metadata
license: apache-2.0
tags:
- moe
- frankenmoe
- merge
- mergekit
- lazymergekit
- mlabonne/AlphaMonarch-7B
- Syed-Hasan-8503/Tess-Coder-7B-Mistral-v1.0
base_model:
- mlabonne/AlphaMonarch-7B
- Syed-Hasan-8503/Tess-Coder-7B-Mistral-v1.0
language:
- en
library_name: transformers
model-index:
- name: MonarchCoder-MoE-2x7B
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: 70.99
name: normalized accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=abideen/MonarchCoder-MoE-2x7B
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: 87.99
name: normalized accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=abideen/MonarchCoder-MoE-2x7B
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: 65.11
name: accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=abideen/MonarchCoder-MoE-2x7B
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: 71.25
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=abideen/MonarchCoder-MoE-2x7B
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: 80.66
name: accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=abideen/MonarchCoder-MoE-2x7B
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: 69.37
name: accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=abideen/MonarchCoder-MoE-2x7B
name: Open LLM Leaderboard
MonarchCoder-MoE-2x7B
MonarchCoder-MoE-2x7B is a Mixure of Experts (MoE) made with the following models using LazyMergekit:
The main aim behind creating this model is to create a model that performs well in reasoning, conversation, and coding. AlphaMonarch performs amazing on reasoning and conversation tasks. Merging AlphaMonarch with a coding model yielded MonarchCoder-2x7B which performs better on OpenLLM, Nous, and HumanEval benchmark.
🏆 Evaluation results
| Metric |MonarchCoder-Moe-2x7B||MonarchCoder-7B||AlphaMonarch|
|---------------------------------|---------------------|-----------------|------------|
|Avg. | 74.23 | 71.17 | 75.99 |
|HumanEval | 41.15 | 39.02 | 34.14 |
|HumanEval+ | 29.87 | 31.70 | 29.26 |
|MBPP | 40.60 | * | * |
|AI2 Reasoning Challenge (25-Shot)| 70.99 | 68.52 | 73.04 |
|HellaSwag (10-Shot) | 87.99 | 87.30 | 89.18 |
|MMLU (5-Shot) | 65.11 | 64.65 | 64.40 |
|TruthfulQA (0-shot) | 71.25 | 61.21 | 77.91 |
|Winogrande (5-shot) | 80.66 | 80.19 .| 84.69 |
|GSM8k (5-shot) . | 69.37 | 65.13 | 66.72 |
🧩 Configuration
base_model: paulml/OGNO-7B
gate_mode: hidden
dtype: bfloat16
experts:
- source_model: mlabonne/AlphaMonarch-7B
positive_prompts:
- "Mathematics"
- "Logical Reasoning"
- "Intelligent Conversations"
- "Thoughtful Analysis"
- "Biology"
- "Medicine"
- "Problem-solving Dialogue"
- "Physics"
- "Emotional intelligence"
negative_prompts:
- "History"
- "Philosophy"
- "Linguistics"
- "Literature"
- "Art and Art History"
- "Music Theory and Composition"
- "Performing Arts (Theater, Dance)"
- source_model: Syed-Hasan-8503/Tess-Coder-7B-Mistral-v1.0
positive_prompts:
- "Coding"
- "Algorithm Design"
- "Problem Solving"
- "Software Development"
- "Computer"
- "Code Refactoring"
- "Web development"
- "Machine learning"
negative_prompts:
- "Education"
- "Law"
- "Theology and Religious Studies"
- "Communication Studies"
- "Business and Management"
- "Agricultural Sciences"
- "Nutrition and Food Science"
- "Sports Science"
💻 Usage
!pip install -qU transformers bitsandbytes accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "abideen/MonarchCoder-MoE-2x7B"
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"])