Create README.md

README.md

@@ -0,0 +1,107 @@
+---
+base_model:
+- elinas/Llama-3-15B-Instruct-zeroed
+library_name: transformers
+tags:
+- mergekit
+- merge
+- finetune
+datasets:
+- Chat-Error/Pure-dove-sharegpt
+license: llama3
+---
+# Llama-3-15B-Instruct-zeroed-ft-v2
+
+This is a QLoRA **finetune** of a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
+
+The model is based on a "zeroed" passthrough merge of [Llama-3-15B-Instruct-zeroed](https://huggingface.co/elinas/Llama-3-15B-Instruct-zeroed)
+
+This was primarily an experiment to see how a passthrough merge will respond to further finetuning of all LoRA modules.
+
+The model was finetuned on **8192 context length** and is likely reliable using RoPE up to 32k.
+
+Further finetuning this model or finetuning the [base model](https://huggingface.co/elinas/Llama-3-15B-Instruct-zeroed) on more samples is encouraged.
+
+**This will be conducted by myself on the 3rd iteration of this model. Until I receive sufficient feedback on comparison between 8B, this finetune will be on hold.**
+
+## Datasets
+
+* [Chat-Error/Pure-dove-sharegpt](https://huggingface.co/datasets/Chat-Error/Pure-dove-sharegpt)
+
+A small, high quality, curated dataset was used as a PoC / validation on stabilizing the model after the original passthrough merge. 
+
+## Finetuning details
+This is a QLoRA model and all of the LoRA modules were targeted this time to ensure sufficient training before moving on to larger datasets. 
+the first version of this model only targeted **o_proj** and **up_proj**
+```yaml
+lora_target_modules:
+  - gate_proj
+  - down_proj
+  - up_proj
+  - q_proj
+  - v_proj
+  - k_proj
+  - o_proj
+lora_modules_to_save:
+  - embed_tokens
+  - lm_head
+```
+
+The model is coherent even with training the "zeroed" layers plus the additional layers, as this was the recommendation from [Charles Goddard](https://huggingface.co/chargoddard) (mergekit developer) - thank you for sharing the method of merging as well as Toasty
+Pigeon for bringing it to my attention!
+
+```yaml
+The following hyperparameters were used during training:
+- learning_rate: 1e-05
+- train_batch_size: 1
+- eval_batch_size: 1
+- seed: 42
+- distributed_type: multi-GPU
+- num_devices: 3
+- total_train_batch_size: 3
+- total_eval_batch_size: 3
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: cosine
+- lr_scheduler_warmup_steps: 25
+- num_epochs: 1
+```
+
+Optimizer `paged_adamw_8bit` and Deepspeed ZeRO 3 was used at a LR of `1e-5` using the cosine scheduler for 1 epoch on 3x3090s taking 4 hours total.
+
+**Unsloth** was used for speed and memory savings.
+
+Sample packing and padding was disabled to reduce VRAM consumption significantly at the cost of speed.
+
+W&B Run Summary
+```
+wandb:                eval/loss 0.90895
+wandb:             eval/runtime 463.4688
+wandb:  eval/samples_per_second 0.833
+wandb:    eval/steps_per_second 0.278
+wandb:               total_flos 8270790524928.0
+wandb:              train/epoch 1.0
+wandb:        train/global_step 1157
+wandb:          train/grad_norm 7.3847
+wandb:      train/learning_rate 0.0
+wandb:               train/loss 0.8702
+wandb:               train_loss 0.87814
+wandb:            train_runtime 16425.2713
+wandb: train_samples_per_second 0.211
+wandb:   train_steps_per_second 0.07
+```
+
+### Framework versions
+
+- PEFT 0.10.0
+- Transformers 4.40.2
+- Pytorch 2.3.0+cu121
+- Datasets 2.19.1
+- Tokenizers 0.19.1
+
+## Model Evaluation
+
+TBD
+
+If you have any questions or comments on the model, feel free to open a discussion in the community tab.
+
+[<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)