Instructions to use georgesung/open_llama_7b_qlora_uncensored with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use georgesung/open_llama_7b_qlora_uncensored with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="georgesung/open_llama_7b_qlora_uncensored")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("georgesung/open_llama_7b_qlora_uncensored")
model = AutoModelForCausalLM.from_pretrained("georgesung/open_llama_7b_qlora_uncensored")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use georgesung/open_llama_7b_qlora_uncensored with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "georgesung/open_llama_7b_qlora_uncensored"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "georgesung/open_llama_7b_qlora_uncensored",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/georgesung/open_llama_7b_qlora_uncensored

SGLang

How to use georgesung/open_llama_7b_qlora_uncensored with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "georgesung/open_llama_7b_qlora_uncensored" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "georgesung/open_llama_7b_qlora_uncensored",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "georgesung/open_llama_7b_qlora_uncensored" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "georgesung/open_llama_7b_qlora_uncensored",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use georgesung/open_llama_7b_qlora_uncensored with Docker Model Runner:
```
docker model run hf.co/georgesung/open_llama_7b_qlora_uncensored
```

Overview

Fine-tuned OpenLLaMA-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 ~18 hours to train.

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.

Demo

For a Gradio chat application using this model, clone this HuggingFace Space and run it on top of a GPU instance. The basic T4 GPU instance will work.

Blog post

Since this was my first time fine-tuning an LLM, I also wrote an accompanying blog post about how I performed the training :)

https://georgesung.github.io/ai/qlora-ift/

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Safetensors

Model size

7B params

Tensor type

F32

georgesung
/

open_llama_7b_qlora_uncensored

Overview

Prompt style

Training code

Demo

Blog post

Dataset used to train georgesung/open_llama_7b_qlora_uncensored

Spaces using georgesung/open_llama_7b_qlora_uncensored 2