Instructions to use solidrust/RainyMotip-2x7B-AWQ with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use solidrust/RainyMotip-2x7B-AWQ with Transformers:

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

pipe = pipeline("text-generation", model="solidrust/RainyMotip-2x7B-AWQ")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("solidrust/RainyMotip-2x7B-AWQ")
model = AutoModelForCausalLM.from_pretrained("solidrust/RainyMotip-2x7B-AWQ")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use solidrust/RainyMotip-2x7B-AWQ with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "solidrust/RainyMotip-2x7B-AWQ"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "solidrust/RainyMotip-2x7B-AWQ",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/solidrust/RainyMotip-2x7B-AWQ

SGLang

How to use solidrust/RainyMotip-2x7B-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/RainyMotip-2x7B-AWQ" \
    --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": "solidrust/RainyMotip-2x7B-AWQ",
		"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 "solidrust/RainyMotip-2x7B-AWQ" \
        --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": "solidrust/RainyMotip-2x7B-AWQ",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use solidrust/RainyMotip-2x7B-AWQ with Docker Model Runner:
```
docker model run hf.co/solidrust/RainyMotip-2x7B-AWQ
```

Alsebay/RainyMotip-2x7B AWQ

Model creator: Alsebay
Original model: RainyMotip-2x7B

Model Summary

What is it? A 2x7B MoE model for Roleplay(?).

You will occur GPT-like responses sometimes, just skip it and reroll (gacha time). Overall, I think it good enough for Roleplaying.

You may want see this: https://huggingface.co/Alsebay/My_LLMs_Leaderboard

This model is is a Mixure of Experts (MoE) made with the following models:

udkai/Turdus
Kquant03/Samlagast-7B-laser-bf16

If you used it, please let me know if it good or not. Thank you :)

Downloads last month: 4

Safetensors

Model size

13B params

Tensor type

I32

F16

Collection including solidrust/RainyMotip-2x7B-AWQ

2x7B AWQ

Collection

Mixture of experts 2 x 7B. • 20 items • Updated Apr 30, 2024