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--- |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: out |
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results: [] |
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language: |
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- en |
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license: llama2 |
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--- |
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Hesperus-v1 - A trained 8-bit LoRA for RP & General Purposes. |
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<br>Trained on the base 13B Llama 2 model. |
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fp16 repo: https://huggingface.co/Sao10K/Hesperus-v1-13B-L2-fp16 |
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<br>GGUF Quants: https://huggingface.co/Sao10K/Hesperus-v1-13B-L2-GGUF |
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Dataset Entry Rows: |
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<br>RP: 8.95K |
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<br>MED: 10.5K |
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<br>General: 8.7K |
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<br>Total: 28.15K |
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This is after heavy filtering of ~500K Rows and Entries from randomly selected scraped sites and datasets. |
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v1 is simply an experimental release. V2 will be the main product? |
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<br>Goals: |
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<br>--- Reduce 28.15K to <10K Entries. |
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<br>--- Adjust RP / Med / General Ratios again. |
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<br>--- Fix Formatting, Markdown in Each Entry. |
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<br>--- Further Filter and Remove Low Quality entries ***again***, with a much harsher pass this time around. |
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<br>--- Do a spellcheck & fix for entries. |
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<br>--- Commit to one prompt format for dataset. Either ShareGPT or Alpaca. Not Both. |
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I recommend keeping Repetition Penalty below 1.1, preferably at 1 as Hesperus begins breaking down at 1.2 Rep Pen and might output nonsense outputs. |
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Prompt Format: |
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``` |
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- sharegpt (recommended!) |
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User: |
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GPT: |
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``` |
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``` |
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- alpaca (less recommended) |
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###Instruction: |
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Your instruction or question here. |
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For roleplay purposes, I suggest the following - Write <CHAR NAME>'s next reply in a chat between <YOUR NAME> and <CHAR NAME>. Write a single reply only. |
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###Response: |
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``` |
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V1 is trained on 50/50 for these two formats. |
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<br>I am working on converting to either for v2. |
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Once V2 is Completed, I will also train a 70B variant of this. |
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EXAMPLE OUTPUTS: |
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*** |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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[<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) |
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# out |
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This model was trained from scratch on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.5134 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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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: 0.0002 |
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- train_batch_size: 8 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- distributed_type: multi-GPU |
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- num_devices: 2 |
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- gradient_accumulation_steps: 16 |
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- total_train_batch_size: 256 |
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- total_eval_batch_size: 16 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: cosine |
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- lr_scheduler_warmup_steps: 10 |
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- num_epochs: 2 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | |
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|:-------------:|:-----:|:----:|:---------------:| |
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| 1.5513 | 0.05 | 1 | 1.6200 | |
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| 1.5555 | 0.11 | 2 | 1.6200 | |
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| 1.5558 | 0.22 | 4 | 1.6180 | |
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| 1.5195 | 0.33 | 6 | 1.6109 | |
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| 1.5358 | 0.44 | 8 | 1.5929 | |
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| 1.5124 | 0.55 | 10 | 1.5740 | |
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| 1.4938 | 0.66 | 12 | 1.5591 | |
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| 1.4881 | 0.77 | 14 | 1.5495 | |
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| 1.4639 | 0.88 | 16 | 1.5427 | |
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| 1.4824 | 0.99 | 18 | 1.5373 | |
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| 1.4752 | 1.1 | 20 | 1.5318 | |
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| 1.4768 | 1.21 | 22 | 1.5278 | |
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| 1.4482 | 1.32 | 24 | 1.5236 | |
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| 1.4444 | 1.42 | 26 | 1.5209 | |
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| 1.4381 | 1.53 | 28 | 1.5192 | |
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| 1.4415 | 1.64 | 30 | 1.5166 | |
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| 1.4412 | 1.75 | 32 | 1.5150 | |
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| 1.4263 | 1.86 | 34 | 1.5146 | |
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| 1.4608 | 1.97 | 36 | 1.5134 | |
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### Framework versions |
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- Transformers 4.34.1 |
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- Pytorch 2.0.1+cu118 |
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- Datasets 2.14.6 |
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- Tokenizers 0.14.1 |
