DeusImperator/L3-8B-Stheno-v3.3-32K_exl2_8.05bpw_rpcal_mk2

LLaMa-3-Stheno-v3.3-32K - EXL2 8.05bpw rpcal mk2

This is a 8bpw EXL2 quant of Sao10K/L3-8B-Stheno-v3.3-32K

This quant was made using exllamav2-0.0.21 with Bluemoon-light dataset for RP.

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 (still better than most other llama3-8B models).

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: <br>Trained at 8K Context -> Expanded to 32K Context with PoSE training.

Dataset Modifications: <br>\- Further Cleaned up Roleplaying Samples -> Quality Check <br>\- Removed Low Quality Samples from Manual Check -> Increased Baseline Quality Floor <br>\- More Creative Writing Samples -> 2x Samples <br>\- Remade and Refined Detailed Instruct Data

Notes: <br>\- Training run is much less aggressive than previous Stheno versions. <br>\- This model works when tested in bf16 with the same configs as within the file. <br>\- I do not know the effects quantisation has on it. <br>\- Roleplays pretty well. Feels nice in my opinion. <br>\- It has some issues on long context understanding and reasoning. Much better vs rope scaling normally though, so that is a plus. <br>\- 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: <br>\- 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.

Wandb Run:

Relevant Axolotl Configurations: <br>-> Taken from winglian/Llama-3-8b-64k-PoSE <br>\- I tried to find my own configs, hours of tinkering but the one he used worked best, so I stuck to it. <br>\- 2M Rope Theta had the best loss results during training compared to other values. <br>\- Leaving it at 500K rope wasn't that much worse, but 4M and 8M Theta made the gradnorm values worsen even if loss drops fast. <br>\- Mixing in Pretraining Data was a PITA. Made it a lot worse with formatting. <br>\- Pretraining / Noise made it worse at Haystack too? It wasn't all Green, Mainly Oranges. <br>\- Improper / Bad Rope Theta shows in GradNorm exploding to thousands. It'll drop to low values alright, but it's a scary fast drop even with gradient clipping.

sequence_len: 8192
use_pose: true
pose_max_context_len: 32768

overrides_of_model_config:
  rope_theta: 2000000.0
  max_position_embeddings: 32768

  # peft_use_dora: true
adapter: lora
peft_use_rslora: true
lora_model_dir:
lora_r: 256
lora_alpha: 256
lora_dropout: 0.1
lora_target_linear: true
lora_target_modules:
  - gate_proj
  - down_proj
  - up_proj
  - q_proj
  - v_proj
  - k_proj
  - o_proj

warmup_steps: 80
gradient_accumulation_steps: 6
micro_batch_size: 1
num_epochs: 2
optimizer: adamw_bnb_8bit
lr_scheduler: cosine_with_min_lr
learning_rate: 0.00004
lr_scheduler_kwargs:
    min_lr: 0.000004