Instructions to use ISTA-DASLab/Llama-3.1-70B-Instruct-3Bit-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-3Bit-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-3Bit-GSQ")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ISTA-DASLab/Llama-3.1-70B-Instruct-3Bit-GSQ")
model = AutoModelForCausalLM.from_pretrained("ISTA-DASLab/Llama-3.1-70B-Instruct-3Bit-GSQ")
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

vLLM

How to use ISTA-DASLab/Llama-3.1-70B-Instruct-3Bit-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-3Bit-GSQ"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ISTA-DASLab/Llama-3.1-70B-Instruct-3Bit-GSQ",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ISTA-DASLab/Llama-3.1-70B-Instruct-3Bit-GSQ

SGLang

How to use ISTA-DASLab/Llama-3.1-70B-Instruct-3Bit-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-3Bit-GSQ" \
    --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": "ISTA-DASLab/Llama-3.1-70B-Instruct-3Bit-GSQ",
		"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 "ISTA-DASLab/Llama-3.1-70B-Instruct-3Bit-GSQ" \
        --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": "ISTA-DASLab/Llama-3.1-70B-Instruct-3Bit-GSQ",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use ISTA-DASLab/Llama-3.1-70B-Instruct-3Bit-GSQ with Docker Model Runner:
```
docker model run hf.co/ISTA-DASLab/Llama-3.1-70B-Instruct-3Bit-GSQ
```

soroushtabesh commited on 1 day ago

Commit

b458ae6

verified ·

1 Parent(s): 4ba99ec

Add humming instructions

Browse files

Files changed (1) hide show

README.md +9 -4

README.md CHANGED Viewed

@@ -71,10 +71,9 @@ weight is `3 bits (packed) + 16 bits / 128 (group scale) ≈ 3.13 bpp`. The
 }
 ```
-Loading this checkpoint requires a vLLM build with the
-[`humming`](https://github.com/inclusionAI/humming) kernel installed (see the
-[GSQ repo](https://github.com/IST-DASLab/GSQ) `scripts/setup_env.sh` for the
-exact install line).
 > Note: GSQ training first writes shards in `compressed-tensors`
 > `pack-quantized` format (where a 3-bit codebook is padded into a 4-bit
@@ -85,6 +84,12 @@ exact install line).
 ## Serving with vLLM
 ```bash
 vllm serve ISTA-DASLab/Llama-3.1-70B-Instruct-3Bit-GSQ \
   --tensor-parallel-size 2

 }
 ```
+Loading this checkpoint requires vLLM plus the
+[`humming`](https://github.com/inclusionAI/humming) kernels (`pip install
+humming-kernels`). See **Serving with vLLM** below.
 > Note: GSQ training first writes shards in `compressed-tensors`
 > `pack-quantized` format (where a 3-bit codebook is padded into a 4-bit
 ## Serving with vLLM
+Install the Humming kernels (required for vLLM to load this checkpoint):
+```bash
+pip install humming-kernels
+```
 ```bash
 vllm serve ISTA-DASLab/Llama-3.1-70B-Instruct-3Bit-GSQ \
   --tensor-parallel-size 2