Instructions to use togethercomputer/GPT-NeoXT-Chat-Base-20B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use togethercomputer/GPT-NeoXT-Chat-Base-20B with Transformers:

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

pipe = pipeline("text-generation", model="togethercomputer/GPT-NeoXT-Chat-Base-20B")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("togethercomputer/GPT-NeoXT-Chat-Base-20B")
model = AutoModelForCausalLM.from_pretrained("togethercomputer/GPT-NeoXT-Chat-Base-20B")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use togethercomputer/GPT-NeoXT-Chat-Base-20B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "togethercomputer/GPT-NeoXT-Chat-Base-20B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "togethercomputer/GPT-NeoXT-Chat-Base-20B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/togethercomputer/GPT-NeoXT-Chat-Base-20B

SGLang

How to use togethercomputer/GPT-NeoXT-Chat-Base-20B 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 "togethercomputer/GPT-NeoXT-Chat-Base-20B" \
    --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": "togethercomputer/GPT-NeoXT-Chat-Base-20B",
		"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 "togethercomputer/GPT-NeoXT-Chat-Base-20B" \
        --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": "togethercomputer/GPT-NeoXT-Chat-Base-20B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use togethercomputer/GPT-NeoXT-Chat-Base-20B with Docker Model Runner:
```
docker model run hf.co/togethercomputer/GPT-NeoXT-Chat-Base-20B
```

jfrick commited on Mar 31, 2023

Commit

b53c82e

1 Parent(s): d386708

Update code examples so they can work out of the box

Browse files

Files changed (1) hide show

README.md +2 -0

README.md CHANGED Viewed

@@ -35,6 +35,7 @@ This allows the model to better adapt to human preferences in the conversations.
 This requires a GPU with 48GB memory.
 ```python
 from transformers import AutoTokenizer, AutoModelForCausalLM
 # init
 tokenizer = AutoTokenizer.from_pretrained("togethercomputer/GPT-NeoXT-Chat-Base-20B")
 model = AutoModelForCausalLM.from_pretrained("togethercomputer/GPT-NeoXT-Chat-Base-20B", torch_dtype=torch.float16)
@@ -66,6 +67,7 @@ print(output_str)
 ```python
 from transformers import AutoTokenizer, AutoModelForCausalLM
 # init
 tokenizer = AutoTokenizer.from_pretrained("togethercomputer/GPT-NeoXT-Chat-Base-20B")
 model = AutoModelForCausalLM.from_pretrained("togethercomputer/GPT-NeoXT-Chat-Base-20B", torch_dtype=torch.bfloat16)

 This requires a GPU with 48GB memory.
 ```python
 from transformers import AutoTokenizer, AutoModelForCausalLM
+import torch
 # init
 tokenizer = AutoTokenizer.from_pretrained("togethercomputer/GPT-NeoXT-Chat-Base-20B")
 model = AutoModelForCausalLM.from_pretrained("togethercomputer/GPT-NeoXT-Chat-Base-20B", torch_dtype=torch.float16)
 ```python
 from transformers import AutoTokenizer, AutoModelForCausalLM
+import torch
 # init
 tokenizer = AutoTokenizer.from_pretrained("togethercomputer/GPT-NeoXT-Chat-Base-20B")
 model = AutoModelForCausalLM.from_pretrained("togethercomputer/GPT-NeoXT-Chat-Base-20B", torch_dtype=torch.bfloat16)