Create README.md

README.md

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+---
+license: apache-2.0
+datasets:
+- teknium/OpenHermes-2.5
+- abhinand/ultrachat_200k_sharegpt
+language:
+- en
+---
+
+# TinyLLaMA OpenHermes2.5 [Work in Progress]
+
+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. 
+
+Training was generously supported by [Jarvislabs.ai](https://jarvislabs.ai/).
+
+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.
+
+[!["Buy Me A Coffee"](https://www.buymeacoffee.com/assets/img/custom_images/orange_img.png)](https://www.buymeacoffee.com/abhinand.b)
+
+<details><summary>See axolotl config</summary>
+
+axolotl version: `0.4.0`
+```yaml
+base_model: TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T
+model_type: AutoModelForCausalLM
+tokenizer_type: AutoTokenizer
+trust_remote_code: true
+is_llama_derived_model: true
+
+# huggingface repo
+datasets:
+  - path: teknium/OpenHermes-2.5
+    type: sharegpt
+    conversation: chatml
+    train_on_split: train
+
+  - path: abhinand/ultrachat_200k_sharegpt
+    type: sharegpt
+    conversation: chatml
+    train_on_split: train
+
+load_in_4bit: false
+load_in_8bit: false
+bf16: true # require >=ampere
+chat_template: chatml
+
+dataset_prepared_path: last_run_prepared_path
+hub_model_id: abhinand/TinyLlama-1.1B-OpenHermes-2.5-Chat-v1.0
+group_by_length: false
+
+val_set_size: 0.0
+sequence_len: 2048
+sample_packing: true
+pad_to_sequence_len: true
+
+adapter: lora
+lora_model_dir:
+lora_r: 32
+lora_alpha: 16
+lora_target_modules:
+  - q_proj
+  - v_proj
+  - k_proj
+  - o_proj
+  - gate_proj
+  - down_proj
+  - up_proj
+lora_modules_to_save:
+  - embed_tokens
+  - lm_head
+lora_dropout: 0.05
+lora_target_linear: true
+lora_fan_in_fan_out:
+
+output_dir: /home/tiny-llama/trained_models
+
+gradient_accumulation_steps: 2
+micro_batch_size: 32
+eval_batch_size: 32
+num_epochs: 1
+logging_steps: 1
+save_steps: 50
+save_total_limit: 3
+
+save_safetensors: true
+gradient_checkpointing: true
+
+lr_scheduler: cosine
+optimizer: "adamw_bnb_8bit"
+adam_beta2: 0.95
+adam_epsilon: 0.00001
+weight_decay: 0.1
+learning_rate: 0.0005
+max_grad_norm: 1.0
+warmup_ratio: 0.05
+# warmup_steps: 100
+
+flash_attention: true
+
+# Resume from a specific checkpoint dir
+resume_from_checkpoint:
+# If resume_from_checkpoint isn't set and you simply want it to start where it left off.
+# Be careful with this being turned on between different models.
+# auto_resume_from_checkpoints: true
+
+# wandb configuration if you're using it
+# Make sure your `WANDB_API_KEY` environment variable is set (recommended) or you login to wandb with `wandb login`.
+wandb_mode: # "offline" to save run metadata locally and not sync to the server, "disabled" to turn off wandb
+wandb_project: "tiny-llama-sft"
+wandb_name:
+wandb_run_id:
+
+special_tokens:
+  bos_token: "<s>"
+  eos_token: "</s>"
+  unk_token: "<unk>"
+tokens: # these are delimiters
+  - "<|im_start|>"
+  - "<|im_end|>"
+
+```
+
+</details>
+
+## Training procedure
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 0.0005
+- train_batch_size: 32
+- eval_batch_size: 32
+- seed: 42
+- gradient_accumulation_steps: 2
+- total_train_batch_size: 64
+- optimizer: Adam with betas=(0.9,0.95) and epsilon=1e-05
+- lr_scheduler_type: cosine
+- lr_scheduler_warmup_steps: 476
+- num_epochs: 1
+
+### Framework versions
+
+- PEFT 0.8.2
+- Transformers 4.38.0.dev0
+- Pytorch 2.0.1
+- Datasets 2.16.1
+- Tokenizers 0.15.0