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

Libraries

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

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("togethercomputer/RedPajama-INCITE-7B-Instruct")
model = AutoModelForCausalLM.from_pretrained("togethercomputer/RedPajama-INCITE-7B-Instruct")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

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

Use Docker

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

SGLang

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

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

carson-together commited on Aug 9, 2023

Commit

7f36397

1 Parent(s): 95667a6

Update README.md

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@@ -7,21 +7,21 @@ datasets:
 - togethercomputer/RedPajama-Data-Instruct
 widget:
 - text: |-
-    Label the tweets as either 'positive', 'negative', 'mixed', or 'neutral':
-    Tweet: I can say that there isn't anything I would change.
     Label: positive
-    Tweet: I'm not sure about this.
     Label: neutral
-    Tweet: I liked some parts but I didn't like other parts.
     Label: mixed
-    Tweet: I think the background image could have been better.
     Label: negative
-    Tweet: I really like it.
     Label:
   example_title: Sentiment Analysis
 - text: |-

 - togethercomputer/RedPajama-Data-Instruct
 widget:
 - text: |-
+    Label the sentences as either 'positive', 'negative', 'mixed', or 'neutral':
+    Sentence: I can say that there isn't anything I would change.
     Label: positive
+    Sentence: I'm not sure about this.
     Label: neutral
+    Sentence: I liked some parts but I didn't like other parts.
     Label: mixed
+    Sentence: I think the background image could have been better.
     Label: negative
+    Sentence: I really like it.
     Label:
   example_title: Sentiment Analysis
 - text: |-