metadata
language:
- en
license: apache-2.0
library_name: transformers
tags:
- merge
base_model:
- sethuiyer/SynthIQ-7b
- openchat/openchat-3.5-0106
pipeline_tag: text-generation
model-index:
- name: Chikuma_10.7B
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: 65.7
name: normalized accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=sethuiyer/Chikuma_10.7B
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: 84.31
name: normalized accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=sethuiyer/Chikuma_10.7B
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: 64.81
name: accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=sethuiyer/Chikuma_10.7B
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: 57.01
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=sethuiyer/Chikuma_10.7B
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: 79.56
name: accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=sethuiyer/Chikuma_10.7B
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: 57.62
name: accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=sethuiyer/Chikuma_10.7B
name: Open LLM Leaderboard
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 (with ChatML Prompt Template)
| Model | AGIEval | GPT4All | TruthfulQA | Bigbench | Average |
|---|---|---|---|---|---|
| SynthIQ-7b | 42.67 | 73.71 | 56.51 | 44.59 | 54.37 |
| openchat/openchat-3.5-0106 | 44.17 | 73.72 | 52.53 | 44.4 | 53.71 |
| Chikuma_10.7B | 42.41 | 73.41 | 56.69 | 43.5 | 54 |
More details can be found here
Recommended Prompt Template (Experimental)
<|im_start|>GPT4 Correct 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.
Always use <|end_of_turn|> when you want to end the answer.<|im_end|>
<|im_start|>GPT4 Correct User:
{{Input}}
<|im_end|>GPT4 Correct Assistant:
ChatML also works, but make sure to add the sentence "Always use <|end_of_turn|> when you want to end the answer" as the default eos token is <|end_of_turn|>.
Tested to work well in :
- text-generation-webui, LLaMa-Precise sampling settings.
transformerstext generation pipeline, temperature=4.0, top_k=50, top_p=0.01.
🧩 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
Ollama:
Chikuma is on Ollama. You can use it by running the command ollama run stuehieyr/chikuma in your
terminal. If you have limited computing resources, check out this video to learn how to run it on
a Google Colab backend.
💻 Usage
sys_message = '''
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.
Always use <|end_of_turn|> when you want to end the answer.
'''
question = '''
Tell me what is a large language model in under 250 words.
'''
messages = [{"role":"system", "content": sys_message}, {"role": "user", "content": question}]
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=4.0, top_k=50, top_p=0.01)
print(outputs[0]["generated_text"])
Open LLM Leaderboard Evaluation Results
Detailed results can be found here
| Metric | Value |
|---|---|
| Avg. | 68.17 |
| AI2 Reasoning Challenge (25-Shot) | 65.70 |
| HellaSwag (10-Shot) | 84.31 |
| MMLU (5-Shot) | 64.81 |
| TruthfulQA (0-shot) | 57.01 |
| Winogrande (5-shot) | 79.56 |
| GSM8k (5-shot) | 57.62 |