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---
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: 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: `0.12.0.dev0`
```yaml
base_model: model
# Automatically upload checkpoint and final model to HF
# hub_model_id: username/custom_model_name
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
# for use with fft to only train on language model layers
# unfrozen_parameters:
# - model.language_model.*
# - lm_head
# - embed_tokens
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: 64
lora_dropout: 0.05
# lora_target_linear: # Does not work with gemma3n currently
lora_target_modules:
- self_attn.q_proj
- self_attn.k_proj
- self_attn.v_proj
- self_attn.o_proj
- mlp.gate_proj
- mlp.up_proj
- mlp.down_proj
sequence_len: 8192
sample_packing: true
eval_sample_packing: true
pad_to_sequence_len: true
wandb_project:
wandb_entity:
wandb_watch:
wandb_name:
wandb_log_model:
gradient_accumulation_steps: 16
micro_batch_size: 1
num_epochs: 1
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 0.0002
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: 0.0
special_tokens:
```
</details><br>
# outputs/out
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: 0.0002
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 16
- total_train_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: 13
- training_steps: 135
### 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 |