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Lllama_AHS_V_7.0

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Lllama_AHS_V_7.0
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---
license: apache-2.0
base_model: openlm-research/open_llama_3b_v2
tags:
- generated_from_trainer
model-index:
- name: working
  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. -->

# working

This model is a fine-tuned version of [openlm-research/open_llama_3b_v2](https://huggingface.co/openlm-research/open_llama_3b_v2) on the None dataset.

## Model description
training_arguments = TrainingArguments(
        per_device_train_batch_size=8,
        num_train_epochs=10,
        learning_rate=3e-5,
        gradient_accumulation_steps=2,
        optim="adamw_hf",
        fp16=True,
        logging_steps=1,
        # debug=True,
        output_dir="/kaggle/Tatvajsh/Lllama_AHS_V_7.0/"
        # warmup_steps=100,
    )

trainer = SFTTrainer(
        model=model,
        tokenizer=tokenizer,
        train_dataset=dataset,
        dataset_text_field="text",
        peft_config=lora_config,
        max_seq_length=512,
        args=training_arguments,
#         packing=True,#change
    )

trainer.train()


EPOCHS=[30-50]


from peft import LoraConfig, get_peft_model

lora_config = LoraConfig(
    r=16,
    lora_alpha=64,
    target_modules=['base_layer','gate_proj', 'v_proj','up_proj','down_proj','q_proj','k_proj','o_proj'],
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM"
)


def generate_prompt(row) -> str:
    prompt=f"""
    Below is an instruction that describes a task. Write a response that appropriately completes the request.
    
    ### Instruction:
    
    {row['Instruction']} 
    
    ### Response:
    
    {row['Answer']}  
    
    ### End
    """
    return prompt


WHEN THE TRAINING LOSS IN NOT REDUCING THEN TRY SETTING FOR LESSER VALUE OF LEARNING RATE I.E. 2E-5 TO 3E-5,ETC.
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: 3e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
- mixed_precision_training: Native AMP

### Training results



### Framework versions

- Transformers 4.35.0.dev0
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.14.1