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
```

opt-2.7b-GPTQ-4bit-g128 / config.json

iproskurina

AutoGPTQ model for facebook/opt-2.7b: 4bits, gr128, desc_act=False

fd9bb4d verified about 2 years ago

Raw History Blame

750 Bytes

	{
	"_name_or_path": "facebook/opt-2.7b",
	"_remove_final_layer_norm": false,
	"activation_dropout": 0.0,
	"activation_function": "relu",
	"architectures": [
	"OPTForCausalLM"
	],
	"attention_dropout": 0.0,
	"bos_token_id": 2,
	"do_layer_norm_before": true,
	"dropout": 0.1,
	"enable_bias": true,
	"eos_token_id": 2,
	"ffn_dim": 10240,
	"hidden_size": 2560,
	"init_std": 0.02,
	"layer_norm_elementwise_affine": true,
	"layerdrop": 0.0,
	"max_position_embeddings": 2048,
	"model_type": "opt",
	"num_attention_heads": 32,
	"num_hidden_layers": 32,
	"pad_token_id": 1,
	"prefix": "</s>",
	"torch_dtype": "float16",
	"transformers_version": "4.44.2",
	"use_cache": true,
	"vocab_size": 50272,
	"word_embed_proj_dim": 2560
	}