Instructions to use pfnet/plamo-13b-instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use pfnet/plamo-13b-instruct with Transformers:

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

pipe = pipeline("text-generation", model="pfnet/plamo-13b-instruct", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("pfnet/plamo-13b-instruct", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use pfnet/plamo-13b-instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "pfnet/plamo-13b-instruct"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "pfnet/plamo-13b-instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/pfnet/plamo-13b-instruct

SGLang

How to use pfnet/plamo-13b-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 "pfnet/plamo-13b-instruct" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "pfnet/plamo-13b-instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "pfnet/plamo-13b-instruct" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "pfnet/plamo-13b-instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use pfnet/plamo-13b-instruct with Docker Model Runner:
```
docker model run hf.co/pfnet/plamo-13b-instruct
```

Add "chat_template" to tokenizer_config.json

by mitmul - opened Jan 23, 2024

base: refs/heads/main

←

from: refs/pr/1

Discussion Files changed

-0

mitmul

Preferred Networks, Inc. org Jan 23, 2024

Since version 4.34.0, Transformers has added a feature called chat_template to its tokenizer. Like other LLMs, PLaMo also performs better in dialogues when inputs are provided according to the expected template. This Pull Request (PR) adds the necessary fields to tokenizer_config.json to use Transformers' apply_chat_template() function to transform inputs into the format expected by PLaMo.

Add "chat_template" to tokenizer_config.json2de2f120

Add assistant role28979cfb

Update tokenizer_config.jsone6f09648

Add assistant content to promptf8e1bb15

torotoki

Jan 25, 2024

LGTM!

torotoki changed pull request status to merged Jan 25, 2024

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