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--- |
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base_model: ai-forever/ruGPT-3.5-13B |
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library_name: peft |
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license: mit |
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datasets: |
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- korotkov/glaive-function-calling-v2-ru-parsed |
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language: |
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- ru |
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tags: |
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- impruver |
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- russian |
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- function call |
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- lora |
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pipeline_tag: text-generation |
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--- |
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# ruGPT-3.5-13B / function call |
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LoRA адаптер для ruGPT3.5-13B обученный на датасете function call. |
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Конфигурация: https://github.com/EvilFreelancer/impruver/blob/main/recipes/configs/ruGPT-3.5/13B_lora_fc.yaml |
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Адаптер обучался на 1x RTX 4090, для этого потребовалось примерно 20Gb VRAM и заняло 11h 8m. |
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```yaml |
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output_dir: ./models/ruGPT35_13B_lora_fc |
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train_path: ./train.ruGPT35_13B_fc.jsonl |
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val_path: ./val.ruGPT35_13B_fc.jsonl |
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datasets: |
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- name: korotkov/glaive-function-calling-v2-ru-parsed |
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split: train |
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model: |
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class: transformers.AutoModelForCausalLM |
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name: ai-forever/ruGPT-3.5-13B |
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load_in_4bit: true |
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load_in_8bit: false |
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dtype: bf16 |
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lora: |
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r: 16 |
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lora_alpha: 16 |
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lora_dropout: 0.05 |
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bias: none |
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target_modules: [ c_attn ] |
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task_type: CAUSAL_LM |
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tokenizer: |
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class: transformers.AutoTokenizer |
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name: ai-forever/ruGPT-3.5-13B |
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max_tokens_count: 1200 |
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trainer: |
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eval_strategy: steps |
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save_strategy: steps |
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eval_steps: 100 |
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save_steps: 100 |
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per_device_train_batch_size: 1 |
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per_device_eval_batch_size: 1 |
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gradient_accumulation_steps: 128 |
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logging_steps: 1 |
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learning_rate: 0.0002 |
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num_train_epochs: 2 |
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lr_scheduler_type: cosine |
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warmup_steps: 16 |
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optim: adamw_8bit |
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metric_for_best_model: eval_loss |
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load_best_model_at_end: true |
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save_total_limit: 2 |
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seed: 42 |
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remove_unused_columns: false |
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max_grad_norm: 1.0 |
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weight_decay: 0.08 |
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torch_compile: false |
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``` |
