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tuned model full

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README.md ADDED
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+ ---
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+ license: apache-2.0
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+ base_model: HuggingFaceTB/cosmo-1b
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+ tags:
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+ - generated_from_trainer
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+ model-index:
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+ - name: tune-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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+ <details><summary>See axolotl config</summary>
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+
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+ axolotl version: `0.4.0`
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+ ```yaml
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+ base_model: HuggingFaceTB/cosmo-1b
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+ model_type: LlamaForCausalLM
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+ tokenizer_type: LlamaTokenizer
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+
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+ load_in_8bit: false
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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: Vezora/Tested-22k-Python-Alpaca
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+ type: alpaca
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+ dataset_prepared_path: prepared-tune
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+ val_set_size: 0.05
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+ output_dir: ./tune-out
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+
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+ sequence_len: 2048
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+ sample_packing: true
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+ pad_to_sequence_len: true
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+
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+ adapter:
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+ lora_model_dir:
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+ lora_r:
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+ lora_alpha:
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+ lora_dropout:
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+ lora_target_linear:
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+ lora_fan_in_fan_out:
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+
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+ wandb_project: cosmo-python-tune
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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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+
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+ gradient_accumulation_steps: 4
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+ micro_batch_size: 1
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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: 0.0005
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+
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+ train_on_inputs: false
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+ group_by_length: false
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+ bf16: auto
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+ fp16:
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+ tf32: false
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+
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+ gradient_checkpointing: true
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+ early_stopping_patience:
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+ resume_from_checkpoint:
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+ local_rank:
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+ logging_steps: 1
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+ xformers_attention:
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+ flash_attention: true
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+
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+ warmup_steps: 10
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+ evals_per_epoch: 4
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+ saves_per_epoch: 1
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+ debug:
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+ deepspeed:
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+ weight_decay: 0.0
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+ fsdp:
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+ fsdp_config:
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+ special_tokens:
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+
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+ ```
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+
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+ </details><br>
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+
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+ # tune-out
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+
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+ This model is a fine-tuned version of [HuggingFaceTB/cosmo-1b](https://huggingface.co/HuggingFaceTB/cosmo-1b) on the None dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.2049
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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: 0.0005
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+ - train_batch_size: 1
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+ - eval_batch_size: 1
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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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+ - gradient_accumulation_steps: 4
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+ - total_train_batch_size: 8
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+ - total_eval_batch_size: 2
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+ - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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+ - lr_scheduler_type: cosine
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+ - lr_scheduler_warmup_steps: 10
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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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+ | 0.6623 | 0.0 | 1 | 0.6460 |
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+ | 0.6503 | 0.25 | 238 | 0.6117 |
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+ | 0.534 | 0.5 | 476 | 0.3380 |
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+ | 0.3682 | 0.75 | 714 | 0.2049 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.40.0.dev0
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+ - Pytorch 2.1.2+cu118
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+ - Datasets 2.18.0
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+ - Tokenizers 0.15.0
config.json ADDED
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+ {
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+ "architectures": [
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+ "LlamaForCausalLM"
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+ ],
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+ "attention_dropout": 0.0,
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+ "eos_token_id": 2,
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+ "hidden_act": "silu",
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+ "hidden_size": 2048,
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+ "initializer_range": 0.02,
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+ "intermediate_size": 8192,
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+ "max_position_embeddings": 2048,
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+ "model_type": "llama",
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+ "num_attention_heads": 16,
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+ "num_hidden_layers": 24,
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+ "num_key_value_heads": 16,
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+ "pretraining_tp": 1,
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+ "rms_norm_eps": 1e-05,
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+ "rope_scaling": null,
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+ "rope_theta": 10000.0,
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+ "tie_word_embeddings": false,
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+ "torch_dtype": "bfloat16",
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+ "transformers_version": "4.40.0.dev0",
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+ "use_cache": false,
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+ "vocab_size": 32000
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+ }
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+ {
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