Instructions to use ISTA-DASLab/Llama-3.1-8B-Instruct-MR-GPTQ-nvfp with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ISTA-DASLab/Llama-3.1-8B-Instruct-MR-GPTQ-nvfp with Transformers:

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

pipe = pipeline("image-text-to-text", model="ISTA-DASLab/Llama-3.1-8B-Instruct-MR-GPTQ-nvfp")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("ISTA-DASLab/Llama-3.1-8B-Instruct-MR-GPTQ-nvfp", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use ISTA-DASLab/Llama-3.1-8B-Instruct-MR-GPTQ-nvfp with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ISTA-DASLab/Llama-3.1-8B-Instruct-MR-GPTQ-nvfp"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ISTA-DASLab/Llama-3.1-8B-Instruct-MR-GPTQ-nvfp",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/ISTA-DASLab/Llama-3.1-8B-Instruct-MR-GPTQ-nvfp

SGLang

How to use ISTA-DASLab/Llama-3.1-8B-Instruct-MR-GPTQ-nvfp 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 "ISTA-DASLab/Llama-3.1-8B-Instruct-MR-GPTQ-nvfp" \
    --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": "ISTA-DASLab/Llama-3.1-8B-Instruct-MR-GPTQ-nvfp",
		"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 "ISTA-DASLab/Llama-3.1-8B-Instruct-MR-GPTQ-nvfp" \
        --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": "ISTA-DASLab/Llama-3.1-8B-Instruct-MR-GPTQ-nvfp",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use ISTA-DASLab/Llama-3.1-8B-Instruct-MR-GPTQ-nvfp with Docker Model Runner:
```
docker model run hf.co/ISTA-DASLab/Llama-3.1-8B-Instruct-MR-GPTQ-nvfp
```

Llama-3.1-8B-Instruct-MR-GPTQ-nvfp

Model Overview

This model was obtained by quantizing the weights of Llama-3.1-8B-Instruct to NVFP4 data type. This optimization reduces the number of bits per parameter from 16 to 4.5, reducing the disk size and GPU memory requirements by approximately 72%.

Usage

MR-GPTQ quantized models with QuTLASS kernels are supported in the following integrations:

transformers with these features:
- Available in main (Documentation).
- RTN on-the-fly quantization.
- Pseudo-quantization QAT.
vLLM with these features:
- Available in this PR.
- Compatible with real quantization models from FP-Quant and the transformers integration.

Evaluation

This model was evaluated on a subset of OpenLLM v1 benchmarks and Platinum bench. Model outputs were generated with the vLLM engine.

OpenLLM v1 results

Model	MMLU‑CoT	GSM8k	Hellaswag	Winogrande	Average	Recovery (%)
`meta‑llama/Llama 3.1‑8B‑Instruct`	0.7276	0.8506	0.8001	0.7790	0.7893	–
`ISTA‑DASLab/Llama‑3.1‑8B‑Instruct‑MR‑GPTQ‑nvfp`	0.6917	0.8089	0.7850	0.7545	0.7600	96.29

Platinum bench results

Below we report recoveries on individual tasks as well as the average recovery.

Recovery by Task

Task	Recovery (%)
SingleOp	100.00
SingleQ	98.99
MultiArith	99.41
SVAMP	97.54
GSM8K	96.64
MMLU‑Math	92.43
BBH‑LogicalDeduction‑3Obj	87.34
BBH‑ObjectCounting	98.80
BBH‑Navigate	92.00
TabFact	86.92
HotpotQA	103.18
SQuAD	101.54
DROP	103.77
Winograd‑WSC	89.47
Average	96.29