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--- |
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base_model: [ibm/merlinite-7b] |
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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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- GGUF |
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license: apache-2.0 |
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--- |
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# Excalibur-7b GGUF |
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<img src="https://i.imgur.com/viIO4WT.png" width="550"/> |
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<i>Image generated with Envoid's [Model9](https://huggingface.co/Envoid/model9) SDXL model </i> |
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FP16 can be found [here](https://huggingface.co/InferenceIllusionist/Excalibur-7b) |
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[Magic-Dolphin-7b](https://huggingface.co/InferenceIllusionist/Magic-Dolphin-7b) was an unexpected surprise. Profoundly satisfied with it as a first attempt. For this follow-up I wanted to target the MMLU benchmark specifically. |
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The challenge this time was placing more weight on Merlinite-7b as an unknown quantity that hasn't been in the spotlight despite its novel LAB tuning method. |
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<b>Excalibur-7b</b> builds on past success and is the culmination of several learnings: |
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* Measuring KL-divergences for new quantization types brought a deeper understanding of benchmarking and assessing model performance |
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* This signifcantly sped up the testing process by using MMLU as a base, narrowing down over 10 candidate linear merges to 1: merliniteX-blockB1 |
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* Reaching the limitations of linear merging necessitated a pivot to reviewing the viability of SLERP, DARE-TIES, and Passthrough methods |
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* Thus a competing candidate merge pool was tested between different merge algorithms. Once more the list was narrowed from 10 candidates to 1: merliniteX-blockF2 |
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* merliniteX-blockF2 (SLERP of Magic-Dolphin-7B and jaskier-7b-dpo in unorthadox proportions) was originally planned for release earlier this week |
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* Instead -blockB1 and -blockF2 were merged and the results were placed head to head in a final round of tests. Ultimately a more conventional execution of SLERP showed the best results for the final step. |
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# Sample Question |
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<img src="https://i.imgur.com/fdFYIhv.jpeg" width="550"/> |
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# Bonus Question - Vision Capabilities |
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<b>Requires additional [mistral-7b-mmproj-v1.5-Q4_1.gguf](https://huggingface.co/koboldcpp/mmproj/tree/main) file for vision functionality</b> |
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<img src="https://i.imgur.com/4wbUrjf.jpeg" width="550"/> |
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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 |
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### Merge Method |
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This model was merged using the SLERP merge method. |
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### Models Merged |
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The following models were included in the merge: |
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* models/merliniteX-blockB1 |
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* models/merliniteX-blockF2 |
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### Configuration |
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The following YAML configuration was used to produce this model: |
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```yaml |
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slices: |
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- sources: |
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- model: models/merliniteX-blockF2 |
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layer_range: [0, 32] |
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- model: models/merliniteX-blockB1 |
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layer_range: [0, 32] |
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# or, the equivalent models: syntax: |
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# models: |
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# - model: psmathur/orca_mini_v3_13b |
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# - model: garage-bAInd/Platypus2-13B |
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merge_method: slerp |
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base_model: models/merliniteX-blockF2 |
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parameters: |
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t: |
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- filter: self_attn |
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value: [1, 0.7, 0.3, 0.5, 0] |
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- filter: mlp |
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value: [0, 0.3, 0.7, 0.5, 1] |
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- value: 0.5 # fallback for rest of tensors |
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dtype: float16 |
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``` |
