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")

# pip install -U transformers accelerate
# 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", device_map="auto")

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
```

run ram 96gb rtx5060ti16gb ubuntu

by zed122 - opened 16 days ago

Discussion

zed122

16 days ago

(gsq) m@m-HP-Z440-Workstation:~/Desktop/GSQ-RCO-GGUF/GSQ$ vllm serve /home/m/Desktop/GSQ-RCO-GGUF/Llama-3.1-70B-Instruct-2Bit-GSQ
--cpu-offload-gb 20
--gpu-memory-utilization 0.90
--max-model-len 2048
--enforce-eager

#chat.py
from openai import OpenAI

client = OpenAI(
base_url="http://localhost:8000/v1",
api_key="none",
)

"""

response = client.completions.create(
model="/home/m/Desktop/GSQ-RCO-GGUF/Llama-3.1-70B-Instruct-2Bit-GSQ",
prompt=prompt,
max_tokens=100,
temperature=0.6,
stop=["<|eot_id|>", "<|end_of_text|>"],
frequency_penalty=0.5,
)

print("Model Response:\n")
print(response.choices[0].text.strip())

zed122

16 days ago

https://github.com/phox9622-dotcom/suc-GSQ-RCO-GGUF_Llama-3.1-70B-Instruct-2Bit-GSQ/tree/main

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