language:
- en
license: apache-2.0
library_name: transformers
tags:
- moe
- frankenmoe
- merge
- mergekit
- lazymergekit
- dvilasuero/DistilabelBeagle14-7B
- beowolx/CodeNinja-1.0-OpenChat-7B
- WizardLM/WizardMath-7B-V1.1
- Maths
- Code
- Python
base_model:
- dvilasuero/DistilabelBeagle14-7B
- beowolx/CodeNinja-1.0-OpenChat-7B
- WizardLM/WizardMath-7B-V1.1
pipeline_tag: text-generation
model-index:
- name: Pearl-3x7B
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: 65.53
name: normalized accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=louisbrulenaudet/Pearl-3x7B
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: 85.54
name: normalized accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=louisbrulenaudet/Pearl-3x7B
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: 64.27
name: accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=louisbrulenaudet/Pearl-3x7B
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: 52.17
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=louisbrulenaudet/Pearl-3x7B
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: 78.69
name: accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=louisbrulenaudet/Pearl-3x7B
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: 57.16
name: accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=louisbrulenaudet/Pearl-3x7B
name: Open LLM Leaderboard
Pearl-3x7B, an xtraordinary Mixture of Experts (MoE) for data science
Pearl-3x7B is a Mixture of Experts (MoE) made with the following models :
A Mixture of Experts (MoE) model represents a sophisticated architecture that amalgamates the capabilities of multiple specialized models to address a wide array of tasks within a unified framework. Within the realm of a MoE model tailored for a chat application, the integration of expertise spanning three distinct domains - chat, code, and mathematics - substantially enhances its capacity to furnish nuanced and precise responses to a diverse spectrum of user inquiries.
The initial expert model, honed for chat applications, exhibits prowess in comprehending natural language nuances, conversational dynamics, and contextual cues. Drawing upon extensive conversational data, it adeptly generates engaging and contextually pertinent responses, thereby fostering meaningful interactions with users.
The subsequent expert model, centered on code, brings to the fore proficiency in programming languages, algorithms, and software engineering principles. Possessing a deep-seated understanding of syntax, logical constructs, and problem-solving methodologies, it deftly tackles queries spanning coding challenges, debugging assistance, and software development inquiries.
Lastly, the third expert model, specializing in mathematics, boasts expertise in mathematical reasoning, problem-solving strategies, and analytical techniques. Armed with a breadth of knowledge encompassing arithmetic, algebra, calculus, and beyond, it offers precise solutions, lucid explanations, and profound insights for mathematical queries, equations, and proofs.
Configuration
base_model: argilla/CapybaraHermes-2.5-Mistral-7B
experts:
- source_model: dvilasuero/DistilabelBeagle14-7B
positive_prompts:
- "chat"
- "assistant"
- "tell me"
- "explain"
- "help"
- "guide"
- "assist"
- "answer"
- "support"
- "clarify"
- "elaborate"
- "educate"
- "inform"
- "advise"
- "instruct"
- source_model: beowolx/CodeNinja-1.0-OpenChat-7B
positive_prompts:
- "code"
- "python"
- "javascript"
- "programming"
- "algorithm"
- "develop"
- "debug"
- "optimize"
- "software"
- "engineer"
- "web"
- "application"
- "framework"
- "library"
- "syntax"
- "logic"
- "compile"
- "execute"
- source_model: WizardLM/WizardMath-7B-V1.1
positive_prompts:
- "reason"
- "math"
- "mathematics"
- "solve"
- "count"
- "calculate"
- "analyze"
- "derive"
- "compute"
- "numbers"
- "equation"
- "theorem"
- "proof"
- "geometry"
- "trigonometry"
- "statistics"
- "probability"
- "algebra"
- "integral"
Usage
!pip install -qU transformers bitsandbytes accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "louisbrulenaudet/Pearl-3x7B"
tokenizer = AutoTokenizer.from_pretrained(model)
pipeline = transformers.pipeline(
"text-generation",
model=model,
model_kwargs={"torch_dtype": torch.float16, "load_in_4bit": True},
)
messages = [{"role": "user", "content": "Explain what a Mixture of Experts is in less than 100 words."}]
prompt = pipeline.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
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"])
Citing & Authors
If you use this code in your research, please use the following BibTeX entry.
@misc{louisbrulenaudet2023,
author = {Louis Brulé Naudet},
title = {Pearl-3x7B, an xtraordinary Mixture of Experts (MoE) for data science},
year = {2023}
howpublished = {\url{https://huggingface.co/louisbrulenaudet/Pearl-3x7B}},
}
Feedback
If you have any feedback, please reach out at louisbrulenaudet@icloud.com.
Open LLM Leaderboard Evaluation Results
Detailed results can be found here
| Metric | Value |
|---|---|
| Avg. | 67.23 |
| AI2 Reasoning Challenge (25-Shot) | 65.53 |
| HellaSwag (10-Shot) | 85.54 |
| MMLU (5-Shot) | 64.27 |
| TruthfulQA (0-shot) | 52.17 |
| Winogrande (5-shot) | 78.69 |
| GSM8k (5-shot) | 57.16 |