---
license: other
library_name: peft
tags:
- trl
- sft
- generated_from_trainer
base_model: google/gemma-7b
model-index:
- name: outputs
  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. -->

## Model description

GOOGLEGEMMA modelini UZB datasetga fine-tuned qilindi  PEFT bilan. natijasi yaxshi deyishish qiyin. 
Shuning uchun PEFT siz qilishni tafsiya qilaman .

**Agarda siz PEFT bilan fine-tuned qilingan modellarni ishlatishni bilmasangiz, exmaple codega qarang**

```
    import torch
    from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig, AutoTokenizer
    
    model_name = "google/gemma-7b"
    
    bnb_config = BitsAndBytesConfig(
        load_in_4bit=True,
        bnb_4bit_quant_type="nf4",
        bnb_4bit_compute_dtype=torch.float16,
    )
    
    model = AutoModelForCausalLM.from_pretrained(
        model_name,
        quantization_config=bnb_config,
        trust_remote_code=True
    )
    model.config.use_cache = False

    ##### yuqoridagi code hamma PEFT bilan qilingan modellarni reduced par qilish orqali free GPU Notebooklarda foydalanish imkoni beradi.

    from peft import PeftModel, PeftConfig
    from transformers import AutoModelForCausalLM,AutoTokenizer
    
    config = PeftConfig.from_pretrained("ai-nightcoder/outputs")
    tokenizer = AutoTokenizer.from_pretrained('ai-nightcoder/outputs')

    inputs = tokenizer("Xorijiy mamlakatlar", return_tensors="pt")
    outputs = model(**inputs, labels=inputs["input_ids"])
    predicted_token_class_ids = outputs.logits.argmax(-1)

    generated_text = tokenizer.batch_decode(predicted_token_class_ids, skip_special_tokens=True)
    print(generated_text)

```

## 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: 1
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2
- training_steps: 10
- mixed_precision_training: Native AMP

### Training results



### Framework versions

- PEFT 0.9.0
- Transformers 4.38.2
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2