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Joah-Llama-3-KoEn-8B-Coder-v1-GGUF

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Joah-Llama-3-KoEn-8B-Coder-v1-GGUF
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---
base_model:
- beomi/Llama-3-KoEn-8B-Instruct-preview
- Danielbrdz/Barcenas-Llama3-8b-ORPO
- maum-ai/Llama-3-MAAL-8B-Instruct-v0.1
- rombodawg/Llama-3-8B-Instruct-Coder
- NousResearch/Meta-Llama-3-8B-Instruct
- rombodawg/Llama-3-8B-Base-Coder-v3.5-10k
- cognitivecomputations/dolphin-2.9-llama3-8b
- asiansoul/Llama-3-Open-Ko-Linear-8B
- NousResearch/Meta-Llama-3-8B
- aaditya/Llama3-OpenBioLLM-8B
library_name: transformers
tags:
- mergekit
- merge

---
# Joah-Llama-3-KoEn-8B-Coder-v1

<a href="https://ibb.co/8XPkwP8"><img src="https://i.ibb.co/kMqZTqc/Joah.png" alt="Joah" border="0"></a><br />

์˜ค๋Š˜ ๋ถ€ํ„ฐ ์„œ๋กœ์—๊ฒŒ ๋น›์ด ๋˜์–ด ์ค„ ์—ฌ๋Ÿฌ๋ถ„์˜ Merge Model

"์ข‹์•„(Joah)" by AsianSoul


## Merge Details


The performance of this merge model doesn't seem to be bad though.-> Just opinion

This may not be a model that satisfies you. But if we continue to overcome our shortcomings, 

Won't we someday find the answer we want?

Don't worry even if you don't get the results you want. 

I'll find the answer for you.

Soon real PoSE to extend Llama's context length to 64k with using my merge method :  "reborn"[reborn](https://medium.com/@puffanddmx82/reborn-elevating-model-adaptation-with-merging-for-superior-nlp-performance-f604e8e307b2)

I have found that most of merge's model outside so far do not actually have 64k in their configs. I will improve it in the next merge.

256k is not possible. My computer is running out of memory. If you support me, i will try it on a computer with maximum specifications,

also, i would like to conduct great tests by building a network with high-capacity traffic and high-speed 10G speeds for you.

### Merge Method

This model was merged using the [DARE](https://arxiv.org/abs/2311.03099) [TIES](https://arxiv.org/abs/2306.01708) merge method using [NousResearch/Meta-Llama-3-8B](https://huggingface.co/NousResearch/Meta-Llama-3-8B) as a base.

### Models Merged

The following models were included in the merge:
* [beomi/Llama-3-KoEn-8B-Instruct-preview](https://huggingface.co/beomi/Llama-3-KoEn-8B-Instruct-preview)
* [Danielbrdz/Barcenas-Llama3-8b-ORPO](https://huggingface.co/Danielbrdz/Barcenas-Llama3-8b-ORPO)
* [maum-ai/Llama-3-MAAL-8B-Instruct-v0.1](https://huggingface.co/maum-ai/Llama-3-MAAL-8B-Instruct-v0.1)
* [rombodawg/Llama-3-8B-Instruct-Coder](https://huggingface.co/rombodawg/Llama-3-8B-Instruct-Coder)
* [NousResearch/Meta-Llama-3-8B-Instruct](https://huggingface.co/NousResearch/Meta-Llama-3-8B-Instruct)
* [rombodawg/Llama-3-8B-Base-Coder-v3.5-10k](https://huggingface.co/rombodawg/Llama-3-8B-Base-Coder-v3.5-10k)
* [cognitivecomputations/dolphin-2.9-llama3-8b](https://huggingface.co/cognitivecomputations/dolphin-2.9-llama3-8b)
* [asiansoul/Llama-3-Open-Ko-Linear-8B](https://huggingface.co/asiansoul/Llama-3-Open-Ko-Linear-8B)
* [aaditya/Llama3-OpenBioLLM-8B](https://huggingface.co/aaditya/Llama3-OpenBioLLM-8B)

### Configuration

The following YAML configuration was used to produce this model:

```yaml
models:
  - model: NousResearch/Meta-Llama-3-8B
    # Base model providing a general foundation without specific parameters

  - model: NousResearch/Meta-Llama-3-8B-Instruct
    parameters:
      density: 0.60  
      weight: 0.25  
  
  - model: beomi/Llama-3-KoEn-8B-Instruct-preview
    parameters:
      density: 0.55  
      weight: 0.15  
  
  - model: asiansoul/Llama-3-Open-Ko-Linear-8B
    parameters:
      density: 0.55  
      weight: 0.2  

  - model: maum-ai/Llama-3-MAAL-8B-Instruct-v0.1
    parameters:
      density: 0.55  
      weight: 0.1 

  - model: rombodawg/Llama-3-8B-Instruct-Coder
    parameters:
      density: 0.55  
      weight: 0.1
      
  - model: rombodawg/Llama-3-8B-Base-Coder-v3.5-10k
    parameters:
      density: 0.55  
      weight: 0.1  

  - model: cognitivecomputations/dolphin-2.9-llama3-8b
    parameters:
      density: 0.55  
      weight: 0.05  

  - model: Danielbrdz/Barcenas-Llama3-8b-ORPO
    parameters:
      density: 0.55  
      weight: 0.05 

  - model: aaditya/Llama3-OpenBioLLM-8B
    parameters:
      density: 0.55  
      weight: 0.1 

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


```