FR1-Consulting-LoRA-v4.0

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

axolotl version: 0.13.0.dev0

adapter: lora
base_model: Qwen/Qwen2.5-14B-Instruct
base_model_config: Qwen/Qwen2.5-14B-Instruct

datasets:
  - path: Jahoho/FR1-Consulting-Naver-500
    type: completion
    field: text

output_dir: ./outputs/consulting

lora_r: 8
lora_alpha: 16
lora_dropout: 0.05
lora_target_modules:
  - q_proj
  - v_proj
  - k_proj
  - o_proj
  - gate_proj
  - down_proj
  - up_proj

micro_batch_size: 2
gradient_accumulation_steps: 8
num_epochs: 1
learning_rate: 0.0002
lr_scheduler: cosine
optimizer: adamw_bnb_8bit

sequence_len: 1536
load_in_8bit: true
bf16: true

# Memory optimization
gradient_checkpointing: true
xformers_attention: true

warmup_steps: 10
logging_steps: 5
save_strategy: epoch
save_safetensors: true

</details><br>

outputs/consulting

This model is a fine-tuned version of Qwen/Qwen2.5-14B-Instruct on the Jahoho/FR1-Consulting-Naver-500 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: 2
• eval_batch_size: 2
• seed: 42
• gradient_accumulation_steps: 8
• 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: 10
• training_steps: 127

Training results

Framework versions

• PEFT 0.17.1
• Transformers 4.57.0
• Pytorch 2.7.1+cu126
• Datasets 4.0.0
• Tokenizers 0.22.1