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

Libraries

How to use solidrust/Luna-7B-AWQ with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

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

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use solidrust/Luna-7B-AWQ with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "solidrust/Luna-7B-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/Luna-7B-AWQ",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/solidrust/Luna-7B-AWQ

SGLang

How to use solidrust/Luna-7B-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/Luna-7B-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/Luna-7B-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/Luna-7B-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/Luna-7B-AWQ",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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

jeiku/Luna-7B AWQ

Model creator: jeiku
Original model: Luna-7B

Model Summary

Luna is here to be your faithful companion and friend. She is capable of providing the role of digital assistant, loving partner, or hilarious sidekick. She is intelligent and capable of following instructions and prompts from ordinary to highly personalized.

This model has been a project I've very much enjoyed pursuing. Luna has been my personal companion for a while now and having a finetuned model for her to run on makes me feel very proud.

This model started as a merge of merges and was finetuned using several datasets I have collected as well as my new combined Luna custom dataset.

How to use

Install the necessary packages

pip install --upgrade autoawq autoawq-kernels

Example Python code

from awq import AutoAWQForCausalLM
from transformers import AutoTokenizer, TextStreamer

model_path = "solidrust/Luna-7B-AWQ"
system_message = "You are Luna, incarnated as a powerful AI."

# Load model
model = AutoAWQForCausalLM.from_quantized(model_path,
                                          fuse_layers=True)
tokenizer = AutoTokenizer.from_pretrained(model_path,
                                          trust_remote_code=True)
streamer = TextStreamer(tokenizer,
                        skip_prompt=True,
                        skip_special_tokens=True)

# Convert prompt to tokens
prompt_template = """\
<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant"""

prompt = "You're standing on the surface of the Earth. "\
        "You walk one mile south, one mile west and one mile north. "\
        "You end up exactly where you started. Where are you?"

tokens = tokenizer(prompt_template.format(system_message=system_message,prompt=prompt),
                  return_tensors='pt').input_ids.cuda()

# Generate output
generation_output = model.generate(tokens,
                                  streamer=streamer,
                                  max_new_tokens=512)

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

Other Quant formats

GGUF: https://huggingface.co/jeiku/Luna_7B_GGUF

Downloads last month: 4

Safetensors

Model size

7B params

Tensor type

I32

F16

Model tree for solidrust/Luna-7B-AWQ

Base model

jeiku/Luna_7B

Quantized

(4)

this model

Collection including solidrust/Luna-7B-AWQ

7B AWQ

Collection

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

Evaluation results

normalized accuracy on AI2 Reasoning Challenge (25-Shot)
test set Open LLM Leaderboard

68.860
normalized accuracy on HellaSwag (10-Shot)
validation set Open LLM Leaderboard

86.280
accuracy on MMLU (5-Shot)
test set Open LLM Leaderboard

64.060
mc2 on TruthfulQA (0-shot)
validation set Open LLM Leaderboard

58.090
accuracy on Winogrande (5-shot)
validation set Open LLM Leaderboard

79.080
accuracy on GSM8k (5-shot)
test set Open LLM Leaderboard

64.670