README.md

---
license: apache-2.0
tags:
- merge
- mergekit
- lazymergekit
language:
- en
library_name: transformers
pipeline_tag: text-generation
---

# Chikuma

## NOTE: For experimental Purposes

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


Chikuma is a 10.7B parameter model and is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [sethuiyer/SynthIQ-7b](https://huggingface.co/sethuiyer/SynthIQ-7b)
* [openchat/openchat-3.5-0106](https://huggingface.co/openchat/openchat-3.5-0106)

The name "Chikuma" is inspired by the [Chikuma River](https://en.wikipedia.org/wiki/Shinano_River), the longest in Japan, known for its continuous flow and meandering path. 
This metaphorically represents the model's depth, fluidity, and adaptability in processing and understanding language.

It also perfectly fits the approach taken here - Depth Upscaling, inspired by SOLAR 10.7B.

## Nous LLM Evaluation
|                             Model                             |AGIEval|GPT4All|TruthfulQA|Bigbench|Average|
|---------------------------------------------------------------|------:|------:|---------:|-------:|------:|
|[Chikuma_10.7B](https://huggingface.co/sethuiyer/Chikuma_10.7B)|  42.41|  73.41|     56.69|    43.5|     54|

More details can be found (here)[https://gist.github.com/sethuiyer/08b4498ed13a6dead38ad3a6f12e349a]



## 🧩 Configuration

```yaml
slices:
  - sources:
    - model: sethuiyer/SynthIQ-7b
      layer_range: [0, 24]
  - sources:
    - model: openchat/openchat-3.5-0106
      layer_range: [8, 32]
merge_method: passthrough
dtype: bfloat16
```

## 💻 Usage

```python
!pip install -qU transformers accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "sethuiyer/Chikuma_10.7B"
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"])
```

```text
A large language model is a type of artificial intelligence (AI) system that has been trained on a vast amount of text data to understand and generate human-like text.
These models are capable of tasks such as text generation, translation, summarization, and more. They have a vast vocabulary and contextual understanding of language, allowing them to generate coherent and relevant responses.
Examples of large language models include GPT-3, OpenAI's text-based model, and Google's BERT, which is designed for natural language understanding.
```