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@@ -14,146 +14,19 @@ model-index:
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  should probably proofread and complete it, then remove this comment. -->
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  [<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)
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- <details><summary>See axolotl config</summary>
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- axolotl version: `0.4.1`
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- ```yaml
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- # python -m axolotl.cli.preprocess adventure-nemo.yml
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- # accelerate launch -m axolotl.cli.train adventure-nemo.yml
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- # python -m axolotl.cli.merge_lora adventure-nemo.yml
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- base_model: unsloth/Mistral-Nemo-Base-2407
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- model_type: AutoModelForCausalLM
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- tokenizer_type: AutoTokenizer
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-
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- load_in_8bit: false
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- load_in_4bit: true
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- strict: false
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- sequence_len: 8192 # 99% vram
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- bf16: auto
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- fp16:
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- tf32: false
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- flash_attention: true
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- special_tokens:
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-
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- # Data
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- dataset_prepared_path: last_run_prepared
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- datasets:
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- - path: ColumbidAI/adventure-8k
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- type: completion
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- warmup_steps: 10
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- shuffle_merged_datasets: true
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-
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- save_safetensors: true
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- saves_per_epoch: 4
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- save_total_limit: 2
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-
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- # WandB
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- wandb_project: Nemo-A
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- wandb_entity:
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-
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- # Iterations
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- num_epochs: 1
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-
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- # Output
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- output_dir: ./adventure-command-r-workspace
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- hub_model_id: ToastyPigeon/adventure-nemo-ws
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- hub_strategy: "all_checkpoints"
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-
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- # Sampling
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- sample_packing: true
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- pad_to_sequence_len: true
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-
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- # Batching
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- gradient_accumulation_steps: 1
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- micro_batch_size: 4
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- gradient_checkpointing: 'unsloth'
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- gradient_checkpointing_kwargs:
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- use_reentrant: true
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-
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- #unsloth_cross_entropy_loss: true
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- #unsloth_lora_mlp: true
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- #unsloth_lora_qkv: true
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- #unsloth_lora_o: true
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-
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- # Evaluation
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- val_set_size: 0.005
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- evals_per_epoch: 5
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- eval_table_size:
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- eval_max_new_tokens: 256
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- eval_sample_packing: false
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- eval_batch_size: 1
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-
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- # LoRA
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- adapter: qlora
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- lora_model_dir:
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- lora_r: 64
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- lora_alpha: 32
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- lora_dropout: 0.125
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- lora_target_linear:
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- lora_fan_in_fan_out:
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- lora_target_modules:
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- - gate_proj
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- - down_proj
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- - up_proj
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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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- lora_modules_to_save:
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- # Optimizer
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- optimizer: paged_adamw_8bit # adamw_8bit
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- lr_scheduler: cosine
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- learning_rate: 0.00025
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- lr_scheduler: cosine_with_min_lr
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- lr_scheduler_kwargs:
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- min_lr: 0.000025
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- weight_decay: 0.01
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- max_grad_norm: 20.0
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- # Misc
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- train_on_inputs: false
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- group_by_length: false
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- early_stopping_patience:
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- local_rank:
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- logging_steps: 1
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- xformers_attention:
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- debug:
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- #deepspeed: /workspace/axolotl/deepspeed_configs/zero3.json # previously blank
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- fsdp:
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- fsdp_config:
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- plugins:
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- - axolotl.integrations.liger.LigerPlugin
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- liger_rope: true
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- liger_rms_norm: true
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- liger_swiglu: true
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- liger_fused_linear_cross_entropy: true
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- ```
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- </details><br>
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-
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- # adventure-nemo-ws
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-
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- This model is a fine-tuned version of [unsloth/Mistral-Nemo-Base-2407](https://huggingface.co/unsloth/Mistral-Nemo-Base-2407) on the None dataset.
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- It achieves the following results on the evaluation set:
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- - Loss: 2.1587
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-
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- ## Model description
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-
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- More information needed
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-
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- ## Intended uses & limitations
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-
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- More information needed
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-
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- ## Training and evaluation data
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-
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- More information needed
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-
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- ## Training procedure
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  ### Training hyperparameters
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@@ -167,17 +40,6 @@ The following hyperparameters were used during training:
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  - lr_scheduler_warmup_steps: 10
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  - num_epochs: 1
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- ### Training results
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-
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- | Training Loss | Epoch | Step | Validation Loss |
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- |:-------------:|:------:|:----:|:---------------:|
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- | 1.9422 | 0.0011 | 1 | 2.3948 |
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- | 1.8427 | 0.2011 | 189 | 2.2440 |
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- | 1.6786 | 0.4021 | 378 | 2.2143 |
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- | 1.9847 | 0.6032 | 567 | 2.1799 |
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- | 1.8358 | 0.8043 | 756 | 2.1587 |
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-
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-
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  ### Framework versions
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  - PEFT 0.12.0
 
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  should probably proofread and complete it, then remove this comment. -->
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  [<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)
 
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+ # adventure-nemo-QLoRA
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ Another QLoRA on Mistral Nemo Base, this time with Spring Dragon *and* Skein data included. ~29M tokens total of text adventure data.
 
 
 
 
 
 
 
 
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+ This was trained in **completion** format where user input is given as `> User input`. Set `>` as a stopping string and preface your input with `>` to use with classic text completion mode.
 
 
 
 
 
 
 
 
 
 
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+ This method of use is set up as default in Kobold Lite's Adventure mode.
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+ **Again, no instruct format was trained into this.**
 
 
 
 
 
 
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+ Apply to a Nemo model and use whatever instruct format that model uses - the style (and deadliness) of the LoRA carries over to instruct usage as well.
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ### Training hyperparameters
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  - lr_scheduler_warmup_steps: 10
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  - num_epochs: 1
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  ### Framework versions
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  - PEFT 0.12.0