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--- |
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license: apache-2.0 |
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datasets: |
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- teknium/OpenHermes-2.5 |
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- abhinand/ultrachat_200k_sharegpt |
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language: |
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- en |
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--- |
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# TinyLLaMA OpenHermes2.5 [Work in Progress] (Quantized) |
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This a finetune of TinyLLaMA base model finetuned on [OpenHermes 2.5](https://huggingface.co/datasets/teknium/OpenHermes-2.5) and [UltraChat 200k](https://huggingface.co/datasets/abhinand/ultrachat_200k_sharegpt) for a single epoch. |
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Training was generously supported by [Jarvislabs.ai](https://jarvislabs.ai/). |
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If you appreciate this work and would like to support its continued development, consider [buying me a coffee](https://www.buymeacoffee.com/abhinand.b). Your support is invaluable and greatly appreciated. |
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[](https://www.buymeacoffee.com/abhinand.b) |
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<details><summary>See axolotl config</summary> |
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axolotl version: `0.4.0` |
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```yaml |
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base_model: TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T |
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model_type: AutoModelForCausalLM |
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tokenizer_type: AutoTokenizer |
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trust_remote_code: true |
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is_llama_derived_model: true |
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# huggingface repo |
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datasets: |
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- path: teknium/OpenHermes-2.5 |
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type: sharegpt |
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conversation: chatml |
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train_on_split: train |
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- path: abhinand/ultrachat_200k_sharegpt |
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type: sharegpt |
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conversation: chatml |
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train_on_split: train |
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load_in_4bit: false |
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load_in_8bit: false |
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bf16: true # require >=ampere |
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chat_template: chatml |
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dataset_prepared_path: last_run_prepared_path |
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hub_model_id: abhinand/TinyLlama-1.1B-OpenHermes-2.5-Chat-v1.0 |
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group_by_length: false |
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val_set_size: 0.0 |
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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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adapter: lora |
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lora_model_dir: |
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lora_r: 32 |
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lora_alpha: 16 |
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lora_target_modules: |
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- q_proj |
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- v_proj |
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- k_proj |
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- o_proj |
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- gate_proj |
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- down_proj |
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- up_proj |
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lora_modules_to_save: |
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- embed_tokens |
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- lm_head |
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lora_dropout: 0.05 |
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lora_target_linear: true |
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lora_fan_in_fan_out: |
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output_dir: /home/tiny-llama/trained_models |
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gradient_accumulation_steps: 2 |
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micro_batch_size: 32 |
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eval_batch_size: 32 |
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num_epochs: 1 |
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logging_steps: 1 |
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save_steps: 50 |
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save_total_limit: 3 |
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save_safetensors: true |
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gradient_checkpointing: true |
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lr_scheduler: cosine |
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optimizer: "adamw_bnb_8bit" |
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adam_beta2: 0.95 |
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adam_epsilon: 0.00001 |
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weight_decay: 0.1 |
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learning_rate: 0.0005 |
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max_grad_norm: 1.0 |
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warmup_ratio: 0.05 |
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# warmup_steps: 100 |
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flash_attention: true |
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# Resume from a specific checkpoint dir |
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resume_from_checkpoint: |
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# If resume_from_checkpoint isn't set and you simply want it to start where it left off. |
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# Be careful with this being turned on between different models. |
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# auto_resume_from_checkpoints: true |
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# wandb configuration if you're using it |
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# Make sure your `WANDB_API_KEY` environment variable is set (recommended) or you login to wandb with `wandb login`. |
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wandb_mode: # "offline" to save run metadata locally and not sync to the server, "disabled" to turn off wandb |
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wandb_project: "tiny-llama-sft" |
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wandb_name: |
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wandb_run_id: |
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special_tokens: |
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bos_token: "<s>" |
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eos_token: "</s>" |
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unk_token: "<unk>" |
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tokens: # these are delimiters |
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- "<|im_start|>" |
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- "<|im_end|>" |
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``` |
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</details> |
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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: 0.0005 |
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- train_batch_size: 32 |
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- eval_batch_size: 32 |
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- seed: 42 |
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- gradient_accumulation_steps: 2 |
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- total_train_batch_size: 64 |
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- optimizer: Adam with betas=(0.9,0.95) and epsilon=1e-05 |
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- lr_scheduler_type: cosine |
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- lr_scheduler_warmup_steps: 476 |
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- num_epochs: 1 |
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### Framework versions |
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- PEFT 0.8.2 |
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- Transformers 4.38.0.dev0 |
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- Pytorch 2.0.1 |
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- Datasets 2.16.1 |
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- Tokenizers 0.15.0 |
