from transformers import AutoModelForImageTextToText, AutoTokenizer

model = AutoModelForImageTextToText.from_pretrained(
    "jkim96/Muse-Glimmer-30B-DASHQ-INT3-g128", trust_remote_code=True, device_map="cuda", dtype="auto"
)
tokenizer = AutoTokenizer.from_pretrained("jkim96/Muse-Glimmer-30B-DASHQ-INT3-g128")

messages = [{"role": "user", "content": "Explain 2-bit quantization in one sentence."}]
text = tokenizer.apply_chat_template(messages, add_generation_prompt=True, tokenize=False)
inputs = tokenizer(text, return_tensors="pt").to(model.device)
print(tokenizer.decode(model.generate(**inputs, max_new_tokens=256)[0]))

trust_remote_code=True is required: the checkpoint ships its quantized-layer implementation (modeling_dashq.py) and Triton kernels (dashq_kernel.py). Without Triton, or on CPU, it falls back to dequantize-and-matmul in PyTorch.

Requirements

Package	Minimum	Verified with
`torch`	2.4	2.12.1+cu130
`transformers`	5.8	5.9.0
`triton`	3.0 (Linux; bundled with CUDA builds of PyTorch)	3.7.1
`huggingface_hub`	1.5 (pulled in by transformers)	1.15.0

Quantization

Field	Value
Base model	`meta-models/Muse-Glimmer-30B`
Precision	INT3, group size 128
Scale / zero dtype	float16
Calibration	wikitext2, 128 samples x 2048
Size	20.0779 GB · original 59.5534 GB · 3.0x compression

Benchmarks

Full zero-shot / few-shot results for every DASH-Q checkpoint: github.com/JaeminK/dashq#benchmarks

Evaluation

Metric	Value
`wikitext2_ppl`	6.5747
`zero-shot accuracy avg`	69.8220
`arc_challenge`	56.9113
`arc_easy`	81.8182
`commonsense_qa`	74.3653
`hellaswag`	80.9201
`lambada_openai`	72.9478
`openbookqa`	44.6000
`piqa`	82.3177
`truthfulqa_mc2`	58.9848
`winogrande`	75.5328