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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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language: |
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- en |
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tags: |
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- text-generation-inference |
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pipeline_tag: text-generation |
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--- |
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Buy me a coffee if you like this project ;) |
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<a href="https://www.buymeacoffee.com/s3nh"><img src="https://www.buymeacoffee.com/assets/img/guidelines/download-assets-sm-1.svg" alt=""></a> |
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#### Description |
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GGML Format model files for [This project](https://huggingface.co/georgesung/llama2_7b_chat_uncensored). |
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### inference |
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```python |
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import ctransformers |
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from ctransformers import AutoModelForCausalLM |
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model = AutoModelForCausalLM.from_pretrained(output_dir, ggml_file, |
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gpu_layers=32, model_type="llama") |
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manual_input: str = "Tell me about your last dream, please." |
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llm(manual_input, |
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max_new_tokens=256, |
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temperature=0.9, |
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top_p= 0.7) |
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``` |
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#### Original Model card |
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# Overview |
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Fine-tuned [Llama-2 7B](https://huggingface.co/TheBloke/Llama-2-7B-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 24GB GPU (NVIDIA A10G) instance, took ~19 hours to train. |
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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_7b_chat_uncensored.yaml |
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``` |
