Instructions to use ThingAI/Quark-135m-Bilingual with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ThingAI/Quark-135m-Bilingual with Transformers:

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

pipe = pipeline("text-generation", model="ThingAI/Quark-135m-Bilingual", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("ThingAI/Quark-135m-Bilingual", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use ThingAI/Quark-135m-Bilingual with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ThingAI/Quark-135m-Bilingual"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ThingAI/Quark-135m-Bilingual",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ThingAI/Quark-135m-Bilingual

SGLang

How to use ThingAI/Quark-135m-Bilingual 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 "ThingAI/Quark-135m-Bilingual" \
    --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": "ThingAI/Quark-135m-Bilingual",
		"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 "ThingAI/Quark-135m-Bilingual" \
        --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": "ThingAI/Quark-135m-Bilingual",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use ThingAI/Quark-135m-Bilingual with Docker Model Runner:
```
docker model run hf.co/ThingAI/Quark-135m-Bilingual
```

you may like to study this:

by usermma - opened 1 day ago

Discussion

usermma

1 day ago

new architecture :
https://huggingface.co/datasets/usermma/HMECA-Whitepaper

ThingsAI

ThingAI org 1 day ago

Thanks for sharing! Interesting concept but currently not implementable as described - runtime neural module generation is still too slow for real-time use.
Many HMECA ideas already exist in practice: MoE (routing + experts), multi-agent systems (domain modules), and chain-of-thought (task decomposition).
If you have working code, happy to benchmark against Quark!

usermma

1 day ago

Thanks for having interest for reading it, unfortunately i don't currently have any of ability of having any cloud or local computers to have it trained in real life, or maybe its just my own hallucinations...

but i may in nowadays start making the datasets that i would train it on, or the someone who have that interest, maybe some researcher or someone curious...

its the idea wheres every embedding works like a net only trained for just one specific tasks, a small or big net..., for example writing codes without any understanding about why some fishs tastes good when eating and some fishs are not, beacuse if the same net have understanded more than a thing, it may more causes hallucinations..

or maybe there is something else you didn't like, maybe i need to change the license into raw apache 2.0...

what are your suggestions?

so i get more people interested in it? or what?

i just doesn't want HMECA to be forgotten without any implementation on real life of it.... !

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