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README.md
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---
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# ThoughtStream-4B-v0.1
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This model is based on [h2oai/h2o-danube3-4b-base](https://huggingface.co/h2oai/h2o-danube3-4b-base) and fine-tuned using [LoRA+](https://arxiv.org/abs/2402.12354 "LoRA+: Efficient Low Rank Adaptation of Large Models") with LLama-Factory. It uses the ChatML template, without a system message, and was trained on the [ThoughtfulAssistant-v01](https://huggingface.co/datasets/trollek/ThoughtfulAssistant-v01) dataset.
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The idea is to abstract the thoughts away or into a thought bubble when chatting.
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{{response}}<|im_end|>
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```
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### LLama-Factory
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```yaml
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### model
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model_name_or_path: danube3/thinking-base-chatml
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lora_alpha: 16
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use_unsloth: true
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upcast_layernorm: true
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seed:
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additional_target: embed_tokens
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### dataset
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### train
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per_device_train_batch_size: 4
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gradient_accumulation_steps:
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learning_rate: 0.00001
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num_train_epochs:
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lr_scheduler_type: cosine
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warmup_ratio: 0.01
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bf16: true
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flash_attn: fa2
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### eval
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val_size: 0.
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per_device_eval_batch_size: 1
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eval_strategy: steps
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eval_steps:
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```
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---
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# ThoughtStream-4B-v0.1
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This model is based on [h2oai/h2o-danube3-4b-base](https://huggingface.co/h2oai/h2o-danube3-4b-base) and fine-tuned using [LoRA+](https://arxiv.org/abs/2402.12354 "LoRA+: Efficient Low Rank Adaptation of Large Models") and [BAdam](https://arxiv.org/abs/2404.02827 "BAdam: A Memory Efficient Full Parameter Optimization Method for Large Language Models") with LLama-Factory. It uses the ChatML template, without a system message, and was trained on the [ThoughtfulAssistant-v01](https://huggingface.co/datasets/trollek/ThoughtfulAssistant-v01) dataset.
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The idea is to abstract the thoughts away or into a thought bubble when chatting.
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{{response}}<|im_end|>
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```
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### LLama-Factory configs
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```yaml
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### model
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model_name_or_path: danube3/thinking-base-chatml
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lora_alpha: 16
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use_unsloth: true
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upcast_layernorm: true
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seed: 24
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additional_target: embed_tokens
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### dataset
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### train
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per_device_train_batch_size: 4
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gradient_accumulation_steps: 4
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learning_rate: 0.00001
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num_train_epochs: 2
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lr_scheduler_type: cosine
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warmup_ratio: 0.01
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bf16: true
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flash_attn: fa2
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### eval
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val_size: 0.01
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per_device_eval_batch_size: 1
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eval_strategy: steps
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eval_steps: 500
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```
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```yaml
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### model
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model_name_or_path: danube3/thinking-base-chatml/merged_loraplus
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### method
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stage: sft
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do_train: true
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finetuning_type: full
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use_badam: true
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badam_switch_mode: ascending
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badam_start_block: 7
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badam_switch_interval: 20
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badam_verbose: 1
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seed: 768
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### dataset
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dataset: thinking_capybara,thinking_panoia
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template: hermes_chatml
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cutoff_len: 8192
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overwrite_cache: false
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preprocessing_num_workers: 12
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### output
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output_dir: danube3/ThoughtStream-4B-v0.1
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logging_steps: 1
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save_steps: 1
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save_strategy: epoch
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plot_loss: true
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overwrite_output_dir: false
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### train
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per_device_train_batch_size: 1
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gradient_accumulation_steps: 4
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learning_rate: 0.00001
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num_train_epochs: 1
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lr_scheduler_type: constant_with_warmup
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warmup_ratio: 0.01
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pure_bf16: true
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flash_attn: fa2
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### eval
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val_size: 0.01
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per_device_eval_batch_size: 1
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eval_strategy: steps
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eval_steps: 200
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```
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