Instructions to use jkim96/Llama-3.1-405B-Instruct-DASHQ-INT2-g32 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use jkim96/Llama-3.1-405B-Instruct-DASHQ-INT2-g32 with Transformers:

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

pipe = pipeline("text-generation", model="jkim96/Llama-3.1-405B-Instruct-DASHQ-INT2-g32")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("jkim96/Llama-3.1-405B-Instruct-DASHQ-INT2-g32")
model = AutoModelForCausalLM.from_pretrained("jkim96/Llama-3.1-405B-Instruct-DASHQ-INT2-g32")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use jkim96/Llama-3.1-405B-Instruct-DASHQ-INT2-g32 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "jkim96/Llama-3.1-405B-Instruct-DASHQ-INT2-g32"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "jkim96/Llama-3.1-405B-Instruct-DASHQ-INT2-g32",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/jkim96/Llama-3.1-405B-Instruct-DASHQ-INT2-g32

SGLang

How to use jkim96/Llama-3.1-405B-Instruct-DASHQ-INT2-g32 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 "jkim96/Llama-3.1-405B-Instruct-DASHQ-INT2-g32" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "jkim96/Llama-3.1-405B-Instruct-DASHQ-INT2-g32",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "jkim96/Llama-3.1-405B-Instruct-DASHQ-INT2-g32" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "jkim96/Llama-3.1-405B-Instruct-DASHQ-INT2-g32",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use jkim96/Llama-3.1-405B-Instruct-DASHQ-INT2-g32 with Docker Model Runner:
```
docker model run hf.co/jkim96/Llama-3.1-405B-Instruct-DASHQ-INT2-g32
```

Llama-3.1-405B-Instruct-DASHQ-INT2-g32

DASH-Q — Diagonal-Aware Shrinkage for Robust PTQ. INT2 · group size 32 · 159.03 GB (from 795.4 GB — 5.0x smaller)

DASH-Q checkpoints load with the lightweight DASH-Q runtime — linear layers are packed PackedQuantizedLinear modules, not plain Transformers weights.

Install

pip install git+https://github.com/JaeminK/dashq.git

Load

from dashq import load_quantized

model, tokenizer = load_quantized("jkim96/Llama-3.1-405B-Instruct-DASHQ-INT2-g32", device_map="auto")

Quantization

Field	Value
Base model	`meta-llama/Llama-3.1-405B-Instruct`
Precision	INT2, group size 32
Scale / zero dtype	float16
Calibration	wikitext2, 128 samples x 2048
Size	159.03 GB · original 795.4 GB · 5.0x compression