---
license: cc-by-nc-4.0
tags:
- merge
- mergekit
- lazymergekit
- abideen/MonarchCoder-7B
- eldogbbhed/NeuralPearlBeagle
base_model:
- abideen/MonarchCoder-7B
- eldogbbhed/NeuralPearlBeagle
model-index:
- name: NeuralMonarchCoderPearlBeagle
  results:
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: AI2 Reasoning Challenge (25-Shot)
      type: ai2_arc
      config: ARC-Challenge
      split: test
      args:
        num_few_shot: 25
    metrics:
    - type: acc_norm
      value: 68.52
      name: normalized accuracy
    source:
      url: https://huggingface.co./spaces/HuggingFaceH4/open_llm_leaderboard?query=eldogbbhed/NeuralMonarchCoderPearlBeagle
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: HellaSwag (10-Shot)
      type: hellaswag
      split: validation
      args:
        num_few_shot: 10
    metrics:
    - type: acc_norm
      value: 87.22
      name: normalized accuracy
    source:
      url: https://huggingface.co./spaces/HuggingFaceH4/open_llm_leaderboard?query=eldogbbhed/NeuralMonarchCoderPearlBeagle
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: MMLU (5-Shot)
      type: cais/mmlu
      config: all
      split: test
      args:
        num_few_shot: 5
    metrics:
    - type: acc
      value: 64.53
      name: accuracy
    source:
      url: https://huggingface.co./spaces/HuggingFaceH4/open_llm_leaderboard?query=eldogbbhed/NeuralMonarchCoderPearlBeagle
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: TruthfulQA (0-shot)
      type: truthful_qa
      config: multiple_choice
      split: validation
      args:
        num_few_shot: 0
    metrics:
    - type: mc2
      value: 61.19
    source:
      url: https://huggingface.co./spaces/HuggingFaceH4/open_llm_leaderboard?query=eldogbbhed/NeuralMonarchCoderPearlBeagle
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: Winogrande (5-shot)
      type: winogrande
      config: winogrande_xl
      split: validation
      args:
        num_few_shot: 5
    metrics:
    - type: acc
      value: 80.51
      name: accuracy
    source:
      url: https://huggingface.co./spaces/HuggingFaceH4/open_llm_leaderboard?query=eldogbbhed/NeuralMonarchCoderPearlBeagle
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: GSM8k (5-shot)
      type: gsm8k
      config: main
      split: test
      args:
        num_few_shot: 5
    metrics:
    - type: acc
      value: 67.02
      name: accuracy
    source:
      url: https://huggingface.co./spaces/HuggingFaceH4/open_llm_leaderboard?query=eldogbbhed/NeuralMonarchCoderPearlBeagle
      name: Open LLM Leaderboard
---

<center><img src='https://i.postimg.cc/K8N1SLYx/ee68f836-5714-4d6f-9646-22f0f7f1601e.png' width='1360px' height='768'></center>

# NeuralMonarchCoderPearlBeagle


NeuralMonarchCoderPearlBeagle is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [abideen/MonarchCoder-7B](https://huggingface.co./abideen/MonarchCoder-7B)
* [eldogbbhed/NeuralPearlBeagle](https://huggingface.co./eldogbbhed/NeuralPearlBeagle)

### Goals

This is a TIES merge, formed from MonarchCoder-7b (A merge of Alpha Monarch and TessCoder) and NeuralPearlBeagle(which is a merge of mlabonne's NeuralBeagle14-7b and Pearl-7B-Slerp). 
It is a somewhat haphazard experiment to see if we can merge more math and coding capabilities into the already outstanding NeuralBeagle14-7b and still maintain the same positive chat abilities.

If you find this or my other merges useful, please consider sending a bit of BTC so I don't have to use Google Colab :D 

BTC: bc1q8lc4mzdtdyz7fx44vaw3jn8qg6w4c3ypfxpdrv

ETH/POLYGON: 0x102a6fd187db8441d2cbead33ac70e87f382f114

## 🧩 Configuration

```yaml
models:
  - model: abideen/MonarchCoder-7B
    parameters:
      density: 0.6
      weight: 0.5
  - model: eldogbbhed/NeuralPearlBeagle
    parameters:
      density: 0.8
      weight: 0.8
merge_method: ties
base_model: eldogbbhed/NeuralPearlBeagle
parameters:
  normalize: true
  int8_mask: true
dtype: float16
```

## 💻 Usage

```python
!pip install -qU transformers accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "eldogbbhed/NeuralMonarchCoderPearlBeagle"
messages = [{"role": "user", "content": "What is a large language model?"}]

tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
    "text-generation",
    model=model,
    torch_dtype=torch.float16,
    device_map="auto",
)

outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
```
# [Open LLM Leaderboard Evaluation Results](https://huggingface.co./spaces/HuggingFaceH4/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co./datasets/open-llm-leaderboard/details_eldogbbhed__NeuralMonarchCoderPearlBeagle)

|             Metric              |Value|
|---------------------------------|----:|
|Avg.                             |71.50|
|AI2 Reasoning Challenge (25-Shot)|68.52|
|HellaSwag (10-Shot)              |87.22|
|MMLU (5-Shot)                    |64.53|
|TruthfulQA (0-shot)              |61.19|
|Winogrande (5-shot)              |80.51|
|GSM8k (5-shot)                   |67.02|