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--- |
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license: other |
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library_name: peft |
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tags: |
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- trl |
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- sft |
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- generated_from_trainer |
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base_model: google/gemma-7b |
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model-index: |
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- name: outputs |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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## Model description |
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GOOGLEGEMMA modelini UZB datasetga fine-tuned qilindi PEFT bilan. natijasi yaxshi deyishish qiyin. |
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Shuning uchun PEFT siz qilishni tafsiya qilaman . |
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**Agarda siz PEFT bilan fine-tuned qilingan modellarni ishlatishni bilmasangiz, exmaple codega qarang** |
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``` |
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import torch |
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from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig, AutoTokenizer |
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model_name = "google/gemma-7b" |
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bnb_config = BitsAndBytesConfig( |
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load_in_4bit=True, |
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bnb_4bit_quant_type="nf4", |
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bnb_4bit_compute_dtype=torch.float16, |
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) |
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model = AutoModelForCausalLM.from_pretrained( |
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model_name, |
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quantization_config=bnb_config, |
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trust_remote_code=True |
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) |
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model.config.use_cache = False |
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##### yuqoridagi code hamma PEFT bilan qilingan modellarni reduced par qilish orqali free GPU Notebooklarda foydalanish imkoni beradi. |
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from peft import PeftModel, PeftConfig |
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from transformers import AutoModelForCausalLM,AutoTokenizer |
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config = PeftConfig.from_pretrained("ai-nightcoder/outputs") |
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tokenizer = AutoTokenizer.from_pretrained('ai-nightcoder/outputs') |
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inputs = tokenizer("Xorijiy mamlakatlar", return_tensors="pt") |
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outputs = model(**inputs, labels=inputs["input_ids"]) |
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predicted_token_class_ids = outputs.logits.argmax(-1) |
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generated_text = tokenizer.batch_decode(predicted_token_class_ids, skip_special_tokens=True) |
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print(generated_text) |
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``` |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 0.0002 |
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- train_batch_size: 1 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 4 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_steps: 2 |
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- training_steps: 10 |
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- mixed_precision_training: Native AMP |
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### Training results |
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### Framework versions |
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- PEFT 0.9.0 |
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- Transformers 4.38.2 |
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- Pytorch 2.1.2 |
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- Datasets 2.18.0 |
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- Tokenizers 0.15.2 |
