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

Libraries

How to use togethercomputer/Pythia-Chat-Base-7B with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("togethercomputer/Pythia-Chat-Base-7B")
model = AutoModelForCausalLM.from_pretrained("togethercomputer/Pythia-Chat-Base-7B")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use togethercomputer/Pythia-Chat-Base-7B with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/togethercomputer/Pythia-Chat-Base-7B

SGLang

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

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

juewang commited on Mar 29, 2023

Commit

8a32e67

1 Parent(s): 80d8529

Update README.md

Browse files

Files changed (1) hide show

README.md +1 -1

README.md CHANGED Viewed

@@ -21,7 +21,7 @@ In addition to the aforementioned fine-tuning, Pythia-Chat-Base-7B-v0.16 has als
 This process allows the model to better adapt to human preferences in the conversations.
 One of the notable features of Pythia-Chat-Base-7B-v0.16 is its ability to **run inference on a 12GB GPU**, thanks to the quantization technique.
-This makes the model not only highly accurate and efficient but also accessible to a wider range of users and hardware configurations.
 ## Model Details
 - **Developed by**: Together Computer.

 This process allows the model to better adapt to human preferences in the conversations.
 One of the notable features of Pythia-Chat-Base-7B-v0.16 is its ability to **run inference on a 12GB GPU**, thanks to the quantization technique.
+It helps maintain the dialogue capabilities while making the model more accessible to a wider range of users and hardware configurations.
 ## Model Details
 - **Developed by**: Together Computer.