---
library_name: transformers
base_model: NewEden/Hamanasu-KTO-V2
tags:
- generated_from_trainer
datasets:
- PocketDoc/Dans-Personamaxx-Logs
- anthracite-org/kalo-opus-instruct-22k-no-refusal
- lodrick-the-lafted/kalo-opus-instruct-3k-filtered
- anthracite-org/nopm_claude_writing_fixed
- anthracite-org/kalo_opus_misc_240827
- anthracite-org/kalo_misc_part2
- NewEden/Claude-Instruct-5K
- NewEden/Claude-Instruct-2.7K
model-index:
- name: outputs/out
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>

axolotl version: `0.8.0.dev0`
```yaml
base_model: NewEden/Hamanasu-KTO-V2
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer

load_in_8bit: false
load_in_4bit: false
strict: false

plugins:
  - axolotl.integrations.liger.LigerPlugin
  - axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin
liger_rope: true
liger_rms_norm: true
liger_layer_norm: true
liger_glu_activation: true
liger_fused_linear_cross_entropy: false
cut_cross_entropy: true

datasets:
  - path: PocketDoc/Dans-Personamaxx-Logs
    type: dan-chat-advanced
  - path: anthracite-org/kalo-opus-instruct-22k-no-refusal
    type: dan-chat-advanced
  - path: lodrick-the-lafted/kalo-opus-instruct-3k-filtered
    type: dan-chat-advanced
  - path: anthracite-org/nopm_claude_writing_fixed
    type: dan-chat-advanced
  - path: anthracite-org/kalo_opus_misc_240827
    type: dan-chat-advanced
  - path: anthracite-org/kalo_misc_part2
    type: dan-chat-advanced
  - path: NewEden/Claude-Instruct-5K
    type: dan-chat-advanced
  - path: NewEden/Claude-Instruct-2.7K
    type: dan-chat-advanced

val_set_size: 0.01
output_dir: ./outputs/out

adapter:
lora_r:
lora_alpha:
lora_dropout:
lora_target_linear:

sequence_len: 32768
sample_packing: true
eval_sample_packing: false
pad_to_sequence_len: true

wandb_project: tavbussy
wandb_entity:
wandb_watch:
wandb_name: magnum-attempt-02
wandb_log_model:

gradient_accumulation_steps: 4
micro_batch_size: 2
num_epochs: 4
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 0.00001
weight_decay: 0.02
max_grad_norm: 0.2

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: true

gradient_checkpointing: true
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true

warmup_steps: 40
evals_per_epoch: 4
eval_table_size:
eval_max_new_tokens: 128
saves_per_epoch: 1

debug:
deepspeed: ./deepspeed_configs/zero3_bf16.json
fsdp:
fsdp_config:

special_tokens:
  pad_token: <|finetune_right_pad_id|>

```

</details><br>

# outputs/out

This model is a fine-tuned version of [NewEden/Hamanasu-KTO-V2](https://huggingface.co/NewEden/Hamanasu-KTO-V2) on the PocketDoc/Dans-Personamaxx-Logs, the anthracite-org/kalo-opus-instruct-22k-no-refusal, the lodrick-the-lafted/kalo-opus-instruct-3k-filtered, the anthracite-org/nopm_claude_writing_fixed, the anthracite-org/kalo_opus_misc_240827, the anthracite-org/kalo_misc_part2, the NewEden/Claude-Instruct-5K and the NewEden/Claude-Instruct-2.7K datasets.
It achieves the following results on the evaluation set:
- Loss: 1.2656

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- total_eval_batch_size: 16
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 40
- num_epochs: 4.0

### Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 1.46          | 0.0109 | 1    | 1.4717          |
| 1.3692        | 0.2514 | 23   | 1.3862          |
| 1.3288        | 0.5027 | 46   | 1.3275          |
| 1.2979        | 0.7541 | 69   | 1.3008          |
| 2.4633        | 1.0109 | 92   | 1.2825          |
| 1.1345        | 1.2623 | 115  | 1.2762          |
| 1.1809        | 1.5137 | 138  | 1.2668          |
| 1.145         | 1.7650 | 161  | 1.2586          |
| 1.0191        | 2.0219 | 184  | 1.2563          |
| 1.0526        | 2.2732 | 207  | 1.2644          |
| 1.0341        | 2.5246 | 230  | 1.2593          |
| 1.0394        | 2.7760 | 253  | 1.2562          |
| 0.9845        | 3.0328 | 276  | 1.2571          |
| 0.9583        | 3.2842 | 299  | 1.2655          |
| 0.9715        | 3.5355 | 322  | 1.2659          |
| 0.9463        | 3.7869 | 345  | 1.2656          |


### Framework versions

- Transformers 4.50.0
- Pytorch 2.5.1+cu124
- Datasets 3.5.0
- Tokenizers 0.21.1