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

Libraries

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

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

pipe = pipeline("text-generation", model="solidrust/Nutopia-7B-AWQ")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("solidrust/Nutopia-7B-AWQ")
model = AutoModelForCausalLM.from_pretrained("solidrust/Nutopia-7B-AWQ", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

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

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "solidrust/Nutopia-7B-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/Nutopia-7B-AWQ",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

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

SGLang

How to use solidrust/Nutopia-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/Nutopia-7B-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/Nutopia-7B-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/Nutopia-7B-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/Nutopia-7B-AWQ",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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

Nutopia-7B-AWQ / README.md

Suparious

Updated and moved existing to merged_models base_model tag in README.md

922470d verified almost 2 years ago

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	---
	base_model: Alsebay/Nutopia-7B
	inference: false
	library_name: transformers
	merged_models:
	- NurtureAI/neural-chat-7b-v3-1-16k
	- NousResearch/Hermes-2-Pro-Mistral-7B
	pipeline_tag: text-generation
	quantized_by: Suparious
	tags:
	- mergekit
	- merge
	- 4-bit
	- AWQ
	- text-generation
	- autotrain_compatible
	- endpoints_compatible
	---
	# Alsebay/Nutopia-7B AWQ

	- Model creator: [Alsebay](https://huggingface.co/Alsebay)
	- Original model: [Nutopia-7B](https://huggingface.co/Alsebay/Nutopia-7B)

	## Model Summary

	Testing purpose only, seem it not good in Roleplaying 😢

	This model was merged using the SLERP merge method.

	The following models were included in the merge:
	* [NurtureAI/neural-chat-7b-v3-1-16k](https://huggingface.co/NurtureAI/neural-chat-7b-v3-1-16k)
	* [NousResearch/Hermes-2-Pro-Mistral-7B](https://huggingface.co/NousResearch/Hermes-2-Pro-Mistral-7B)