Model save
Browse files- README.md +67 -0
- adapter_config.json +31 -0
- adapter_model.safetensors +3 -0
- all_results.json +8 -0
- runs/Apr30_03-33-37_ip-172-31-69-60.ec2.internal/events.out.tfevents.1714448127.ip-172-31-69-60.ec2.internal.18260.0 +3 -0
- runs/Apr30_03-37-35_ip-172-31-69-60.ec2.internal/events.out.tfevents.1714448265.ip-172-31-69-60.ec2.internal.20637.0 +3 -0
- runs/Apr30_03-38-29_ip-172-31-69-60.ec2.internal/events.out.tfevents.1714448315.ip-172-31-69-60.ec2.internal.21254.0 +3 -0
- runs/Apr30_03-40-35_ip-172-31-69-60.ec2.internal/events.out.tfevents.1714448440.ip-172-31-69-60.ec2.internal.22451.0 +3 -0
- runs/Apr30_03-41-46_ip-172-31-69-60.ec2.internal/events.out.tfevents.1714448511.ip-172-31-69-60.ec2.internal.23313.0 +3 -0
- runs/Apr30_03-42-46_ip-172-31-69-60.ec2.internal/events.out.tfevents.1714448572.ip-172-31-69-60.ec2.internal.24076.0 +3 -0
- runs/Apr30_03-44-15_ip-172-31-69-60.ec2.internal/events.out.tfevents.1714448661.ip-172-31-69-60.ec2.internal.25121.0 +3 -0
- runs/Apr30_03-45-03_ip-172-31-69-60.ec2.internal/events.out.tfevents.1714448708.ip-172-31-69-60.ec2.internal.25755.0 +3 -0
- runs/Apr30_03-46-24_ip-172-31-69-60.ec2.internal/events.out.tfevents.1714448790.ip-172-31-69-60.ec2.internal.26784.0 +3 -0
- special_tokens_map.json +24 -0
- tokenizer.json +0 -0
- tokenizer.model +3 -0
- tokenizer_config.json +42 -0
- train_results.json +8 -0
- trainer_state.json +178 -0
- training_args.bin +3 -0
README.md
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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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- trl
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- sft
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- generated_from_trainer
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base_model: TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T
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model-index:
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- name: tiny-llama-20p
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results: []
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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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# tiny-llama-20p
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This model is a fine-tuned version of [TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T](https://huggingface.co/TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T) on the None dataset.
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It achieves the following results on the evaluation set:
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- Loss: nan
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## Model description
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More information needed
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## Intended uses & limitations
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More information needed
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## Training and evaluation data
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More information needed
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## Training procedure
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### Training hyperparameters
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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: 14
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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: 4
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- gradient_accumulation_steps: 4
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- total_train_batch_size: 224
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- total_eval_batch_size: 4
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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_ratio: 0.1
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- num_epochs: 1
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### Training results
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| Training Loss | Epoch | Step | Validation Loss |
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|:-------------:|:-----:|:----:|:---------------:|
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| 1.0275 | 0.99 | 96 | nan |
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### Framework versions
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- PEFT 0.7.1
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- Transformers 4.39.0.dev0
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- Pytorch 2.2.2+cu121
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- Datasets 2.14.6
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- Tokenizers 0.15.2
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adapter_config.json
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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-1431k-3T",
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"bias": "none",
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"fan_in_fan_out": false,
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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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"loftq_config": {},
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"lora_alpha": 16,
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"lora_dropout": 0.05,
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"megatron_config": null,
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"megatron_core": "megatron.core",
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"modules_to_save": null,
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"peft_type": "LORA",
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"r": 16,
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"rank_pattern": {},
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"revision": null,
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"target_modules": [
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"gate_proj",
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"q_proj",
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"down_proj",
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"v_proj",
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"o_proj",
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"k_proj",
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"up_proj"
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],
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"task_type": "CAUSAL_LM"
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}
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adapter_model.safetensors
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all_results.json
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{
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"epoch": 0.99,
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"train_loss": 1.0698106847703457,
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"train_runtime": 2647.5008,
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"train_samples": 21594,
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"train_samples_per_second": 8.156,
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"train_steps_per_second": 0.036
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}
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special_tokens_map.json
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tokenizer.json
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tokenizer.model
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tokenizer_config.json
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}
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},
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|
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"chat_template": "{% for message in messages %}\n{% if message['role'] == 'user' %}\n{{ '<|user|>\n' + message['content'] + eos_token }}\n{% elif message['role'] == 'system' %}\n{{ '<|system|>\n' + message['content'] + eos_token }}\n{% elif message['role'] == 'assistant' %}\n{{ '<|assistant|>\n' + message['content'] + eos_token }}\n{% endif %}\n{% if loop.last and add_generation_prompt %}\n{{ '<|assistant|>' }}\n{% endif %}\n{% endfor %}",
|
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|
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|
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|
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|
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|
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|
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train_results.json
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{
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"epoch": 0.99,
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"train_steps_per_second": 0.036
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}
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trainer_state.json
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