Instructions to use ISTA-DASLab/Llama-3.1-70B-Instruct-2Bit-GSQ with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ISTA-DASLab/Llama-3.1-70B-Instruct-2Bit-GSQ with Transformers:

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

pipe = pipeline("text-generation", model="ISTA-DASLab/Llama-3.1-70B-Instruct-2Bit-GSQ")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ISTA-DASLab/Llama-3.1-70B-Instruct-2Bit-GSQ")
model = AutoModelForCausalLM.from_pretrained("ISTA-DASLab/Llama-3.1-70B-Instruct-2Bit-GSQ")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use ISTA-DASLab/Llama-3.1-70B-Instruct-2Bit-GSQ with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ISTA-DASLab/Llama-3.1-70B-Instruct-2Bit-GSQ"
# 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-3.1-70B-Instruct-2Bit-GSQ",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/ISTA-DASLab/Llama-3.1-70B-Instruct-2Bit-GSQ

SGLang

How to use ISTA-DASLab/Llama-3.1-70B-Instruct-2Bit-GSQ 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-3.1-70B-Instruct-2Bit-GSQ" \
    --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-3.1-70B-Instruct-2Bit-GSQ",
		"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-3.1-70B-Instruct-2Bit-GSQ" \
        --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-3.1-70B-Instruct-2Bit-GSQ",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use ISTA-DASLab/Llama-3.1-70B-Instruct-2Bit-GSQ with Docker Model Runner:
```
docker model run hf.co/ISTA-DASLab/Llama-3.1-70B-Instruct-2Bit-GSQ
```

soroushtabesh commited on 15 days ago

Commit

89fa3b5

verified ·

1 Parent(s): 5893d4a

Add storage layout details

Browse files

Files changed (1) hide show

README.md +2 -2

README.md CHANGED Viewed

@@ -46,7 +46,7 @@ standard zero-shot suite (ARC-C/E, HellaSwag, PIQA, Winogrande):
 - **Bits / weight (effective):** ≈2.13 bpp
 - **Codebook:** 2-bit symmetric scalar `{-2, -1, 0, +1} × scale`
 - **Group size:** 128
-- **Format:** [Humming](https://github.com/IST-DASLab/humming) (`quant_method: "humming"`, `b_dtype: "uint2"`)
 - **Pipeline:** GPTQ initialization → Gumbel-Softmax refinement (Lion optimizer)
 ### Storage layout (why the HF UI shows I32 + BF16)
@@ -84,7 +84,7 @@ weight is `2 bits (packed) + 16 bits / 128 (group scale) ≈ 2.13 bpp`. The
 ```
 Loading this checkpoint requires a vLLM build with the
-[`humming`](https://github.com/IST-DASLab/humming) MoE kernel installed (see
 the [GSQ repo](https://github.com/IST-DASLab/GSQ) `scripts/setup_env.sh` for
 the exact install line).

 - **Bits / weight (effective):** ≈2.13 bpp
 - **Codebook:** 2-bit symmetric scalar `{-2, -1, 0, +1} × scale`
 - **Group size:** 128
+- **Format:** [Humming](https://github.com/inclusionAI/humming) (`quant_method: "humming"`, `b_dtype: "uint2"`)
 - **Pipeline:** GPTQ initialization → Gumbel-Softmax refinement (Lion optimizer)
 ### Storage layout (why the HF UI shows I32 + BF16)
 ```
 Loading this checkpoint requires a vLLM build with the
+[`humming`](https://github.com/inclusionAI/humming) MoE kernel installed (see
 the [GSQ repo](https://github.com/IST-DASLab/GSQ) `scripts/setup_env.sh` for
 the exact install line).