You can deploy this model and start earning money today!

RichardErkhov/
abideen_-_MonarchCoder-MoE-2x7B-gguf

Name: abideen_-_MonarchCoder-MoE-2x7B-gguf
Availability: InStock

endpoints_compatible

gguf

template

Views

No views yet

Quantization made by Richard Erkhov.

Github

Discord

Request more models

MonarchCoder-MoE-2x7B - GGUF

Model creator: https://huggingface.co/abideen/
Original model: https://huggingface.co/abideen/MonarchCoder-MoE-2x7B/

Name	Quant method	Size
MonarchCoder-MoE-2x7B.Q2_K.gguf	Q2_K	4.43GB
MonarchCoder-MoE-2x7B.IQ3_XS.gguf	IQ3_XS	4.95GB
MonarchCoder-MoE-2x7B.IQ3_S.gguf	IQ3_S	5.22GB
MonarchCoder-MoE-2x7B.Q3_K_S.gguf	Q3_K_S	5.2GB
MonarchCoder-MoE-2x7B.IQ3_M.gguf	IQ3_M	5.35GB
MonarchCoder-MoE-2x7B.Q3_K.gguf	Q3_K	5.78GB
MonarchCoder-MoE-2x7B.Q3_K_M.gguf	Q3_K_M	5.78GB
MonarchCoder-MoE-2x7B.Q3_K_L.gguf	Q3_K_L	6.27GB
MonarchCoder-MoE-2x7B.IQ4_XS.gguf	IQ4_XS	6.5GB
MonarchCoder-MoE-2x7B.Q4_0.gguf	Q4_0	6.78GB
MonarchCoder-MoE-2x7B.IQ4_NL.gguf	IQ4_NL	6.85GB
MonarchCoder-MoE-2x7B.Q4_K_S.gguf	Q4_K_S	6.84GB
MonarchCoder-MoE-2x7B.Q4_K.gguf	Q4_K	7.25GB
MonarchCoder-MoE-2x7B.Q4_K_M.gguf	Q4_K_M	7.25GB
MonarchCoder-MoE-2x7B.Q4_1.gguf	Q4_1	7.52GB
MonarchCoder-MoE-2x7B.Q5_0.gguf	Q5_0	8.26GB
MonarchCoder-MoE-2x7B.Q5_K_S.gguf	Q5_K_S	8.26GB
MonarchCoder-MoE-2x7B.Q5_K.gguf	Q5_K	8.51GB
MonarchCoder-MoE-2x7B.Q5_K_M.gguf	Q5_K_M	8.51GB
MonarchCoder-MoE-2x7B.Q5_1.gguf	Q5_1	9.01GB
MonarchCoder-MoE-2x7B.Q6_K.gguf	Q6_K	9.84GB
MonarchCoder-MoE-2x7B.Q8_0.gguf	Q8_0	12.75GB

Original model description:

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

yaml

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

python

!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"])

RichardErkhov/abideen_-_MonarchCoder-MoE-2x7B-gguf