Instructions to use DataPilot/ArrowSmartPlus_3.6B_instruction with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use DataPilot/ArrowSmartPlus_3.6B_instruction with Transformers:

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

pipe = pipeline("text-generation", model="DataPilot/ArrowSmartPlus_3.6B_instruction")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("DataPilot/ArrowSmartPlus_3.6B_instruction")
model = AutoModelForCausalLM.from_pretrained("DataPilot/ArrowSmartPlus_3.6B_instruction")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use DataPilot/ArrowSmartPlus_3.6B_instruction with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "DataPilot/ArrowSmartPlus_3.6B_instruction"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "DataPilot/ArrowSmartPlus_3.6B_instruction",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/DataPilot/ArrowSmartPlus_3.6B_instruction

SGLang

How to use DataPilot/ArrowSmartPlus_3.6B_instruction 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 "DataPilot/ArrowSmartPlus_3.6B_instruction" \
    --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": "DataPilot/ArrowSmartPlus_3.6B_instruction",
		"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 "DataPilot/ArrowSmartPlus_3.6B_instruction" \
        --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": "DataPilot/ArrowSmartPlus_3.6B_instruction",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use DataPilot/ArrowSmartPlus_3.6B_instruction with Docker Model Runner:
```
docker model run hf.co/DataPilot/ArrowSmartPlus_3.6B_instruction
```

DataPilot commited on Mar 27, 2024

Commit

913cf2d

verified ·

1 Parent(s): 54ae212

3

Browse files

Files changed (1) hide show

README.md +14 -4

README.md CHANGED Viewed

@@ -6,17 +6,27 @@ language: ja
 ## 概要
-「LOCAL AI HACKATHON」における、チームDataPilot,3つめの成果品です。Line社が開発した「japanese-large-lm-3.6b-instruction-sft」をウィキブックの内容をもとに中学、高校範囲にてファインチューニングを行いました。
 ## how to use
 ```python
 import torch
 from transformers import AutoModelForSequenceClassification
-from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline #transformerとtorchがインストールされていることを前提とします。
-model = AutoModelForCausalLM.from_pretrainedmodel = AutoModelForCausalLM.from_pretrained("DataPilot/ArrowSmartPlus_3.6B_instant_sft")
-tokenizer = AutoTokenizer.from_pretrained("DataPilot/ArrowSmartPlus_3.6B_instant_sft")
 generator = pipeline("text-generation", model=model, tokenizer=tokenizer, device=0)

 ## 概要
+「LOCAL AI HACKATHON」における、チームDataPilot,4つめの成果品です。Line社が開発した「japanese-large-lm-3.6b-instruction-sft」をウィキブックの内容をもとに中学、高校範囲にてファインチューニングを行いました。
+それに加え、saldra/sakura_japanese_datasetでインストラクションチューニングを行いました。
 ## how to use
 ```python
+#pip install transformer
+#pip install pipline
+#pip install protobu
+#pip install accelerate
+#pip install sentencepiece
+#pip install torch
+#以上6つをピップインストールしてください。
 import torch
 from transformers import AutoModelForSequenceClassification
+from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
+model = AutoModelForCausalLM.from_pretrainedmodel = AutoModelForCausalLM.from_pretrained("DataPilot/ArrowSmartPlus_3.6B_instruction")
+tokenizer = AutoTokenizer.from_pretrained("DataPilot/ArrowSmartPlus_3.6B_instruction")
 generator = pipeline("text-generation", model=model, tokenizer=tokenizer, device=0)