Instructions to use togethercomputer/RedPajama-INCITE-7B-Chat with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use togethercomputer/RedPajama-INCITE-7B-Chat with Transformers:

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

pipe = pipeline("text-generation", model="togethercomputer/RedPajama-INCITE-7B-Chat")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("togethercomputer/RedPajama-INCITE-7B-Chat")
model = AutoModelForCausalLM.from_pretrained("togethercomputer/RedPajama-INCITE-7B-Chat", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use togethercomputer/RedPajama-INCITE-7B-Chat with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "togethercomputer/RedPajama-INCITE-7B-Chat"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "togethercomputer/RedPajama-INCITE-7B-Chat",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/togethercomputer/RedPajama-INCITE-7B-Chat

SGLang

How to use togethercomputer/RedPajama-INCITE-7B-Chat 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/RedPajama-INCITE-7B-Chat" \
    --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/RedPajama-INCITE-7B-Chat",
		"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/RedPajama-INCITE-7B-Chat" \
        --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/RedPajama-INCITE-7B-Chat",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use togethercomputer/RedPajama-INCITE-7B-Chat with Docker Model Runner:
```
docker model run hf.co/togethercomputer/RedPajama-INCITE-7B-Chat
```

zhangce commited on May 5, 2023

Commit

9c5de29

1 Parent(s): 6f2c961

Update README.md

Browse files

Files changed (1) hide show

README.md +2 -2

README.md CHANGED Viewed

@@ -28,7 +28,7 @@ tokenizer = AutoTokenizer.from_pretrained("togethercomputer/RedPajama-Chat-INCIT
 model = AutoModelForCausalLM.from_pretrained("togethercomputer/RedPajama-Chat-INCITE-6.9B-v1", torch_dtype=torch.float16)
 model = model.to('cuda:0')
 # infer
-inputs = tokenizer("Hello", return_tensors='pt').to(model.device)
 outputs = model.generate(**inputs, max_new_tokens=10, do_sample=True, temperature=0.8)
 output_str = tokenizer.decode(outputs[0])
 print(output_str)
@@ -44,7 +44,7 @@ from transformers import AutoTokenizer, AutoModelForCausalLM
 tokenizer = AutoTokenizer.from_pretrained("togethercomputer/RedPajama-Chat-INCITE-6.9B-v1")
 model = AutoModelForCausalLM.from_pretrained("togethercomputer/RedPajama-Chat-INCITE-6.9B-v1", device_map="auto", load_in_8bit=True)
 # infer
-inputs = tokenizer("Hello", return_tensors='pt').to(model.device)
 outputs = model.generate(**inputs, max_new_tokens=10, do_sample=True, temperature=0.8)
 output_str = tokenizer.decode(outputs[0])
 print(output_str)

 model = AutoModelForCausalLM.from_pretrained("togethercomputer/RedPajama-Chat-INCITE-6.9B-v1", torch_dtype=torch.float16)
 model = model.to('cuda:0')
 # infer
+inputs = tokenizer("<human>: Hello!\n<bot>:", return_tensors='pt').to(model.device)
 outputs = model.generate(**inputs, max_new_tokens=10, do_sample=True, temperature=0.8)
 output_str = tokenizer.decode(outputs[0])
 print(output_str)
 tokenizer = AutoTokenizer.from_pretrained("togethercomputer/RedPajama-Chat-INCITE-6.9B-v1")
 model = AutoModelForCausalLM.from_pretrained("togethercomputer/RedPajama-Chat-INCITE-6.9B-v1", device_map="auto", load_in_8bit=True)
 # infer
+inputs = tokenizer("<human>: Hello!\n<bot>:", return_tensors='pt').to(model.device)
 outputs = model.generate(**inputs, max_new_tokens=10, do_sample=True, temperature=0.8)
 output_str = tokenizer.decode(outputs[0])
 print(output_str)