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README.md ADDED
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+ ---
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+ license: apache-2.0
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+ library_name: peft
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+ tags:
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+ - generated_from_trainer
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+ base_model: TinyLlama/TinyLlama-1.1B-intermediate-step-1195k-token-2.5T
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+ model-index:
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+ - name: airoboros-lora-out
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+ results: []
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+ ---
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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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+
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+ [<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
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+ # airoboros-lora-out
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+
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+ This model is a fine-tuned version of [TinyLlama/TinyLlama-1.1B-intermediate-step-1195k-token-2.5T](https://huggingface.co/TinyLlama/TinyLlama-1.1B-intermediate-step-1195k-token-2.5T) on the None dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.7230
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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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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: 8
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+ - eval_batch_size: 8
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+ - seed: 42
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+ - distributed_type: multi-GPU
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+ - num_devices: 2
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+ - total_train_batch_size: 16
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+ - total_eval_batch_size: 16
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+ - optimizer: Adam with betas=(0.999,0.95) and epsilon=1e-08
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+ - lr_scheduler_type: cosine
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+ - lr_scheduler_warmup_steps: 100
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+ - num_epochs: 1
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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss |
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+ |:-------------:|:-----:|:----:|:---------------:|
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+ | 0.9777 | 0.0 | 1 | 1.0628 |
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+ | 0.6566 | 0.5 | 379 | 0.7230 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.36.2
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+ - Pytorch 2.0.1+cu117
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+ - Datasets 2.16.0
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+ - Tokenizers 0.15.0
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+ ## Training procedure
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+
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+
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+ The following `bitsandbytes` quantization config was used during training:
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+ - quant_method: bitsandbytes
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+ - load_in_8bit: True
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+ - load_in_4bit: False
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+ - llm_int8_threshold: 6.0
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+ - llm_int8_skip_modules: None
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+ - llm_int8_enable_fp32_cpu_offload: False
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+ - llm_int8_has_fp16_weight: False
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+ - bnb_4bit_quant_type: fp4
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+ - bnb_4bit_use_double_quant: False
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+ - bnb_4bit_compute_dtype: float32
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+
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+ ### Framework versions
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+
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+
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+ - PEFT 0.6.0
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+ {
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+ "alpha_pattern": {},
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+ "auto_mapping": null,
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+ "base_model_name_or_path": "TinyLlama/TinyLlama-1.1B-intermediate-step-1195k-token-2.5T",
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+ "bias": "none",
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+ "fan_in_fan_out": null,
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+ "inference_mode": true,
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+ "init_lora_weights": true,
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+ "layers_pattern": null,
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+ "layers_to_transform": null,
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+ "lora_alpha": 64,
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+ "lora_dropout": 0.1,
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+ "peft_type": "LORA",
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+ "r": 128,
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+ "target_modules": [
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+ "o_proj",
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+ "up_proj",
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+ "q_proj",
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+ "down_proj",
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+ "gate_proj",
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+ "v_proj",
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+ "k_proj"
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+ ],
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+ "task_type": "CAUSAL_LM"
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+ }
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+ "hidden_size": 2048,
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+ "initializer_range": 0.02,
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+ "intermediate_size": 5632,
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+ base_model: TinyLlama/TinyLlama-1.1B-intermediate-step-1195k-token-2.5T
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+ model_type: LlamaForCausalLM
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+ tokenizer_type: LlamaTokenizer
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+ is_llama_derived_model: true
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+
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+ load_in_8bit: true
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+ load_in_4bit: false
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+ strict: false
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+
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+ datasets:
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+ - path: jondurbin/airoboros-3.1
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+ type: sharegpt
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+ dataset_prepared_path:
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+ val_set_size: 0.01
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+ output_dir: ./airoboros-lora-out
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+
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+ sequence_len: 2048
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+ sample_packing: true
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+
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+ adapter: lora
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+ lora_model_dir:
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+ lora_r: 128
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+ lora_alpha: 64
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+ lora_dropout: 0.1
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+ lora_target_linear: true
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+ lora_fan_in_fan_out:
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+ wandb_project: airoboros-tinyllama
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+ wandb_entity:
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+ wandb_watch:
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+ wandb_name:
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+ wandb_log_model:
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+ gradient_accumulation_steps: 1
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+ micro_batch_size: 8
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+ num_epochs: 1
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+ optimizer: adamw_bnb_8bit
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+ lr_scheduler: cosine
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+ learning_rate: 0.0002
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+ adam_beta1: 0.999
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+ adam_beta2: 0.95
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+ adam_eps: 0.00001
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+ train_on_inputs: false
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+ group_by_length: false
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+ bf16: true
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+ tf32: true
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+ gradient_checkpointing: true
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+ xformers_attention:
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+ warmup_steps: 100
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+ evals_per_epoch: 2
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+ eval_table_size:
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+ saves_per_epoch: 2
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+ debug:
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+ weight_decay: 0.0
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