Instructions to use LoneStriker/xDAN-L1-Chat-RL-v1-5.0bpw-h6-exl2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use LoneStriker/xDAN-L1-Chat-RL-v1-5.0bpw-h6-exl2 with Transformers:

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

pipe = pipeline("text-generation", model="LoneStriker/xDAN-L1-Chat-RL-v1-5.0bpw-h6-exl2")

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("LoneStriker/xDAN-L1-Chat-RL-v1-5.0bpw-h6-exl2")
model = AutoModelForMultimodalLM.from_pretrained("LoneStriker/xDAN-L1-Chat-RL-v1-5.0bpw-h6-exl2")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use LoneStriker/xDAN-L1-Chat-RL-v1-5.0bpw-h6-exl2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "LoneStriker/xDAN-L1-Chat-RL-v1-5.0bpw-h6-exl2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "LoneStriker/xDAN-L1-Chat-RL-v1-5.0bpw-h6-exl2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/LoneStriker/xDAN-L1-Chat-RL-v1-5.0bpw-h6-exl2

SGLang

How to use LoneStriker/xDAN-L1-Chat-RL-v1-5.0bpw-h6-exl2 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 "LoneStriker/xDAN-L1-Chat-RL-v1-5.0bpw-h6-exl2" \
    --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": "LoneStriker/xDAN-L1-Chat-RL-v1-5.0bpw-h6-exl2",
		"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 "LoneStriker/xDAN-L1-Chat-RL-v1-5.0bpw-h6-exl2" \
        --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": "LoneStriker/xDAN-L1-Chat-RL-v1-5.0bpw-h6-exl2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use LoneStriker/xDAN-L1-Chat-RL-v1-5.0bpw-h6-exl2 with Docker Model Runner:
```
docker model run hf.co/LoneStriker/xDAN-L1-Chat-RL-v1-5.0bpw-h6-exl2
```

Top 1 Performer on MT-bench🏆

**The first top model which is performance at Humanalities, Coding and Writing with 7b. **

xDAN-AI • > Discord • Twitter • Huggingface

Dataset:

Selected from OpenOrca
Intel Orca-DPO-Pairs
Privately Crafted Dataset

########## First turn ##########

model	turn	score	size
gpt-4	1	8.95625	-
xDAN-L1-Chat-RL-v1	1	8.87500	7b
xDAN-L2-Chat-RL-v2	1	8.78750	30b
claude-v1	1	8.15000	-
gpt-3.5-turbo	1	8.07500	20b
vicuna-33b-v1.3	1	7.45625	33b
wizardlm-30b	1	7.13125	30b
oasst-sft-7-llama-30b	1	7.10625	30b
Llama-2-70b-chat	1	6.98750	70b

########## Second turn ##########

model	turn	score	size
gpt-4	2	9.025000	-
xDAN-L2-Chat-RL-v2	2	8.087500	30b
xDAN-L1-Chat-RL-v1	2	7.825000	7b
gpt-3.5-turbo	2	7.812500	20b
claude-v1	2	7.650000	-
wizardlm-30b	2	6.887500	30b
vicuna-33b-v1.3	2	6.787500	33b
Llama-2-70b-chat	2	6.725000	70b

########## Average turn##########

model	score	size
gpt-4	8.990625	-
xDAN-L2-Chat-RL-v2	8.437500	30b
xDAN-L1-Chat-RL-v1	8.350000	7b
gpt-3.5-turbo	7.943750	20b
claude-v1	7.900000	-
vicuna-33b-v1.3	7.121875	33b
wizardlm-30b	7.009375	30b
Llama-2-70b-chat	6.856250	70b

Prompt Template(Alpaca)

You are a helpful assistant named DAN. You are an expert in worldly knowledge, skilled in employing a probing questioning strategy, and you carefully consider each step before providing answers. \n\n### Instruction:\n{instruction}\n\n### Response:

Created By xDAN-AI at 2023-12-15

Eval by FastChat: https://github.com/lm-sys/FastChat.git

Disclaimer We employ data compliance checking algorithms during the training of our language model to strive for the highest degree of compliance. However, given the intricate nature of data and the vast array of potential usage scenarios for the model, we cannot assure that it will always generate correct and reasonable outputs. Users should be cognizant of the risk of the model producing problematic outputs. Our organization will not bear responsibility for any risks or issues stemming from misuse, misguidance, illegal use, and related misinformation, as well as any consequent data security concerns.

About xDAN-AI xDAN-AI is a top lead high-performance model factory. For detailed information and further insights into our cutting-edge technology and offerings, please visit our website: https://www.xdan.ai.

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xDAN-L1-Chat-RL-v1-5.0bpw-h6-exl2

Dataset:

Prompt Template(Alpaca)

Created By xDAN-AI at 2023-12-15

Eval by FastChat: https://github.com/lm-sys/FastChat.git

Datasets used to train LoneStriker/xDAN-L1-Chat-RL-v1-5.0bpw-h6-exl2