---
license: openrail
pipeline_tag: text-generation
library_name: transformers
language:
- en
---


## Original model card 

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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>

#### Description 

GGML Format model files for [This project](https://huggingface.co/arogov/llama2_13b_chat_uncensored).


### inference 


```python

import ctransformers

from ctransformers import AutoModelForCausalLM

model = AutoModelForCausalLM.from_pretrained(output_dir, ggml_file,
gpu_layers=32, model_type="llama")

manual_input: str = "Tell me about your last dream, please."


llm(manual_input, 
      max_new_tokens=256, 
      temperature=0.9, 
      top_p= 0.7)

```



# Original model card



# Overview
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).
Used QLoRA for fine-tuning. Trained for one epoch on a two 24GB GPU (NVIDIA RTX 3090) instance, took ~26.5 hours to train.
```
{'train_runtime': 95229.7197, 'train_samples_per_second': 0.363, 'train_steps_per_second': 0.091, 'train_loss': 0.5828390517308127, 'epoch': 1.0}
100%|██████████████████████████████████████████████████████████████████████████████████████████████| 8649/8649 [26:27:09<00:00, 11.01s/it]
Training complete, adapter model saved in models//llama2_13b_chat_uncensored_adapter
```

The version here is the fp16 HuggingFace model.

## GGML & GPTQ versions
Thanks to [TheBloke](https://huggingface.co/TheBloke), he has created the GGML and GPTQ versions:
* https://huggingface.co/TheBloke/Llama-2-13B-GGML
* https://huggingface.co/TheBloke/Llama-2-13B-GPTQ

# Prompt style
The model was trained with the following prompt style:
```
### HUMAN:
Hello

### RESPONSE:
Hi, how are you?

### HUMAN:
I'm fine.

### RESPONSE:
How can I help you?
...
```

# Training code
Code used to train the model is available [here](https://github.com/georgesung/llm_qlora).

To reproduce the results:
```
git clone https://github.com/georgesung/llm_qlora
cd llm_qlora
pip install -r requirements.txt
python train.py configs/llama2_13b_chat_uncensored.yaml
```

# Fine-tuning guide
https://georgesung.github.io/ai/qlora-ift/