Instructions to use arogov/llama2_13b_chat_uncensored with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use arogov/llama2_13b_chat_uncensored with Transformers:

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

pipe = pipeline("text-generation", model="arogov/llama2_13b_chat_uncensored")

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("arogov/llama2_13b_chat_uncensored")
model = AutoModelForMultimodalLM.from_pretrained("arogov/llama2_13b_chat_uncensored")

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use arogov/llama2_13b_chat_uncensored with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "arogov/llama2_13b_chat_uncensored"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "arogov/llama2_13b_chat_uncensored",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/arogov/llama2_13b_chat_uncensored

SGLang

How to use arogov/llama2_13b_chat_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 "arogov/llama2_13b_chat_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": "arogov/llama2_13b_chat_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 "arogov/llama2_13b_chat_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": "arogov/llama2_13b_chat_uncensored",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use arogov/llama2_13b_chat_uncensored with Docker Model Runner:
```
docker model run hf.co/arogov/llama2_13b_chat_uncensored
```

Overview

Fine-tuned Llama-2 13B with an uncensored/unfiltered Wizard-Vicuna conversation dataset 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, he has created the GGML and GPTQ versions:

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_13b_chat_uncensored.yaml

Fine-tuning guide

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

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arogov
/

llama2_13b_chat_uncensored

Overview

GGML & GPTQ versions

Prompt style

Training code

Fine-tuning guide

Dataset used to train arogov/llama2_13b_chat_uncensored