Instructions to use solidrust/Noromaid-7B-0.4-DPO-AWQ with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use solidrust/Noromaid-7B-0.4-DPO-AWQ with Transformers:

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

pipe = pipeline("text-generation", model="solidrust/Noromaid-7B-0.4-DPO-AWQ")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("solidrust/Noromaid-7B-0.4-DPO-AWQ")
model = AutoModelForCausalLM.from_pretrained("solidrust/Noromaid-7B-0.4-DPO-AWQ")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use solidrust/Noromaid-7B-0.4-DPO-AWQ with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "solidrust/Noromaid-7B-0.4-DPO-AWQ"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "solidrust/Noromaid-7B-0.4-DPO-AWQ",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/solidrust/Noromaid-7B-0.4-DPO-AWQ

SGLang

How to use solidrust/Noromaid-7B-0.4-DPO-AWQ 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 "solidrust/Noromaid-7B-0.4-DPO-AWQ" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "solidrust/Noromaid-7B-0.4-DPO-AWQ",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "solidrust/Noromaid-7B-0.4-DPO-AWQ" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "solidrust/Noromaid-7B-0.4-DPO-AWQ",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use solidrust/Noromaid-7B-0.4-DPO-AWQ with Docker Model Runner:
```
docker model run hf.co/solidrust/Noromaid-7B-0.4-DPO-AWQ
```

Noromaid 7B v0.4 DPO - AWQ

Model creator: IkariDev and Undi
Original model: Noromaid 7B v0.4 DPO

Model description

This repo contains AWQ model files for IkariDev and Undi's Noromaid 7B v0.4 DPO.

These files were quantised using hardware kindly provided by SolidRusT Networks.

About AWQ

AWQ is an efficient, accurate and blazing-fast low-bit weight quantization method, currently supporting 4-bit quantization. Compared to GPTQ, it offers faster Transformers-based inference with equivalent or better quality compared to the most commonly used GPTQ settings.

AWQ models are currently supported on Linux and Windows, with NVidia GPUs only. macOS users: please use GGUF models instead.

It is supported by:

Text Generation Webui - using Loader: AutoAWQ
vLLM - version 0.2.2 or later for support for all model types.
Hugging Face Text Generation Inference (TGI)
Transformers version 4.35.0 and later, from any code or client that supports Transformers
AutoAWQ - for use from Python code

Prompt template: ChatML

<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant

Training data

no_robots dataset let the model have more human behavior, enhances the output.
[Aesir Private RP dataset] New data from a new and never used before dataset, add fresh data, no LimaRP spam, this is 100% new. Thanks to the MinvervaAI Team and, in particular, Gryphe for letting us use it!
[Another private Aesir dataset]
[Another private Aesir dataset]
limarp

DPO training data

Downloads last month: 12

Safetensors

Model size

7B params

Tensor type

I32

F16

Model tree for solidrust/Noromaid-7B-0.4-DPO-AWQ

Base model

NeverSleep/Noromaid-7B-0.4-DPO

Quantized

(4)

this model

Datasets used to train solidrust/Noromaid-7B-0.4-DPO-AWQ

Collection including solidrust/Noromaid-7B-0.4-DPO-AWQ

7B AWQ

Collection

These models are selected for their compatibility with small 12GB memory GPUs. • 203 items • Updated Mar 2 • 2