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---
base_model:
- mlabonne/ChimeraLlama-3-8B-v3
- johnsnowlabs/JSL-MedLlama-3-8B-v2.0
library_name: transformers
tags:
- mergekit
- merge
license: llama3
---
# Chimera_MedLlama-3-8B
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
## Merge Details
### Merge Method
This model was merged using the SLERP merge method.
### Models Merged
The following models were included in the merge:
* [mlabonne/ChimeraLlama-3-8B-v3](https://huggingface.co/mlabonne/ChimeraLlama-3-8B-v3)
* [johnsnowlabs/JSL-MedLlama-3-8B-v2.0](https://huggingface.co/johnsnowlabs/JSL-MedLlama-3-8B-v2.0)
### Evaluation
- multimedqa (0 shot)</br>
| Tasks |Version|Filter|n-shot| Metric |Value | |Stderr|
|-------------------------------|-------|------|-----:|--------|-----:|---|-----:|
| - medmcqa |Yaml |none | 0|acc |0.6087|± |0.0075|
| | |none | 0|acc_norm|0.6087|± |0.0075|
| - medqa_4options |Yaml |none | 0|acc |0.6269|± |0.0136|
| | |none | 0|acc_norm|0.6269|± |0.0136|
| - anatomy (mmlu) | 0|none | 0|acc |0.6963|± |0.0397|
| - clinical_knowledge (mmlu) | 0|none | 0|acc |0.7585|± |0.0263|
| - college_biology (mmlu) | 0|none | 0|acc |0.7847|± |0.0344|
| - college_medicine (mmlu) | 0|none | 0|acc |0.6936|± |0.0351|
| - medical_genetics (mmlu) | 0|none | 0|acc |0.8200|± |0.0386|
| - professional_medicine (mmlu)| 0|none | 0|acc |0.7684|± |0.0256|
|stem |N/A |none | 0|acc_norm|0.6129|± |0.0066|
| | |none | 0|acc |0.6440|± |0.0057|
| - pubmedqa | 1|none | 0|acc |0.7480|± |0.0194|
|Groups|Version|Filter|n-shot| Metric |Value | |Stderr|
|------|-------|------|-----:|--------|-----:|---|-----:|
|stem |N/A |none | 0|acc_norm|0.6129|± |0.0066|
| | |none | 0|acc |0.6440|± |0.0057|
### Configuration
The following YAML configuration was used to produce this model:
```yaml
slices:
- sources:
- model: mlabonne/ChimeraLlama-3-8B-v3
layer_range: [0, 32]
- model: johnsnowlabs/JSL-MedLlama-3-8B-v2.0
layer_range: [0, 32]
merge_method: slerp
base_model: mlabonne/ChimeraLlama-3-8B-v3
parameters:
t:
- filter: self_attn
value: [0, 0.5, 0.3, 0.7, 1]
- filter: mlp
value: [1, 0.5, 0.7, 0.3, 0]
- value: 0.5
dtype: bfloat16
``` |