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library_name: peft
license: apache-2.0
base_model: google/gemma-4-31B-it
tags:
- axolotl
- base_model:adapter:google/gemma-4-31B-it
- lora
- transformers
datasets:
- ConicCat/Mura_Books
pipeline_tag: text-generation
model-index:
- name: Writer-Stage-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. -->
[<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: `0.16.0.dev0`
```yaml
base_model: google/gemma-4-31B-it
load_in_8bit: false
load_in_4bit: false
plugins:
- axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin
- axolotl.integrations.liger.LigerPlugin
torch_compile: false
liger_layer_norm: true
liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_rms_norm_gated: true
strict: false
sequence_len: 2048
max_sample_length: 2048
flash_attention: false
sdp_attention: true
sample_packing: true
gradient_checkpointing: true
activation_offloading: true
bf16: true
tf32: true
lora_mlp_kernel: false
lora_qkv_kernel: false
lora_o_kernel: false
datasets:
- path: ConicCat/Mura_Books
type: chat_template
chat_template_jinja: >
{%- macro strip_thinking(text) -%}
{%- set ns = namespace(result='') -%}
{%- for part in text.split('<channel|>') -%}
{%- if '<|channel>' in part -%}
{%- set ns.result = ns.result + part.split('<|channel>')[0] -%}
{%- else -%}
{%- set ns.result = ns.result + part -%}
{%- endif -%}
{%- endfor -%}
{{- ns.result | trim -}}
{%- endmacro -%}
{%- set loop_messages = messages -%}
{{ bos_token }}
{#- Handle System Definitions Block -#}
{%- if (enable_thinking is defined and enable_thinking) or messages[0]['role'] in ['system', 'developer'] -%}
{{- '<|turn>system\n' -}}
{#- Inject Thinking token at the very top of the FIRST system turn -#}
{%- if enable_thinking is defined and enable_thinking -%}
{{- '<|think|>' -}}
{%- endif -%}
{%- if messages[0]['role'] in ['system', 'developer'] -%}
{{- messages[0]['content'] | trim -}}
{%- set loop_messages = messages[1:] -%}
{%- endif -%}
{{- '<turn|>\n' -}}
{%- endif %}
{#- Loop through messages -#}
{%- for message in loop_messages -%}
{%- set role = 'model' if message['role'] == 'assistant' else message['role'] -%}
{{- '<|turn>' + role + '\n' -}}
{#- Flag to identify the final SFT turn -#}
{%- set is_final_sft_turn = loop.last and not add_generation_prompt -%}
{%- if message['content'] is string -%}
{%- if role == 'model' -%}
{%- if is_final_sft_turn and '<|channel>thought' not in message['content'] -%}
{{- '<|channel>thought\n<channel|>' -}}
{%- endif -%}
{{- strip_thinking(message['content']) -}}
{%- else -%}
{{- message['content'] | trim -}}
{%- endif -%}
{%- elif message['content'] is sequence -%}
{%- set ns = namespace(has_thinking=false) -%}
{%- for item in message['content'] -%}
{%- if item['type'] == 'text' and '<|channel>thought' in item['text'] -%}
{%- set ns.has_thinking = true -%}
{%- endif -%}
{%- endfor -%}
{%- if role == 'model' and is_final_sft_turn and not ns.has_thinking -%}
{{- '<|channel>thought\n<channel|>' -}}
{%- endif -%}
{%- for item in message['content'] -%}
{%- if item['type'] == 'text' -%}
{%- if role == 'model' -%}
{{- strip_thinking(item['text']) -}}
{%- else -%}
{{- item['text'] | trim -}}
{%- endif -%}
{%- endif -%}
{%- endfor -%}
{%- endif -%}
{{- '<turn|>\n' -}}
{%- endfor -%}
{#- Generation Prompt handled as normal (serves as the final turn when true) -#}
{%- if add_generation_prompt -%}
{{- '<|turn>model\n' -}}
{%- if not enable_thinking | default(false) -%}
{{- '<|channel>thought\n<channel|>' -}}
{%- endif -%}
{%- endif -%}
adapter: lora
lora_r: 32
lora_alpha: 64
lora_dropout: 0.0
lora_bias: None
lora_target_modules: 'model.language_model.layers.[\d]+.(_checkpoint_wrapped_module.)?(mlp|self_attn).(up|down|gate|q|k|v|o)_proj'
use_tensorboard: true
optimizer: paged_adamw_8bit
learning_rate: 2.5e-5 # 1e-4 / 4
loraplus_lr_ratio: 16
# Training arguments
output_dir: ./Writer-Stage-2
num_epochs: 11
micro_batch_size: 2
gradient_accumulation_steps: 4
save_strategy: 'no'
warmup_ratio: 0.05
lr_scheduler: 'cosine'
max_grad_norm: 1
logging_steps: 1
seed: 42
eot_tokens:
- "<turn|>"
push_dataset_to_hub: ConicCat/Gemma4-Mura
hf_use_auth_token: true
```
</details><br>
# Writer-Stage-2
This model is a fine-tuned version of [google/gemma-4-31B-it](https://huggingface.co/google/gemma-4-31B-it) on the ConicCat/Mura_Books 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: 2.5e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Use OptimizerNames.PAGED_ADAMW_8BIT 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: 12
- training_steps: 242
### Training results
### Framework versions
- PEFT 0.19.1
- Transformers 5.5.0
- Pytorch 2.8.0+cu128
- Datasets 4.5.0
- Tokenizers 0.22.2 |