End of training

README.md

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+---
+library_name: peft
+license: gemma
+base_model: google/gemma-2-27b-it
+tags:
+- axolotl
+- generated_from_trainer
+datasets:
+- databricks/databricks-dolly-15k
+model-index:
+- name: gemma-2-27b-it-dolly-15k
+  results: []
+---
+
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
+
+[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
+<details><summary>See axolotl config</summary>
+
+axolotl version: `0.6.0`
+```yaml
+# base_model: meta-llama/Llama-3.2-1B-Instruct
+# # Automatically upload checkpoint and final model to HF
+# # hub_model_id: kweinmeister/Llama-3.2-1B-Instruct-MetaMathQA
+# hub_model_id: kweinmeister/Llama-3.2-1B-Instruct-gsm8k
+
+# load_in_8bit: false
+# load_in_4bit: true
+# strict: false
+
+
+# datasets:
+#   - path: openai/gsm8k
+#     type: alpaca_chat.load_qa
+#     name: "main"
+#     train_on_split: "train"
+
+
+# # datasets:
+# #   - path: meta-math/MetaMathQA
+# #     type:
+# #       field_instruction: query
+# #       field_output: response
+
+# val_set_size: 0.1
+# # output_dir: "/mnt/disks/gcs/axolotl/outputs/out"
+# output_dir: "/mnt/disks/gcs/axolotl/outputs/gsm8k-out"
+# # output_dir: "/mnt/disks/gcs/axolotl/outputs/MetaMathQA-out"
+
+# adapter: qlora
+# lora_model_dir:
+
+# sequence_len: 2048
+# sample_packing: true
+# eval_sample_packing: true
+# pad_to_sequence_len: true
+
+# lora_r: 32
+# lora_alpha: 16
+# lora_dropout: 0.05
+# lora_fan_in_fan_out:
+# lora_target_modules:
+#   - gate_proj
+#   - down_proj
+#   - up_proj
+#   - q_proj
+#   - v_proj
+#   - k_proj
+#   - o_proj
+
+# wandb_project:
+# wandb_entity:
+# wandb_watch:
+# wandb_name:
+# wandb_log_model:
+
+# gradient_accumulation_steps: 4
+# micro_batch_size: 2
+# num_epochs: 3
+# # optimizer: adamw_bnb_8bit
+# optimizer: adamw_torch
+# lr_scheduler: cosine
+# learning_rate: 2e-5
+
+# train_on_inputs: false
+# group_by_length: false
+# bf16: auto
+# fp16:
+# tf32: false
+
+# # gradient_checkpointing: true
+# gradient_checkpointing: false
+# early_stopping_patience:
+# resume_from_checkpoint:
+# local_rank:
+# logging_steps: 1
+# xformers_attention:
+# flash_attention: true
+
+# loss_watchdog_threshold: 5.0
+# loss_watchdog_patience: 3
+
+# warmup_steps: 10
+# evals_per_epoch: 4
+# eval_table_size:
+# eval_max_new_tokens: 128
+# saves_per_epoch: 1
+# debug:
+# deepspeed:
+# weight_decay: 0.0
+# # fsdp:
+# # fsdp_config:
+# fsdp:
+#   - full_shard
+#   - auto_wrap  
+# fsdp_config:
+#   fsdp_limit_all_gathers: true
+#   fsdp_sync_module_states: true
+#   fsdp_offload_params: true
+#   fsdp_use_orig_params: false
+#   fsdp_cpu_ram_efficient_loading: true
+#   fsdp_auto_wrap_policy: TRANSFORMER_BASED_WRAP
+#   fsdp_transformer_layer_cls_to_wrap: LlamaDecoderLayer
+#   fsdp_state_dict_type: FULL_STATE_DICT
+#   fsdp_sharding_strategy: FULL_SHARD
+#   fsdp_activation_checkpointing: true  
+# special_tokens:
+#   # pad_token: "<|end_of_text|>"
+#   special_tokens:
+#   bos_token: "<|begin_of_text|>"
+#   eos_token: "<|eot_id|>"
+#   pad_token: "<|finetune_right_pad_id|>"
+
+base_model: google/gemma-2-27b-it
+# model_type: AutoModelForCausalLM
+# tokenizer_type: AutoTokenizer
+hub_model_id: kweinmeister/gemma-2-27b-it-dolly-15k
+
+load_in_8bit: false
+load_in_4bit: true
+strict: false
+
+datasets:
+  - path: databricks/databricks-dolly-15k
+    type:
+      field_instruction: instruction       
+      field_input: context
+      field_output: response
+
+val_set_size: 0.1
+output_dir: "/mnt/disks/gcs/axolotl/outputs/dolly-15k-out"
+
+adapter: qlora
+
+lora_r: 32
+lora_alpha: 16
+lora_dropout: 0.05
+lora_target_linear: true
+
+sequence_len: 2048
+sample_packing: true
+# eval_sample_packing: true
+pad_to_sequence_len: true
+
+gradient_accumulation_steps: 4
+micro_batch_size: 2
+num_epochs: 3
+# optimizer: adamw_bnb_8bit
+optimizer: adamw_torch
+lr_scheduler: cosine
+learning_rate: 2e-5
+
+
+train_on_inputs: false
+group_by_length: false
+bf16: auto
+fp16:
+tf32: false
+
+
+# gradient_checkpointing: false
+gradient_checkpointing: true
+early_stopping_patience:
+resume_from_checkpoint:
+local_rank:
+logging_steps: 1
+xformers_attention:
+flash_attention: false
+
+# loss_watchdog_threshold: 5.0
+# loss_watchdog_patience: 3
+
+
+warmup_ratio: 0.1
+evals_per_epoch: 4
+eval_max_new_tokens: 128
+saves_per_epoch: 1
+debug:
+# deepspeed:
+weight_decay: 0.0
+
+deepspeed: deepspeed_configs/zero1.json
+
+fsdp:
+fsdp_config:
+# fsdp:
+#   - full_shard
+#   - auto_wrap  
+
+# fsdp_config:
+#   fsdp_auto_wrap_policy: TRANSFORMER_BASED_WRAP
+#   fsdp_backward_prefetch: BACKWARD_PRE
+#   fsdp_cpu_ram_efficient_loading: true
+#   fsdp_forward_prefetch: false
+#   fsdp_offload_params: true
+#   fsdp_sharding_strategy: FULL_SHARD
+#   fsdp_state_dict_type: SHARDED_STATE_DICT
+#   fsdp_transformer_layer_cls_to_wrap: GemmaDecoderLayer
+#   fsdp_sync_module_states: true
+#   fsdp_use_orig_params: true
+
+
+# fsdp_config:
+#   fsdp_limit_all_gathers: true
+#   fsdp_sync_module_states: true
+#   fsdp_offload_params: true
+#   fsdp_use_orig_params: false
+#   fsdp_cpu_ram_efficient_loading: true
+#   fsdp_auto_wrap_policy: TRANSFORMER_BASED_WRAP
+#   fsdp_transformer_layer_cls_to_wrap: GemmaDecoderLayer
+#   fsdp_state_dict_type: FULL_STATE_DICT
+#   fsdp_sharding_strategy: FULL_SHARD
+#   fsdp_activation_checkpointing: true  
+# special_tokens:
+#   # pad_token: "<|end_of_text|>"
+#   special_tokens:
+#   bos_token: "<|begin_of_text|>"
+#   eos_token: "<|eot_id|>"
+#   pad_token: "<|finetune_right_pad_id|>"
+```
+
+</details><br>
+
+# gemma-2-27b-it-dolly-15k
+
+This model is a fine-tuned version of [google/gemma-2-27b-it](https://huggingface.co/google/gemma-2-27b-it) on the databricks/databricks-dolly-15k dataset.
+It achieves the following results on the evaluation set:
+- Loss: 1.6809
+
+## Model description
+
+More information needed
+
+## Intended uses & limitations
+
+More information needed
+
+## Training and evaluation data
+
+More information needed
+
+## Training procedure
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 2e-05
+- train_batch_size: 2
+- eval_batch_size: 2
+- seed: 42
+- distributed_type: multi-GPU
+- num_devices: 2
+- gradient_accumulation_steps: 4
+- total_train_batch_size: 16
+- total_eval_batch_size: 4
+- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
+- lr_scheduler_type: cosine
+- lr_scheduler_warmup_steps: 23
+- num_epochs: 3
+
+### Training results
+
+| Training Loss | Epoch  | Step | Validation Loss |
+|:-------------:|:------:|:----:|:---------------:|
+| 3.8741        | 0.0129 | 1    | 4.1287          |
+| 3.5275        | 0.2589 | 20   | 3.7627          |
+| 2.5496        | 0.5178 | 40   | 2.5361          |
+| 2.1047        | 0.7767 | 60   | 2.0215          |
+| 1.8435        | 1.0259 | 80   | 1.8475          |
+| 1.8821        | 1.2848 | 100  | 1.7748          |
+| 1.834         | 1.5437 | 120  | 1.7345          |
+| 1.7633        | 1.8026 | 140  | 1.7098          |
+| 1.6382        | 2.0647 | 160  | 1.6954          |
+| 1.9356        | 2.3236 | 180  | 1.6863          |
+| 1.6196        | 2.5825 | 200  | 1.6819          |
+| 1.7489        | 2.8414 | 220  | 1.6809          |
+
+
+### Framework versions
+
+- PEFT 0.14.0
+- Transformers 4.47.1
+- Pytorch 2.3.1+cu121
+- Datasets 3.1.0
+- Tokenizers 0.21.0

adapter_model.bin

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