---
library_name: peft
license: apache-2.0
base_model: Qwen/Qwen3-4B
tags:
- generated_from_trainer
datasets:
- laurent-maille/plc-draft2
model-index:
- name: outputs/ax_qwen3_4b_8bit
  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: Qwen/Qwen3-4B
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
adapter: lora
lora_r: 32
lora_alpha: 32
lora_dropout: 0.05
lora_target_modules:
- q_proj
- k_proj
- v_proj
- o_proj
- gate_proj
- up_proj
- down_proj
datasets:
- path: laurent-maille/plc-draft2
  type: chat_template
  field_messages: messages
  conversation: chat
dataset_prepared_path: ./prepared/plc_sharegpt
output_dir: ./outputs/ax_qwen3_4b_8bit
sequence_len: 4096
sample_packing: true
gradient_checkpointing: true
use_flash_attention: true
bf16: true
tf32: true
seed: 42
optimizer: adamw_bnb_8bit
learning_rate: 0.0001
weight_decay: 0.0
lr_scheduler: cosine
warmup_ratio: 0.03
micro_batch_size: 1
gradient_accumulation_steps: 8
num_epochs: 1
logging_steps: 10
save_strategy: steps
save_steps: 500
eval_steps: 0
evaluation_strategy: 'no'
cutoff_len: 4096
flash_attn_impl: fa2
load_in_8bit: true
fp16: false
packing: true
pad_to_sequence_len: true
group_by_length: true
max_grad_norm: 0.3
save_safetensors: true
save_total_limit: 2

```

</details><br>

# outputs/ax_qwen3_4b_8bit

This model is a fine-tuned version of [Qwen/Qwen3-4B](https://huggingface.co/Qwen/Qwen3-4B) on the laurent-maille/plc-draft2 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: 0.0001
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 8
- 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
- num_epochs: 1.0

### Training results



### Framework versions

- PEFT 0.14.0
- Transformers 4.55.4
- Pytorch 2.5.1+cu124
- Datasets 3.2.0
- Tokenizers 0.21.0