Instructions to use torchao-dev/opt-125m-float8dq-row-0.13-dev with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use torchao-dev/opt-125m-float8dq-row-0.13-dev with Transformers:

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

pipe = pipeline("text-generation", model="torchao-dev/opt-125m-float8dq-row-0.13-dev")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("torchao-dev/opt-125m-float8dq-row-0.13-dev")
model = AutoModelForCausalLM.from_pretrained("torchao-dev/opt-125m-float8dq-row-0.13-dev")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use torchao-dev/opt-125m-float8dq-row-0.13-dev with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "torchao-dev/opt-125m-float8dq-row-0.13-dev"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "torchao-dev/opt-125m-float8dq-row-0.13-dev",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/torchao-dev/opt-125m-float8dq-row-0.13-dev

SGLang

How to use torchao-dev/opt-125m-float8dq-row-0.13-dev 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 "torchao-dev/opt-125m-float8dq-row-0.13-dev" \
    --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": "torchao-dev/opt-125m-float8dq-row-0.13-dev",
		"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 "torchao-dev/opt-125m-float8dq-row-0.13-dev" \
        --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": "torchao-dev/opt-125m-float8dq-row-0.13-dev",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use torchao-dev/opt-125m-float8dq-row-0.13-dev with Docker Model Runner:
```
docker model run hf.co/torchao-dev/opt-125m-float8dq-row-0.13-dev
```

Update README.md

by jerryzh168 - opened Jul 14, 2025

base: refs/heads/main

←

from: refs/pr/1

Discussion Files changed

-1

Files changed (1) hide show

README.md +1 -1

README.md CHANGED Viewed

@@ -24,7 +24,7 @@ tokenizer = AutoTokenizer.from_pretrained(model_id)
 # Push to hub
 USER_ID = "torchao-testing"
 MODEL_NAME = model_id.split("/")[-1]
-save_to = f"{USER_ID}/{MODEL_NAME}-int4wo-preshuffled-0.13-dev"
 quantized_model.push_to_hub(save_to, safe_serialization=False)
 tokenizer.push_to_hub(save_to)

 # Push to hub
 USER_ID = "torchao-testing"
 MODEL_NAME = model_id.split("/")[-1]
+save_to = f"{USER_ID}/{MODEL_NAME}-float8dq-row-0.13-dev"
 quantized_model.push_to_hub(save_to, safe_serialization=False)
 tokenizer.push_to_hub(save_to)