Instructions to use digitalpipelines/llama2_7b_chat_uncensored with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use digitalpipelines/llama2_7b_chat_uncensored with Transformers:

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

pipe = pipeline("text-generation", model="digitalpipelines/llama2_7b_chat_uncensored")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("digitalpipelines/llama2_7b_chat_uncensored")
model = AutoModelForCausalLM.from_pretrained("digitalpipelines/llama2_7b_chat_uncensored")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use digitalpipelines/llama2_7b_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_7b_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_7b_chat_uncensored",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/digitalpipelines/llama2_7b_chat_uncensored

SGLang

How to use digitalpipelines/llama2_7b_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_7b_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_7b_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_7b_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_7b_chat_uncensored",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use digitalpipelines/llama2_7b_chat_uncensored with Docker Model Runner:
```
docker model run hf.co/digitalpipelines/llama2_7b_chat_uncensored
```

Overview

Fine-tuned OpenLLaMA-7B with an uncensored/unfiltered Wizard-Vicuna conversation dataset digitalpipelines/wizard_vicuna_70k_uncensored. Used QLoRA for fine-tuning using the process outlined in https://georgesung.github.io/ai/qlora-ift/

GPTQ quantized model can be found at digitalpipelines/llama2_7b_chat_uncensored-GPTQ
GGML 2, 3, 4, 5, 6 and 8-bit quanitized models for CPU+GPU inference of digitalpipelines/llama2_7b_chat_uncensored-GGML

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?
...

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digitalpipelines
/

llama2_7b_chat_uncensored

Overview

Prompt style

Dataset used to train digitalpipelines/llama2_7b_chat_uncensored