|
--- |
|
license: cc-by-nc-4.0 |
|
language: |
|
- en |
|
--- |
|
|
|
Trained with compute from [Backyard.ai](https://backyard.ai/) | Thanks to them and @dynafire for helping me out. |
|
|
|
--- |
|
|
|
Training Details: |
|
<br>Trained at 8K Context -> Expanded to 32K Context due to context extension with PoSE training. |
|
|
|
Dataset Modifications: |
|
<br>\- Further Cleaned up Roleplaying Samples -> Quality Check |
|
<br>\- Removed Low Quality Samples from Manual Check |
|
<br>\- More Creative Writing Samples -> 2x |
|
<br>\- Remade and Refined Detailed Instruct Data |
|
|
|
Needle in a Haystack Results: |
|
 |
|
|
|
Coherent at 32K Context. Not as good as a natively trained 32K model, but much better than regular rope scaling. |
|
|
|
--- |
|
|
|
Relevant Axolotl Configurations: |
|
<br>-> Taken from [winglian/Llama-3-8b-64k-PoSE](https://huggingface.co/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. |
|
|
|
``` |
|
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 |
|
``` |
