output = llm(
"Once upon a time,",
max_tokens=512,
echo=True
)
print(output)
QuantFactory/llama2_7b_chat_uncensored-GGUF
This is quantized version of georgesung/llama2_7b_chat_uncensored created using llama.cpp
Original Model Card
Overview
Fine-tuned Llama-2 7B with an uncensored/unfiltered Wizard-Vicuna conversation dataset (originally from ehartford/wizard_vicuna_70k_unfiltered). Used QLoRA for fine-tuning. Trained for one epoch on a 24GB GPU (NVIDIA A10G) instance, took ~19 hours to train.
The version here is the fp16 HuggingFace model.
GGML & GPTQ versions
Thanks to TheBloke, he has created the GGML and GPTQ versions:
- https://huggingface.co/TheBloke/llama2_7b_chat_uncensored-GGML
- https://huggingface.co/TheBloke/llama2_7b_chat_uncensored-GPTQ
Running in Ollama
https://ollama.com/library/llama2-uncensored
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.
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_7b_chat_uncensored.yaml
Fine-tuning guide
https://georgesung.github.io/ai/qlora-ift/
Open LLM Leaderboard Evaluation Results
Detailed results can be found here
| Metric | Value |
|---|---|
| Avg. | 43.39 |
| ARC (25-shot) | 53.58 |
| HellaSwag (10-shot) | 78.66 |
| MMLU (5-shot) | 44.49 |
| TruthfulQA (0-shot) | 41.34 |
| Winogrande (5-shot) | 74.11 |
| GSM8K (5-shot) | 5.84 |
| DROP (3-shot) | 5.69 |
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# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="QuantFactory/llama2_7b_chat_uncensored-GGUF", filename="", )