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---
license: cc-by-nc-4.0
language:
- en
---
### 8bpw 8h
Frostwind-v1

![Frost1](https://huggingface.co/Sao10K/Frostwind-10.7B-v1/resolve/main/frost1.png)

A finetune of [upstage/SOLAR-10.7B-v1.0](https://huggingface.co/upstage/SOLAR-10.7B-v1.0)
<br>Took Roughly 3 Hours with 4x 4090s, over 2 Epochs, with around 52K varied samples.

Dataset Composition:
<br>20% - Coding
<br>30% - Instruct
<br>30% - Generalised Data
<br>10% - Roleplay
<br>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:

```

***

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