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

Libraries

How to use georgesung/llama2_7b_chat_uncensored with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("georgesung/llama2_7b_chat_uncensored")
model = AutoModelForMultimodalLM.from_pretrained("georgesung/llama2_7b_chat_uncensored")

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use georgesung/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 "georgesung/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": "georgesung/llama2_7b_chat_uncensored",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/georgesung/llama2_7b_chat_uncensored

SGLang

How to use georgesung/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 "georgesung/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": "georgesung/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 "georgesung/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": "georgesung/llama2_7b_chat_uncensored",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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

georgesung commited on Mar 7, 2024

Commit

bbdb2ff

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1 Parent(s): baf1d07

Update README.md

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@@ -1,11 +1,11 @@
 ---
 license: other
 datasets:
-- ehartford/wizard_vicuna_70k_unfiltered
 ---
 # Overview
-Fine-tuned [Llama-2 7B](https://huggingface.co/TheBloke/Llama-2-7B-fp16) with an uncensored/unfiltered Wizard-Vicuna conversation dataset [ehartford/wizard_vicuna_70k_unfiltered](https://huggingface.co/datasets/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.
@@ -58,4 +58,4 @@ Detailed results can be found [here](https://huggingface.co/datasets/open-llm-le
 | TruthfulQA (0-shot)   | 41.34   |
 | Winogrande (5-shot)   | 74.11   |
 | GSM8K (5-shot)        | 5.84        |
-| DROP (3-shot)         | 5.69         |

 ---
 license: other
 datasets:
+- georgesung/wizard_vicuna_70k_unfiltered
 ---
 # Overview
+Fine-tuned [Llama-2 7B](https://huggingface.co/TheBloke/Llama-2-7B-fp16) with an uncensored/unfiltered Wizard-Vicuna conversation dataset (originally from [ehartford/wizard_vicuna_70k_unfiltered](https://huggingface.co/datasets/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.
 | TruthfulQA (0-shot)   | 41.34   |
 | Winogrande (5-shot)   | 74.11   |
 | GSM8K (5-shot)        | 5.84        |
+| DROP (3-shot)         | 5.69         |