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
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).
- 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|>