Instructions to use tarekziade/test-quantize with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use tarekziade/test-quantize with Transformers:

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

pipe = pipeline("image-text-to-text", model="tarekziade/test-quantize")

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("tarekziade/test-quantize")
model = AutoModelForMultimodalLM.from_pretrained("tarekziade/test-quantize")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use tarekziade/test-quantize with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/tarekziade/test-quantize

SGLang

How to use tarekziade/test-quantize 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 "tarekziade/test-quantize" \
    --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": "tarekziade/test-quantize",
		"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 "tarekziade/test-quantize" \
        --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": "tarekziade/test-quantize",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use tarekziade/test-quantize with Docker Model Runner:
```
docker model run hf.co/tarekziade/test-quantize
```

test-quantize

File size: 3,110 Bytes

dbcdc44

{
    "per_channel": true,
    "reduce_range": true,
    "per_model_config": {
        "decoder_with_past_model": {
            "op_types": [
                "MatMul",
                "Gemm",
                "Where",
                "ConstantOfShape",
                "Split",
                "Slice",
                "Pow",
                "Shape",
                "Unsqueeze",
                "Squeeze",
                "ReduceMean",
                "Reshape",
                "Mul",
                "Add",
                "Concat",
                "Sub",
                "Transpose",
                "Constant",
                "Div",
                "Gather",
                "Cast",
                "Tanh",
                "Sqrt",
                "Softmax",
                "Range"
            ],
            "weight_type": "QInt8"
        },
        "decoder_model": {
            "op_types": [
                "MatMul",
                "Gemm",
                "Where",
                "ConstantOfShape",
                "Split",
                "Slice",
                "Pow",
                "Shape",
                "Unsqueeze",
                "Squeeze",
                "ReduceMean",
                "Reshape",
                "Mul",
                "Add",
                "Concat",
                "Sub",
                "Transpose",
                "Constant",
                "Div",
                "Gather",
                "Cast",
                "Tanh",
                "Sqrt",
                "Softmax",
                "Range"
            ],
            "weight_type": "QInt8"
        },
        "encoder_model": {
            "op_types": [
                "MatMul",
                "Where",
                "ConstantOfShape",
                "Expand",
                "Slice",
                "Pow",
                "Shape",
                "Unsqueeze",
                "ReduceMean",
                "Reshape",
                "Equal",
                "Mul",
                "Add",
                "Conv",
                "Concat",
                "Sub",
                "Transpose",
                "Constant",
                "Gather",
                "Div",
                "Sqrt",
                "Softmax",
                "Erf"
            ],
            "weight_type": "QUInt8"
        },
        "decoder_model_merged": {
            "op_types": [
                "MatMul",
                "Gemm",
                "Where",
                "ConstantOfShape",
                "Split",
                "Slice",
                "Pow",
                "Shape",
                "Unsqueeze",
                "Squeeze",
                "ReduceMean",
                "Reshape",
                "Mul",
                "Add",
                "Concat",
                "Sub",
                "Transpose",
                "Constant",
                "Div",
                "Gather",
                "Cast",
                "Tanh",
                "Sqrt",
                "Softmax",
                "Range",
                "If"
            ],
            "weight_type": "QInt8"
        }
    }
}