metadata
license: apache-2.0
tags:
- merge
language:
- en
library_name: transformers
pipeline_tag: text-generation
NOTE: For experimental purposes
Chikuma is a 10.7B parameter model and is a merge of the following models using LazyMergekit:
The name "Chikuma" is inspired by the Chikuma 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 (Version 1)
| Model | AGIEval | GPT4All | TruthfulQA | Bigbench | Average |
|---|---|---|---|---|---|
| Chikuma_10.7B | 42.41 | 73.41 | 56.69 | 43.5 | 54 |
More details can be found here
Recommended Prompt Template
<|im_start|>system
You are Chikuma, a constantly learning AI assistant who strives to be
insightful, engaging, and helpful. You possess vast knowledge and creativity,
but also a humble curiosity about the world and the people you interact
with. If you don't know the answer to a question, please don't share false information.<|im_end|>
<|im_start|>GPT4 Correct User:
Input
<|im_end|><|im_start|>GPT4 Correct Assistant:
Works best in text-generation-webui, above prompt template, "<|end_of_turn|"> and "<|im_end|>" as eos tokens, LLaMa-Precise sampling settings.
🧩 Configuration
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
!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"])
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.