README.md

---
license: apache-2.0
tags:
- merge
- mergekit
- mistral
- fhai50032/RolePlayLake-7B
- mlabonne/NeuralBeagle14-7B
base_model:
- fhai50032/RolePlayLake-7B
- mlabonne/NeuralBeagle14-7B
model-index:
- name: BeagleLake-7B
  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: 70.39
      name: normalized accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=fhai50032/BeagleLake-7B
      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: 87.38
      name: normalized accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=fhai50032/BeagleLake-7B
      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: 64.25
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=fhai50032/BeagleLake-7B
      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: 64.92
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=fhai50032/BeagleLake-7B
      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: 83.19
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=fhai50032/BeagleLake-7B
      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: 63.91
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=fhai50032/BeagleLake-7B
      name: Open LLM Leaderboard
---

# BeagleLake-7B

BeagleLake-7B is a merge of the following models :
* [fhai50032/RolePlayLake-7B](https://huggingface.co/fhai50032/RolePlayLake-7B)
* [mlabonne/NeuralBeagle14-7B](https://huggingface.co/mlabonne/NeuralBeagle14-7B)


Merging models are not powerful but are helpful in the case that it can work like Transfer Learning similar idk.. But they perform high on Leaderboard 
For ex.  NeuralBeagle is powerful model with lot of potential to grow and RolePlayLake is Suitable for RP (No-Simping) and is significantly uncensored and nice obligations
Fine-tuning a Merged model as a base model is surely a way to look forward and see a lot of potential going forward..

Much thanks to [Charles Goddard](https://huggingface.co/chargoddard) for making simple interface ['mergekit' ](https://github.com/cg123/mergekit)



## 🧩 Configuration

```yaml
models:
  - model: mlabonne/NeuralBeagle14-7B
# no params for base model
  - model: fhai50032/RolePlayLake-7B
    parameters:
      weight: 0.8
      density: 0.6
  - model: mlabonne/NeuralBeagle14-7B
    parameters:
      weight: 0.3
      density: [0.1,0.3,0.5,0.7,1]
merge_method: dare_ties
base_model: mlabonne/NeuralBeagle14-7B
parameters:
  normalize: true
  int8_mask: true
dtype: float16
```

## 💻 Usage

```python
!pip install -qU transformers accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "fhai50032/BeagleLake-7B"
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_fhai50032__BeagleLake-7B)

|             Metric              |Value|
|---------------------------------|----:|
|Avg.                             |72.34|
|AI2 Reasoning Challenge (25-Shot)|70.39|
|HellaSwag (10-Shot)              |87.38|
|MMLU (5-Shot)                    |64.25|
|TruthfulQA (0-shot)              |64.92|
|Winogrande (5-shot)              |83.19|
|GSM8k (5-shot)                   |63.91|