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

Libraries

How to use digitalpipelines/llama2_13b_chat_uncensored with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("digitalpipelines/llama2_13b_chat_uncensored")
model = AutoModelForCausalLM.from_pretrained("digitalpipelines/llama2_13b_chat_uncensored")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

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

Use Docker

docker model run hf.co/digitalpipelines/llama2_13b_chat_uncensored

SGLang

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

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

Overview

Fine-tuned Llama-2 13B trained with an uncensored/unfiltered Wizard-Vicuna conversation dataset digitalpipelines/wizard_vicuna_70k_uncensored. A QLoRA was created and used for fine-tuning and then merged back into the model. Llama2 has inherited bias even though it's been finetuned on an uncensored dataset.

Available versions of this model

Prompt template: Llama-2-Chat

SYSTEM: You are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe.  Your answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure that your responses are socially unbiased and positive in nature. If a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. If you don't know the answer to a question, please don't share false information.
USER: {prompt}
ASSISTANT:

Downloads last month: 11

digitalpipelines
/

llama2_13b_chat_uncensored

Overview

Available versions of this model

Prompt template: Llama-2-Chat

Dataset used to train digitalpipelines/llama2_13b_chat_uncensored