arithmetic-circuit-overloading
/

Qwen3-32B-3d-500K-50K-0.1-reverse-padzero-plus-mul-sub-99-128D-3L-8H-512I

Instructions to use arithmetic-circuit-overloading/Qwen3-32B-3d-500K-50K-0.1-reverse-padzero-plus-mul-sub-99-128D-3L-8H-512I with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use arithmetic-circuit-overloading/Qwen3-32B-3d-500K-50K-0.1-reverse-padzero-plus-mul-sub-99-128D-3L-8H-512I with Transformers:

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

pipe = pipeline("text-generation", model="arithmetic-circuit-overloading/Qwen3-32B-3d-500K-50K-0.1-reverse-padzero-plus-mul-sub-99-128D-3L-8H-512I")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("arithmetic-circuit-overloading/Qwen3-32B-3d-500K-50K-0.1-reverse-padzero-plus-mul-sub-99-128D-3L-8H-512I")
model = AutoModelForCausalLM.from_pretrained("arithmetic-circuit-overloading/Qwen3-32B-3d-500K-50K-0.1-reverse-padzero-plus-mul-sub-99-128D-3L-8H-512I", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use arithmetic-circuit-overloading/Qwen3-32B-3d-500K-50K-0.1-reverse-padzero-plus-mul-sub-99-128D-3L-8H-512I with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "arithmetic-circuit-overloading/Qwen3-32B-3d-500K-50K-0.1-reverse-padzero-plus-mul-sub-99-128D-3L-8H-512I"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "arithmetic-circuit-overloading/Qwen3-32B-3d-500K-50K-0.1-reverse-padzero-plus-mul-sub-99-128D-3L-8H-512I",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/arithmetic-circuit-overloading/Qwen3-32B-3d-500K-50K-0.1-reverse-padzero-plus-mul-sub-99-128D-3L-8H-512I

SGLang

How to use arithmetic-circuit-overloading/Qwen3-32B-3d-500K-50K-0.1-reverse-padzero-plus-mul-sub-99-128D-3L-8H-512I 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 "arithmetic-circuit-overloading/Qwen3-32B-3d-500K-50K-0.1-reverse-padzero-plus-mul-sub-99-128D-3L-8H-512I" \
    --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": "arithmetic-circuit-overloading/Qwen3-32B-3d-500K-50K-0.1-reverse-padzero-plus-mul-sub-99-128D-3L-8H-512I",
		"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 "arithmetic-circuit-overloading/Qwen3-32B-3d-500K-50K-0.1-reverse-padzero-plus-mul-sub-99-128D-3L-8H-512I" \
        --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": "arithmetic-circuit-overloading/Qwen3-32B-3d-500K-50K-0.1-reverse-padzero-plus-mul-sub-99-128D-3L-8H-512I",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use arithmetic-circuit-overloading/Qwen3-32B-3d-500K-50K-0.1-reverse-padzero-plus-mul-sub-99-128D-3L-8H-512I with Docker Model Runner:
```
docker model run hf.co/arithmetic-circuit-overloading/Qwen3-32B-3d-500K-50K-0.1-reverse-padzero-plus-mul-sub-99-128D-3L-8H-512I
```

Qwen3-32B-3d-500K-50K-0.1-reverse-padzero-plus-mul-sub-99-128D-3L-8H-512I / training_args.bin

allen-ajith

Training in progress, step 500

8382c4c verified 5 months ago

Download

History Blame Contribute Delete

6.16 kB

This file is stored with Xet . It is too big to display, but you can still download it.

Xet Pointer Details

( Raw pointer file )

Xet hash:: ce10e90db64cbfd76212802e8c6a240dde90998e3f138d61aa09ae18f7722836
Size of remote file:: 6.16 kB
SHA256:: 7f6f16a2225ed266eb927fdec7723e6888f1b71b195f31f2e107a2e30dd4c098

Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.