Training Details:
Trained at 8K Context -> Expanded to 32K Context due to context extension with PoSE training.

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

Needle in a Haystack Results:
![Results](Linkhere)

Coherent at 32K Context. Not as good as a natively trained 32K model, but much better than regular rope scaling.

```
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
```