Instructions to use Deepthoughtworks/gpt-neo-2.7B__low-cpu with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Deepthoughtworks/gpt-neo-2.7B__low-cpu with Transformers:

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

pipe = pipeline("text-generation", model="Deepthoughtworks/gpt-neo-2.7B__low-cpu")

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("Deepthoughtworks/gpt-neo-2.7B__low-cpu")
model = AutoModelForMultimodalLM.from_pretrained("Deepthoughtworks/gpt-neo-2.7B__low-cpu")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Deepthoughtworks/gpt-neo-2.7B__low-cpu with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/Deepthoughtworks/gpt-neo-2.7B__low-cpu

SGLang

How to use Deepthoughtworks/gpt-neo-2.7B__low-cpu 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 "Deepthoughtworks/gpt-neo-2.7B__low-cpu" \
    --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": "Deepthoughtworks/gpt-neo-2.7B__low-cpu",
		"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 "Deepthoughtworks/gpt-neo-2.7B__low-cpu" \
        --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": "Deepthoughtworks/gpt-neo-2.7B__low-cpu",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Deepthoughtworks/gpt-neo-2.7B__low-cpu with Docker Model Runner:
```
docker model run hf.co/Deepthoughtworks/gpt-neo-2.7B__low-cpu
```

Update handler.py

by philschmid - opened Nov 19, 2022

base: refs/heads/main

←

from: refs/pr/1

Discussion Files changed

-2

Files changed (1) hide show

handler.py +2 -2

handler.py CHANGED Viewed

@@ -1,7 +1,7 @@
 import torch
 from typing import Dict, List, Any
 # from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
-from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline
 # check for GPU
 device = 0 if torch.cuda.is_available() else -1
@@ -13,7 +13,7 @@ class EndpointHandler:
         tokenizer = AutoTokenizer.from_pretrained(path)
         # model = AutoModel.from_pretrained(path, low_cpu_mem_usage=True)
         # model = AutoModelForCausalLM.from_pretrained(path, low_cpu_mem_usage=True)
-        model = AutoModelForSeq2SeqLM.from_pretrained(path, low_cpu_mem_usage=True)
         # create inference pipeline
         self.pipeline = pipeline("text-generation", model=model, tokenizer=tokenizer, device=device)

 import torch
 from typing import Dict, List, Any
 # from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
+from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
 # check for GPU
 device = 0 if torch.cuda.is_available() else -1
         tokenizer = AutoTokenizer.from_pretrained(path)
         # model = AutoModel.from_pretrained(path, low_cpu_mem_usage=True)
         # model = AutoModelForCausalLM.from_pretrained(path, low_cpu_mem_usage=True)
+        model = AutoModelForCausalLM.from_pretrained(path, low_cpu_mem_usage=True)
         # create inference pipeline
         self.pipeline = pipeline("text-generation", model=model, tokenizer=tokenizer, device=device)