README.md

---
license: apache-2.0
tags:
- merge
- mergekit
- lazymergekit
- liminerity/binarized-ingotrix-slerp-7b
- eren23/dpo-binarized-NeutrixOmnibe-7B
base_model:
- liminerity/binarized-ingotrix-slerp-7b
- eren23/dpo-binarized-NeutrixOmnibe-7B
model-index:
- name: Omningotex-7b-slerp
  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: 73.29
      name: normalized accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=liminerity/Omningotex-7b-slerp
      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: 88.96
      name: normalized accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=liminerity/Omningotex-7b-slerp
      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: 64.69
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=liminerity/Omningotex-7b-slerp
      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: 76.32
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=liminerity/Omningotex-7b-slerp
      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: 84.21
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=liminerity/Omningotex-7b-slerp
      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: 70.51
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=liminerity/Omningotex-7b-slerp
      name: Open LLM Leaderboard
---
Title: Introducing Omningotex-7b: The World's Most Accurate 7B LLM

Today, I'm excited to share the creation of a groundbreaking language model, "liminerity/Omningotex-7b-slerp." This model has achieved an impressive accuracy rate of 76.33%, making it the most accurate 7B LLM in the world.
The journey to create Omningotex-7b-slerp began with an experimental process called "merging." I started with a model named "ingot-7b-slerp," which was created by merging two other LLMs, "blurred-beagle-7b-slerp" (by myself, liminerity) and "Macaroni-7b-Tied" (by andrijdavid), a total of eight times over.
After the successful creation of ingot-7b-slerp, I proceeded to merge it with another model, "dpo-binarized-NeuralTrix-7B" by eren23, using gradient slerp. The resulting model, "binarized-ingotrix-slerp-7b," achieved an accuracy rate of 76.04%.
To further enhance the model's performance, I decided to merge "binarized-ingotrix-slerp-7b" with "dpo-binarized-NeutrixOmnibe-7B" by eren23 once again. The resulting model, "Omningotex-7b," is now the most accurate 7B LLM available.
This breakthrough in LLM accuracy was achieved through a combination of careful experimentation and a deep understanding of the underlying algorithms and techniques. I believe that Omningotex-7b-slerp's success demonstrates the potential for further advancements in the field of natural language processing and artificial intelligence.
I look forward to sharing more updates and insights as I continue to explore the possibilities of LLMs and push the boundaries of what is possible in the world of AI. Stay tuned for more exciting developments in the future!

A huge thank you to Maxime Labonne and his creation of LazyMergeKit colab project. Use of it helped me gain a further grasp of the concepts at play and led to the creation of this model. I'm sure it won't be number 1 for long which excited me even more!

Next, I set out to learn how to fine-tune with the resources I have available.
My next overall goal is to try and find a way to produce a smaller model with high accuracy either through merging down using fewer layers after each merge. I may need to include finetuning between each merge or merging larger more accurate models into a smaller base while maintaining accuracy and performance. Every version of "TinyMistral" I come by seems to be bricked in the sense it spits out nonsense. Thank you for your time If you read this all the way.



# Omningotex-7B-slerp

Omningotex-7B-slerp is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [liminerity/binarized-ingotrix-slerp-7b](https://huggingface.co/liminerity/binarized-ingotrix-slerp-7b)
* [eren23/dpo-binarized-NeutrixOmnibe-7B](https://huggingface.co/eren23/dpo-binarized-NeutrixOmnibe-7B)

## 🧩 Configuration

```yaml
slices:
  - sources:
      - model: liminerity/binarized-ingotrix-slerp-7b
        layer_range: [0, 32]
      - model: eren23/dpo-binarized-NeutrixOmnibe-7B
        layer_range: [0, 32]
merge_method: slerp
base_model: liminerity/binarized-ingotrix-slerp-7b
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
```

## 💻 Usage

```python
!pip install -qU transformers accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "liminerity/Omningotex-7b-slerp"
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_liminerity__Omningotex-7b-slerp)

|             Metric              |Value|
|---------------------------------|----:|
|Avg.                             |76.33|
|AI2 Reasoning Challenge (25-Shot)|73.29|
|HellaSwag (10-Shot)              |88.96|
|MMLU (5-Shot)                    |64.69|
|TruthfulQA (0-shot)              |76.32|
|Winogrande (5-shot)              |84.21|
|GSM8k (5-shot)                   |70.51|