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metadata
base_model:
  - nothingiisreal/L3.1-8B-Celeste-V1.5
  - Sao10K/Llama-3.1-8B-Stheno-v3.4
  - Sao10K/L3.1-8B-Niitama-v1.1
  - arcee-ai/Llama-3.1-SuperNova-Lite
  - akjindal53244/Llama-3.1-Storm-8B
  - arcee-ai/Llama-Spark
  - grimjim/Llama-3-Instruct-abliteration-LoRA-8B
  - crestf411/sunfall-peft
  - v000000/L3.1-Celestial-Stone-2x8B
library_name: transformers
tags:
  - merge
  - llama
  - mixtral
  - dpo

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QuantFactory/L3.1-Celestial-Stone-2x8B-DPO-GGUF

This is quantized version of v000000/L3.1-Celestial-Stone-2x8B-DPO created using llama.cpp

Original Model Card

Sampler:
Likes a low temperature due to the MoE architecture. I use 0.3 personally.

Llama-3.1-Celestial-Stone-2x8B-DPO (BF16)

  • DPO Trained, Mixture of Experts (14B).

image/png

  • Direct Preference Optimization run

----> Q6_K


L3.1-Celestial-Stone-2x8B Finetuned on Nvidia A100.

0.5 Epoch completed of dataset jondurbin/gutenberg-dpo-v0.1 with learning_rate=8e-6

Result seems pretty good. More compliant and verbose, less sloppy and safety aligned.


The first expert is Instruct 405B distillation/RP vector merge (Supernova-Lite, Niitama1.1, Storm)

The second expert is ERP/Reddit data merge (Celeste1.5, Stheno3.4, Storm)


The base model is Sao10k/L3.1-Stheno-3.4 with the Sunfall LoRa 0.6.1 to make it understand SillyTavern prompts and storywriting better.


Resultant merge finetuned on jondurbin/gutenberg-dpo-v0.1.

Prompt Template:

<|begin_of_text|><|start_header_id|>system<|end_header_id|>

{system_prompt}<|eot_id|><|start_header_id|>user<|end_header_id|>

{input}<|eot_id|><|start_header_id|>assistant<|end_header_id|>

{output}<|eot_id|>