Instructions to use iproskurina/opt-2.7b-GPTQ-4bit-g128 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use iproskurina/opt-2.7b-GPTQ-4bit-g128 with Transformers:

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

pipe = pipeline("text-generation", model="iproskurina/opt-2.7b-GPTQ-4bit-g128")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("iproskurina/opt-2.7b-GPTQ-4bit-g128")
model = AutoModelForCausalLM.from_pretrained("iproskurina/opt-2.7b-GPTQ-4bit-g128", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use iproskurina/opt-2.7b-GPTQ-4bit-g128 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "iproskurina/opt-2.7b-GPTQ-4bit-g128"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "iproskurina/opt-2.7b-GPTQ-4bit-g128",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/iproskurina/opt-2.7b-GPTQ-4bit-g128

SGLang

How to use iproskurina/opt-2.7b-GPTQ-4bit-g128 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 "iproskurina/opt-2.7b-GPTQ-4bit-g128" \
    --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": "iproskurina/opt-2.7b-GPTQ-4bit-g128",
		"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 "iproskurina/opt-2.7b-GPTQ-4bit-g128" \
        --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": "iproskurina/opt-2.7b-GPTQ-4bit-g128",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use iproskurina/opt-2.7b-GPTQ-4bit-g128 with Docker Model Runner:
```
docker model run hf.co/iproskurina/opt-2.7b-GPTQ-4bit-g128
```

iproskurina commited on Sep 23, 2024

Commit

e49651b

verified ·

1 Parent(s): 70bb27a

Update README.md

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README.md +16 -4

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@@ -38,13 +38,25 @@ The grouping size used for quantization is equal to 128.
 ### Install the necessary packages
 ```shell
-pip install accelerate==0.26.1 datasets==2.16.1 dill==0.3.7 gekko==1.0.6 multiprocess==0.70.15 peft==0.7.1 rouge==1.0.1 sentencepiece==0.1.99
-git clone https://github.com/upunaprosk/AutoGPTQ
 cd AutoGPTQ
-pip install -v .
 ```
-Recommended transformers version: 4.35.2.
 ### You can then use the following code

 ### Install the necessary packages
+Requires: Transformers 4.33.0 or later, Optimum 1.12.0 or later, and AutoGPTQ 0.4.2 or later.
+```shell
+pip3 install --upgrade transformers optimum
+# If using PyTorch 2.1 + CUDA 12.x:
+pip3 install --upgrade auto-gptq
+# or, if using PyTorch 2.1 + CUDA 11.x:
+pip3 install --upgrade auto-gptq --extra-index-url https://huggingface.github.io/autogptq-index/whl/cu118/
+```
+If you are using PyTorch 2.0, you will need to install AutoGPTQ from source. Likewise if you have problems with the pre-built wheels, you should try building from source:
 ```shell
+pip3 uninstall -y auto-gptq
+git clone https://github.com/PanQiWei/AutoGPTQ
 cd AutoGPTQ
+git checkout v0.5.1
+pip3 install .
 ```
 ### You can then use the following code