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8bpw 8h

Frostwind-v1

Frost1

A finetune of upstage/SOLAR-10.7B-v1.0
Took Roughly 3 Hours with 4x 4090s, over 2 Epochs, with around 52K varied samples.

Dataset Composition:
20% - Coding
30% - Instruct
30% - Generalised Data
10% - Roleplay
10% - Dealignment


Testing Notes:

Fairly smart, as I expected. Obviously not at the level of the bigger models, but I did not expect that level from this.

Could be sampler issues, but generally I needed 1/2 swipes to get the correct answer when doing Zero context tests. If context is filled, no issues on my end.

For Roleplays: adding things like avoid writing as {{user}} suprisingly helps. Plus a proper prompt of course. I liked the writing style. Handles group characters in 1 card well, during my tests.

Fairly uncensored during roleplay. Yeah the as an AI stuff can happen at Zero context, but I have no issues once a character card is introduced. I had no issues making outputs that would give me 2500 Life Sentences if posted here.


Trained with Alpaca Format:

### Instruction:
<Prompt>

### Response:

OR

### Instruction:
<Prompt>

### Input:
<Insert Context Here>

### Response:


wandb:
wandb: Run history:
wandb: eval/loss β–ˆβ–ƒβ–‚β–‚β–‚β–‚β–‚β–β–β–β–β–‚β–‚β–‚β–‚β–‚β–‚β–β–β–
wandb: eval/runtime β–ƒβ–‚β–ƒβ–‚β–ƒβ–‚β–‚β–ƒβ–β–ƒβ–ˆβ–‚β–ƒβ–ƒβ–ƒβ–‚β–ƒβ–ƒβ–‚β–‚
wandb: eval/samples_per_second β–†β–‡β–†β–‡β–†β–‡β–‡β–†β–ˆβ–†β–β–‡β–†β–†β–†β–‡β–†β–†β–‡β–‡
wandb: eval/steps_per_second β–†β–‡β–†β–‡β–†β–‡β–‡β–†β–ˆβ–†β–β–‡β–†β–†β–†β–‡β–†β–†β–‡β–‡
wandb: train/epoch β–β–β–β–‚β–‚β–‚β–‚β–‚β–‚β–ƒβ–ƒβ–ƒβ–ƒβ–ƒβ–„β–„β–„β–„β–„β–„β–…β–…β–…β–…β–…β–…β–†β–†β–†β–†β–†β–‡β–‡β–‡β–‡β–‡β–‡β–ˆβ–ˆβ–ˆ
wandb: train/global_step β–β–β–β–‚β–‚β–‚β–‚β–‚β–‚β–ƒβ–ƒβ–ƒβ–ƒβ–ƒβ–„β–„β–„β–„β–„β–„β–…β–…β–…β–…β–…β–…β–†β–†β–†β–†β–†β–‡β–‡β–‡β–‡β–‡β–‡β–ˆβ–ˆβ–ˆ
wandb: train/learning_rate β–„β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–‡β–‡β–‡β–‡β–‡β–†β–†β–†β–†β–…β–…β–…β–…β–„β–„β–„β–ƒβ–ƒβ–ƒβ–ƒβ–‚β–‚β–‚β–‚β–‚β–β–β–β–β–β–β–
wandb: train/loss β–ˆβ–…β–…β–†β–…β–…β–„β–„β–„β–†β–†β–…β–†β–†β–†β–…β–„β–†β–…β–…β–…β–†β–„β–„β–ƒβ–„β–ƒβ–ƒβ–‚β–ƒβ–„β–‚β–‚β–ƒβ–ƒβ–‚β–β–‚β–‚β–‚
wandb:
wandb: Run summary:
wandb: eval/loss 0.74622
wandb: eval/runtime 72.5049
wandb: eval/samples_per_second 37.239
wandb: eval/steps_per_second 2.331
wandb: train/epoch 1.98
wandb: train/global_step 410
wandb: train/learning_rate 0.0
wandb: train/loss 0.6457
wandb: train/total_flos 3.4382652340646707e+18
wandb: train/train_loss 0.70204
wandb: train/train_runtime 10880.917
wandb: train/train_samples_per_second 9.417
wandb: train/train_steps_per_second 0.038
wandb:

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