Name	Quant method	Size
Llama3-merge-biomed-8b.Q2_K.gguf	Q2_K	2.96GB
Llama3-merge-biomed-8b.IQ3_XS.gguf	IQ3_XS	3.28GB
Llama3-merge-biomed-8b.IQ3_S.gguf	IQ3_S	3.43GB
Llama3-merge-biomed-8b.Q3_K_S.gguf	Q3_K_S	3.41GB
Llama3-merge-biomed-8b.IQ3_M.gguf	IQ3_M	3.52GB
Llama3-merge-biomed-8b.Q3_K.gguf	Q3_K	3.74GB
Llama3-merge-biomed-8b.Q3_K_M.gguf	Q3_K_M	3.74GB
Llama3-merge-biomed-8b.Q3_K_L.gguf	Q3_K_L	4.03GB
Llama3-merge-biomed-8b.IQ4_XS.gguf	IQ4_XS	4.18GB
Llama3-merge-biomed-8b.Q4_0.gguf	Q4_0	4.34GB
Llama3-merge-biomed-8b.IQ4_NL.gguf	IQ4_NL	4.38GB
Llama3-merge-biomed-8b.Q4_K_S.gguf	Q4_K_S	4.37GB
Llama3-merge-biomed-8b.Q4_K.gguf	Q4_K	4.58GB
Llama3-merge-biomed-8b.Q4_K_M.gguf	Q4_K_M	4.58GB
Llama3-merge-biomed-8b.Q4_1.gguf	Q4_1	4.78GB
Llama3-merge-biomed-8b.Q5_0.gguf	Q5_0	5.21GB
Llama3-merge-biomed-8b.Q5_K_S.gguf	Q5_K_S	5.21GB
Llama3-merge-biomed-8b.Q5_K.gguf	Q5_K	5.34GB
Llama3-merge-biomed-8b.Q5_K_M.gguf	Q5_K_M	5.34GB
Llama3-merge-biomed-8b.Q5_1.gguf	Q5_1	5.65GB
Llama3-merge-biomed-8b.Q6_K.gguf	Q6_K	6.14GB
Llama3-merge-biomed-8b.Q8_0.gguf	Q8_0	7.95GB

Original model description:

base_model:

meta-llama/Meta-Llama-3-8B-Instruct
NousResearch/Hermes-2-Pro-Llama-3-8B
aaditya/Llama3-OpenBioLLM-8B library_name: transformers tags:
mergekit
merge license: llama3

Llama3-merge-biomed-8b

This is a DARE-TIES Merge of Llama3-8b-Instruct + NousResearch/Hermes-2-Pro-Llama-3-8B + aaditya/Llama3-OpenBioLLM-8B. It is a simple experiment to assess whether combining models with strengths in general language understanding and biomedical knowledge can enhance performance on specialized tasks without compromising general applicability. The results indicate promising outcomes in areas like HendrycksTest tasks related to Biology and Medicine, as well as improvements in complex reasoning as seen in the ARC Challenge and Winogrande benchmarks.

Usage

I recommend using the prompt template of Llama3: https://llama.meta.com/docs/model-cards-and-prompt-formats/meta-llama-3/

Leaderboard metrics according to 🤗 Open LLM Leaderboard

Task	Metric	Ours (%)	Llama38BInstr. (%)	OpenBioLLM8B (%)
ARC Challenge	Accuracy	59.39	57.17	55.38
	Normalized Accuracy	63.65	60.75	58.62
Hellaswag	Accuracy	62.59	59.04	61.83
	Normalized Accuracy	81.53	78.55	80.76
Winogrande	Accuracy	75.93	74.51	70.88
GSM8K	Accuracy	59.36	68.69	10.15
HendrycksTest-Anatomy	Accuracy	72.59	65.19	69.62
HendrycksTest-Clinical Knowledge	Accuracy	77.83	74.72	60.38
HendrycksTest-College Biology	Accuracy	81.94	79.86	79.86
HendrycksTest-College Medicine	Accuracy	69.36	63.58	70.52
HendrycksTest-Medical Genetics	Accuracy	86.00	80.00	80.00
HendrycksTest-Professional Medicine	Accuracy	77.94	71.69	77.94

This is a merge of pre-trained language models created using mergekit.

Merge Details

Merge Method

This model was merged using the DARE TIES merge method using meta-llama/Meta-Llama-3-8B-Instruct as a base.

Models Merged

The following models were included in the merge:

Configuration

The following YAML configuration was used to produce this model:

yaml

models:
  - model: meta-llama/Meta-Llama-3-8B-Instruct
    # Base model providing a general foundation without specific parameters

  - model: meta-llama/Meta-Llama-3-8B-Instruct
    parameters:
      density: 0.60  
      weight: 0.5  

  - model: NousResearch/Hermes-2-Pro-Llama-3-8B
    parameters:
      density: 0.55  
      weight: 0.1  

  - model: aaditya/Llama3-OpenBioLLM-8B
    parameters:
      density: 0.55  
      weight: 0.4 

merge_method: dare_ties
base_model: meta-llama/Meta-Llama-3-8B-Instruct
parameters:
  int8_mask: true
dtype: bfloat16

RichardErkhov/lighteternal_-_Llama3-merge-biomed-8b-gguf