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ReMM SLERP L2 13B

undi95/remm-slerp-l2-13b
Chatcc-by-nc-4.0
Undi95|Released Sep 2023 · Updated Jan 2026
Input Price
$1.88
/1M tokens
Output Price
$1.88
/1M tokens

Overview

Re:MythoMax (ReMM) is a recreation trial of the original MythoMax-L2-B13 with updated models.

Model Card

Re:MythoMax (ReMM) is a recreation trial of the original MythoMax-L2-B13 with updated models.

This merge use SLERP [TESTING] to merge ReML and Huginn v1.2.

Command useds and explaination :

Due to hardware limitation, some merge was done in 2 part.

  • Recreate ReML : Mythologic (v2) (Chronos/Hermes/Airboros)
=> Replacing Chronos by The-Face-Of-Goonery/Chronos-Beluga-v2-13bfp16 (0.30) => Replacing Airoboros by jondurbin/airoboros-l2-13b-2.1 (last version) (0.40) => Keeping NousResearch/Nous-Hermes-Llama2-13b (0.30)

Part 1: python ties_merge.py TheBloke/Llama-2-13B-fp16 ./ReML-L2-13B-part1 --merge The-Face-Of-Goonery/Chronos-Beluga-v2-13bfp16 --density 0.42 --merge jondurbin/airoboros-l2-13b-2.1 --density 0.56 --cuda

Part 2: python ties_merge.py TheBloke/Llama-2-13B-fp16 ./ReML-L2-13B --merge NousResearch/Nous-Hermes-Llama2-13b --density 0.30 --merge Undi95/ReML-L2-13B-part1 --density 0.70 --cuda

With that :

  • Recreate ReMM : MythoMax (v2) (Mythologic/Huginn v1)

=> Replacing Mythologic by the one above (0.5)
=> Replacing Huginn by The-Face-Of-Goonery/Huginn-13b-v1.2 (hottest) (0.5)

Part 3: python slerpmergelm.py "The-Face-Of-Goonery_Huginn-13b-v1.2" "Undi95_ReML-L2-13B" "result"

Version of SLERP used is different to accept usage on notebook : https://github.com/Undi95/LLM-SLERP-MergeTest/tree/main (Thanks @Vali)


Description

This repo contains fp16 files of ReMM-SLERP, a recreation of the original MythoMax, but updated and merged with SLERP.


Models used

  • TheBloke/Llama-2-13B-fp16 (base)
  • The-Face-Of-Goonery/Chronos-Beluga-v2-13bfp16
  • jondurbin/airoboros-l2-13b-2.1
  • NousResearch/Nous-Hermes-Llama2-13b
  • The-Face-Of-Goonery/Huginn-13b-v1.2
  • ReML-L2-13B (Private recreation trial of an updated Mythologic-L2-13B)

Prompt template: Alpaca

Below is an instruction that describes a task. Write a response that appropriately completes the request.

Instruction:

{prompt}

Response:

Special thanks to Sushi kek

Open LLM Leaderboard Evaluation Results


Detailed results can be found here

| Metric | Value |
|-----------------------|---------------------------|
| Avg. | 50.99 |
| ARC (25-shot) | 60.92 |
| HellaSwag (10-shot) | 83.56 |
| MMLU (5-shot) | 55.33 |
| TruthfulQA (0-shot) | 51.97 |
| Winogrande (5-shot) | 75.22 |
| GSM8K (5-shot) | 9.17 |
| DROP (3-shot) | 20.76 |

Features & Capabilities

Modechat
Function Calling-
Vision-
Reasoning-
Web Search-
Url Context-

Technical Details

ArchitectureLlamaForCausalLM
Model Typellama
Base ModelTheBloke/Llama-2-13B-fp16
Librarytransformers

API Usage

from openai import OpenAI

client = OpenAI(
    base_url="https://api.haimaker.ai/v1",
    api_key="YOUR_API_KEY",
)

response = client.chat.completions.create(
    model="undi95/remm-slerp-l2-13b",
    messages=[
        {"role": "user", "content": "Hello, how are you?"}
    ],
)

print(response.choices[0].message.content)

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