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--- |
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license: other |
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datasets: |
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- ehartford/wizard_vicuna_70k_unfiltered |
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--- |
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# Overview |
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Fine-tuned [Llama-2 13B](https://huggingface.co/TheBloke/Llama-2-13B-fp16) with an uncensored/unfiltered Wizard-Vicuna conversation dataset [ehartford/wizard_vicuna_70k_unfiltered](https://huggingface.co/datasets/ehartford/wizard_vicuna_70k_unfiltered). |
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Used QLoRA for fine-tuning. Trained for one epoch on a two 24GB GPU (NVIDIA RTX 3090) instance, took ~26.5 hours to train. |
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``` |
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{'train_runtime': 95229.7197, 'train_samples_per_second': 0.363, 'train_steps_per_second': 0.091, 'train_loss': 0.5828390517308127, 'epoch': 1.0} |
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100%|██████████████████████████████████████████████████████████████████████████████████████████████| 8649/8649 [26:27:09<00:00, 11.01s/it] |
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Training complete, adapter model saved in models//llama2_13b_chat_uncensored_adapter |
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``` |
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The version here is the fp16 HuggingFace model. |
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## GGML & GPTQ versions |
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Thanks to [TheBloke](https://huggingface.co/TheBloke), he has created the GGML and GPTQ versions: |
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* https://huggingface.co/TheBloke/Llama-2-13B-GGML |
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* https://huggingface.co/TheBloke/Llama-2-13B-GPTQ |
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# Prompt style |
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The model was trained with the following prompt style: |
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``` |
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### HUMAN: |
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Hello |
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### RESPONSE: |
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Hi, how are you? |
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### HUMAN: |
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I'm fine. |
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### RESPONSE: |
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How can I help you? |
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... |
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``` |
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# Training code |
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Code used to train the model is available [here](https://github.com/georgesung/llm_qlora). |
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To reproduce the results: |
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``` |
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git clone https://github.com/georgesung/llm_qlora |
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cd llm_qlora |
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pip install -r requirements.txt |
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python train.py configs/llama2_13b_chat_uncensored.yaml |
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``` |
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# Fine-tuning guide |
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https://georgesung.github.io/ai/qlora-ift/ |
