3 |
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#2,5. Apply abliteration to the previous model |
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models: |
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dtype: float32 |
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merge_method: linear |
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weight: 1.0 |
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parameters: |
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- model: sppo-rpmix-part2+grimjim/Llama-3-Instruct-abliteration-LoRA-8B |
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#3. Create an abliterated version of Stheno3.2-8B as we will use this in the 15B frankenmerge. |
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models: |
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dtype: float32 |
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merge_method: linear |
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weight: 1.0 |
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parameters: |
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- model: Sao10K/L3-8B-Stheno-v3.2+grimjim/Llama-3-Instruct-abliteration-LoRA-8B |
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#4. Make an inverted version of a Llama-3-15B Frankenmerge with the previous models. |
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models: |
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model: v000000/L3-8B-Stheno-v3.2-abliterated |
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- layer_range: [24, 32] |
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- sources: |
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model: v000000/SwallowMaid-8B-L3-SPPO-abliterated |
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- layer_range: [8, 24] |
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- sources: |
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parameters: |
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model: v000000/L3-8B-Stheno-v3.2-abliterated |
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- layer_range: [8, 24] |
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- sources: |
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model: v000000/SwallowMaid-8B-L3-SPPO-abliterated |
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- layer_range: [0, 24] |
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- sources: |
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slices: |
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#5. Make an non-inverted version of a Llama-3-15B Frankenmerge with the previous models. |
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merge_method: passthrough |
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dtype: float32 |
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model: v000000/SwallowMaid-8B-L3-SPPO-abliterated |
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- layer_range: [24, 32] |
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- sources: |
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model: v000000/L3-8B-Stheno-v3.2-abliterated |
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- layer_range: [8, 24] |
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- sources: |
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model: v000000/SwallowMaid-8B-L3-SPPO-abliterated |
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- layer_range: [8, 24] |
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- sources: |
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model: v000000/L3-8B-Stheno-v3.2-abliterated |
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- layer_range: [0, 24] |
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- sources: |
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slices: |
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#6. Test the previous two models and determine which is better in the output/input stage and which is best in the middle and we slerp them in a v-shape. |
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merge_method: passthrough |
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dtype: float32 |
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t: [0, 0.5, 1, 0.5, 0] |
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parameters: |
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dtype: float32 |
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base_model: v000000/Sthalomaid-15B-abliterated |
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merge_method: slerp |
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- model: v000000/Sthalomaid-15B-Inverted-abliterated |
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- model: v000000/Sthalomaid-15B-abliterated |
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#7. Apply Blackroot Lora in a model_stock merge of the different models so far |
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models: |
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dtype: float32 |
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merge_method: model_stock |
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base_model: v000000/Sthalomaid-V-15B-abliterated |
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- model: v000000/Sthalomaid-15B-Inverted-abliterated+Blackroot/Llama-3-8B-Abomination-LORA |
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- model: v000000/Sthalomaid-15B-abliterated+Blackroot/Llama-3-8B-Abomination-LORA |
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- model: v000000/Sthalomaid-V-15B-abliterated+Blackroot/Llama-3-8B-Abomination-LORA #seems to work on 15b |
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- model: v000000/Sthalomaid-15B-Inverted-abliterated |
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- model: v000000/Sthalomaid-15B-abliterated |
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- model: v000000/Sthalomaid-V-15B-abliterated |
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#7. Create another 15B frankenmerge from just SPPO and abiterate it, this is so we can merge in a smarter model. |
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models: |
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dtype: float32 |
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merge_method: passthrough |
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slices: |
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- sources: |
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- layer_range: [0, 24] |
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model: UCLA-AGI/Llama-3-Instruct-8B-SPPO-Iter3+grimjim/Llama-3-Instruct-abliteration-LoRA-8B |
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- sources: |
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- layer_range: [8, 24] |
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model: UCLA-AGI/Llama-3-Instruct-8B-SPPO-Iter3+grimjim/Llama-3-Instruct-abliteration-LoRA-8B |
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parameters: |
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- sources: |
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- layer_range: [8, 24] |
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model: UCLA-AGI/Llama-3-Instruct-8B-SPPO-Iter3+grimjim/Llama-3-Instruct-abliteration-LoRA-8B |
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- sources: |
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- layer_range: [24, 32] |
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model: UCLA-AGI/Llama-3-Instruct-8B-SPPO-Iter3+grimjim/Llama-3-Instruct-abliteration-LoRA-8B |
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#8. Learn vectors from our previous blackroot model_stock model to smarter SPPO-Iter model to preserve RP capabilities. |
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models: |
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- model: v000000/HaloMaidRP-V-15B-Blackroot-v0.1 |
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parameters: |
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weight: 1.3 |
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merge_method: task_arithmetic |
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base_model: v000000/Llama-3-Instruct-15B-SPPO-Iter3-abliterated |
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parameters: |
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normalize: false |
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#9. Merge the blackroot model_stock-15B and SPPO-15B models together with a smooth gradient. |
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dtype: float32 |
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slices: |
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- sources: |
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- model: v000000/HaloMaidRP-V-15B-Blackroot-v0.1 |
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layer_range: [0, 64] |
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- model: v000000/HaloMaidRP-V-15B-Blackroot-v0.223 |
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layer_range: [0, 64] |
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merge_method: slerp |
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base_model: v000000/HaloMaidRP-V-15B-Blackroot-v0.223 |
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parameters: |
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t: |
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- filter: self_attn |
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value: [0, 0.5, 0.3, 0.7, 1, 0.1, 0.6, 0.3, 0.8, 0.5] |
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- filter: mlp |
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value: [1, 0.5, 0.7, 0.3, 0, 0.3, 0.4, 0.7, 0.2, 0.5] |
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- value: 0.5 |
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dtype: bfloat16 #Oops accidentally swtich to half precision do this also very important |
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#10. Heal the layers, o_proj and down_proj seems to be the only tensors that determine adaptation to a new architecture, so we can steal them from an already finetuned 15B, |
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#this way we don't need to finetune our new frankenmerge at all to have full performance. Why reinvent the wheel? |
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#sapphire |
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models: |
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- model: v000000/HaloMaidRP1_component |
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merge_method: slerp |
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base_model: ZeusLabs/L3-Aethora-15B-V2 |
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parameters: |
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t: |
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- filter: o_proj |
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value: 0 |
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- filter: down_proj |
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value: 0 |
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- value: 1 |
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dtype: bfloat16 |
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#11. Go back to an earlier checkpoint that had interesting results with being very depraved before the blackroot model_stock merge and do the same as (10.) to heal it. |
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#ruby |
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models: |
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- model: v000000/component____HaloMaidRP-V |
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merge_method: slerp |
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base_model: ZeusLabs/L3-Aethora-15B-V2 |
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parameters: |
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t: |
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- filter: o_proj |
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value: 0 |
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- filter: down_proj |
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value: 0 |
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- value: 1 |
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dtype: bfloat16 |
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#12. Then we merge these two together to get a semi-depraved smart model. |
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#emerald (this) |
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slices: |
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- sources: |
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- model: v000000/HaloMaidRP-v1.32-15B-Sapphire |
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layer_range: [0, 64] |
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- model: v000000/HaloMaidRP-v1.32-15B-Ruby |
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layer_range: [0, 64] |
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merge_method: slerp |
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base_model: v000000/HaloMaidRP-v1.32-15B-Sapphire |
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parameters: |
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t: |
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- filter: self_attn |
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value: [0.1, 0.6, 0.3, 0.8, 0.5] |
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- filter: mlp |
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value: [0.9, 0.4, 0.7, 0.2, 0.5] |
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- value: 0.5 |
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dtype: bfloat16 |
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``` |
