README.md

---
library_name: peft
tags:
- axolotl
- base_model:adapter:model
- lora
- transformers
datasets:
- hardlyworking/HardlyRPv2-10k
base_model: model
pipeline_tag: text-generation
model-index:
- name: MS32-2
  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. -->

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<details><summary>See axolotl config</summary>

axolotl version: `0.12.0.dev0`
```yaml
base_model: model

hub_model_id: hardlyworking/MS32-2
hub_strategy: "all_checkpoints"
push_dataset_to_hub:
hf_use_auth_token: true

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

load_in_8bit: false
load_in_4bit: true

chat_template: mistral_v7_tekken
datasets:
  - path: hardlyworking/HardlyRPv2-10k
    type: chat_template
    split: train
    field_messages: conversations
    message_property_mappings:
      role: from
      content: value
      user: human
      assistant: gpt

val_set_size: 0.0
output_dir: ./outputs/out

adapter: qlora
lora_r: 32
lora_alpha: 16
lora_dropout: 0.0
lora_target_linear: true
peft_use_rslora: true


sequence_len: 8192
sample_packing: true
eval_sample_packing: true
pad_to_sequence_len: true

wandb_project: MS32-2
wandb_entity:
wandb_watch:
wandb_name: MS32-2
wandb_log_model:

gradient_accumulation_steps: 32
micro_batch_size: 1
num_epochs: 1
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 2e-5
max_grad_norm: 1.0

bf16: auto
tf32: true

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
  unsloth: true
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_ratio: 0.1
evals_per_epoch:
saves_per_epoch: 4
weight_decay: 0.0025
special_tokens:

```

</details><br>

# MS32-2

This model was trained from scratch on the hardlyworking/HardlyRPv2-10k dataset.

## 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: 2e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 32
- total_train_batch_size: 32
- 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: 6
- training_steps: 67

### Training results



### Framework versions

- PEFT 0.17.0
- Transformers 4.55.0
- Pytorch 2.7.1+cu126
- Datasets 4.0.0
- Tokenizers 0.21.4