Edit model card

EXL2 quants of ryzen88/Llama-3-70b-Arimas-story-RP-V1

3.00 bits per weight
3.50 bits per weight
4.00 bits per weight
4.50 bits per weight
5.00 bits per weight
6.00 bits per weight
8.00 bits per weight

Llama-3-70b-Arimas-story-RP-V1

This is really a followup and improvement off my original Lumi-Tess model.

model

A large context uncencored Llama 3 instruct model focussed on story & RP. I found the Smaug version of lama very impressive, exept for a couple of quirks and the default context window. This merge is with the Giraffe instruct for long context window, and basically a smaug - lumi tess merger. I am planning to do the same with a gradient model and compaire it to this giraffe version. Breadcrumbs_ties really is awesome.

This is a merge of pre-trained language models created using mergekit. A big thanks to the creators of the models used in this merge

Merge Details

Merge Method

This model was merged using the breadcrumbs_ties merge method using Z:\Llama-3-Giraffe-70B-Instruct as a base.

Models Merged

The following models were included in the merge:

  • \Smaug-Llama-3-70B-Instruct
  • \Llama-3-Lumimaid-70B-v0.1-alt
  • \Tess-2.0-Llama-3-70B-v0.2

Configuration

The following YAML configuration was used to produce this model:

models:
  - model: \Llama-3-Giraffe-70B-Instruct
    parameters:
      weight: 0.25
      density: 0.90
      gamma: 0.01
  - model: \Smaug-Llama-3-70B-Instruct
    parameters:
      weight: 0.30
      density: 0.90
      gamma: 0.01
  - model: \Tess-2.0-Llama-3-70B-v0.2
    parameters:
      weight: 0.15
      density: 0.90
      gamma: 0.01
  - model: \Llama-3-Lumimaid-70B-v0.1-alt
    parameters:
      weight: 0.30
      density: 0.90
      gamma: 0.01
merge_method: breadcrumbs_ties
base_model: \Llama-3-Giraffe-70B-Instruct
dtype: bfloat16
Downloads last month
3
Inference Examples
This model does not have enough activity to be deployed to Inference API (serverless) yet. Increase its social visibility and check back later, or deploy to Inference Endpoints (dedicated) instead.