README.md

---
language:
- en
license: llama2
library_name: transformers
tags:
- merge
- mergekit
- lazymergekit
datasets:
- teknium/openhermes
- cognitivecomputations/dolphin
base_model:
- cognitivecomputations/dolphin-llama2-7b
- Tensoic/Llama-2-openhermes
pipeline_tag: text-generation
model-index:
- name: OpenDolphinHermes_Llama2_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: 55.03
      name: normalized accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=sethuiyer/OpenDolphinHermes_Llama2_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: 78.74
      name: normalized accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=sethuiyer/OpenDolphinHermes_Llama2_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: 52.25
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=sethuiyer/OpenDolphinHermes_Llama2_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: 46.1
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=sethuiyer/OpenDolphinHermes_Llama2_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: 73.16
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=sethuiyer/OpenDolphinHermes_Llama2_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: 20.17
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=sethuiyer/OpenDolphinHermes_Llama2_7B
      name: Open LLM Leaderboard
---

# OpenDolphinHermes_Llama2_7B


<p align="center">
  <img src="https://huggingface.co/sethuiyer/OpenDolphinHermes_Llama2_7B/resolve/main/dolphin_hermes.webp" height="256px" alt="SynthIQ">
</p>

mergekit SLERP of these two models
* [cognitivecomputations/dolphin-llama2-7b](https://huggingface.co/cognitivecomputations/dolphin-llama2-7b)
* [Tensoic/Llama-2-openhermes](https://huggingface.co/Tensoic/Llama-2-openhermes)

## 🧩 Configuration

```yaml
slices:
  - sources:
      - model: cognitivecomputations/dolphin-llama2-7b
        layer_range: [0, 32]
      - model: Tensoic/Llama-2-openhermes
        layer_range: [0, 32]
merge_method: slerp
base_model: Tensoic/Llama-2-openhermes
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
```

# Prompt Template (ChatML)
```text
<|im_start|>system
You are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe.
Your answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content.
Please ensure that your responses are socially unbiased and positive in nature.

If a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct.
If you don't know the answer to a question, please don't share false information.
<|im_end|>
<|im_start|>user
{ .Prompt}
<|im_end|>
<|im_start|>assistant
```

# OpenLLM Leaderboard

| T | Model                                      | Average | ARC  | HellaSwag | MMLU  | TruthfulQA | Winogrande | GSM8K |
|---|--------------------------------------------|---------|------|-----------|-------|------------|------------|-------|
| 0 | meta-llama/llama-2-13b-hf        | 55.69   | 59.39 | 82.13     | 55.77 | 37.38      | 76.64      | 22.82 |
| 1 | sethuiyer/OpenDolphinHermes_Llama2_7B      | 54.24   | 55.03| 78.74     | 52.25 | 46.1       | 73.16      | 20.17 |
| 2 | togethercomputer/Llama-2-7B-32K-Instruct   | 50.02   | 51.11| 78.51     | 46.11 | 44.86      | 73.88      | 5.69  |
| 3 | togethercomputer/LLaMa-2-7B-32K            | 47.07   | 47.53| 76.14     | 43.33 | 39.23      | 71.9       | 4.32  |

## Why?

I wanted a LLaMa2-7B model which is as good as base LLaMa2-13B model.

## 💻 Usage

```python
!pip install -qU transformers accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "sethuiyer/OpenDolphinHermes_Llama2_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"])
```

Output:
```text
A large language model is a type of artificial intelligence system that has been trained on a massive amount of data, often millions or even billions of words, to learn the patterns and relationships between words and phrases.
These models can then be used to generate new text, understand and translate languages, and perform various natural language processing tasks.
They have become increasingly popular in recent years due to advances in machine learning technology and their ability to achieve high levels of accuracy and performance on natural language processing tasks.
Examples of large language models include GPT-2, BERT, and T5.
```
## Thanks
Thanks to Google Colab for the compute.
# [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_sethuiyer__OpenDolphinHermes_Llama2_7B)

|             Metric              |Value|
|---------------------------------|----:|
|Avg.                             |54.24|
|AI2 Reasoning Challenge (25-Shot)|55.03|
|HellaSwag (10-Shot)              |78.74|
|MMLU (5-Shot)                    |52.25|
|TruthfulQA (0-shot)              |46.10|
|Winogrande (5-shot)              |73.16|
|GSM8k (5-shot)                   |20.17|