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--- |
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license: apache-2.0 |
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base_model: Llama-2-7B-bf16-sharded |
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model-index: |
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- name: llama_ft |
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results: [] |
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--- |
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# llama_ft |
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This model is a fine-tuned version of [Llama-2-7B-bf16-sharded](https://huggingface.co/TinyPixel/Llama-2-7B-bf16-sharded) on a grocery cart dataset. |
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## Intended uses & limitations |
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The model helps to tell to what type of grocery does the following items belong to. |
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## Training procedure |
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Fine tuning techniques like Qlora and PEFT have been used to train the model on the dataset on a single gpu , and the adapters are then finally merged with the model. |
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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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The loading configurations of the model |
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### Training hyperparameters |
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The following are the LORA configs--> |
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lora_alpha = 16 |
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lora_dropout = 0.1 |
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lora_r = 64 |
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peft_config = LoraConfig( |
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lora_alpha=lora_alpha, |
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lora_dropout=lora_dropout, |
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r=lora_r, |
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bias="none", |
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task_type="CAUSAL_LM", |
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target_modules=["q_proj","v_proj"] |
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) |
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The following are the training configs --> |
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per_device_train_batch_size = 4 |
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gradient_accumulation_steps = 4 |
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optim = "paged_adamw_32bit" |
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save_steps = 10 |
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logging_steps = 1 |
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learning_rate = 2e-4 |
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max_grad_norm = 0.3 |
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max_steps = 120 |
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warmup_ratio = 0.03 |
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lr_scheduler_type = "constant" |
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