This quant was made using exllamav2-0.0.21 with default dataset.
I tested this quant shortly in some random RPs (including one over 8k context) and it seems to work ok, it might a bit dumber than v3.2 though.
Prompt Templates
Seems to use llama3 prompt template.
Original readme below
Trained with compute from Backyard.ai | Thanks to them and @dynafire for helping me out.
Training Details:
Trained at 8K Context -> Expanded to 32K Context with PoSE training.
Dataset Modifications:
- Further Cleaned up Roleplaying Samples -> Quality Check
- Removed Low Quality Samples from Manual Check -> Increased Baseline Quality Floor
- More Creative Writing Samples -> 2x Samples
- Remade and Refined Detailed Instruct Data
Notes:
- Training run is much less aggressive than previous Stheno versions.
- This model works when tested in bf16 with the same configs as within the file.
- I do not know the effects quantisation has on it.
- Roleplays pretty well. Feels nice in my opinion.
- It has some issues on long context understanding and reasoning. Much better vs rope scaling normally though, so that is a plus.
- Reminder, this isn't a native 32K model. It has it's issues, but it's coherent and working well.
Sanity Check // Needle in a Haystack Results:
- This is not as complex as RULER or NIAN, but it's a basic evaluator. Some improper train examples had Haystack scores ranging from Red to Orange for most of the extended contexts.
Results
Wandb Run:
Wandb
Relevant Axolotl Configurations:
-> Taken from winglian/Llama-3-8b-64k-PoSE - I tried to find my own configs, hours of tinkering but the one he used worked best, so I stuck to it.
- 2M Rope Theta had the best loss results during training compared to other values.
- Leaving it at 500K rope wasn't that much worse, but 4M and 8M Theta made the grad_norm values worsen even if loss drops fast.
- Mixing in Pretraining Data was a PITA. Made it a lot worse with formatting.
- Pretraining / Noise made it worse at Haystack too? It wasn't all Green, Mainly Oranges.
- Improper / Bad Rope Theta shows in Grad_Norm exploding to thousands. It'll drop to low values alright, but it's a scary fast drop even with gradient clipping.
