Instructions to use STEM-AI-mtl/phi-2-electrical-engineering with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use STEM-AI-mtl/phi-2-electrical-engineering with Transformers:

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

pipe = pipeline("text-generation", model="STEM-AI-mtl/phi-2-electrical-engineering", trust_remote_code=True)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("STEM-AI-mtl/phi-2-electrical-engineering", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("STEM-AI-mtl/phi-2-electrical-engineering", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use STEM-AI-mtl/phi-2-electrical-engineering with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "STEM-AI-mtl/phi-2-electrical-engineering"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "STEM-AI-mtl/phi-2-electrical-engineering",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/STEM-AI-mtl/phi-2-electrical-engineering

SGLang

How to use STEM-AI-mtl/phi-2-electrical-engineering 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 "STEM-AI-mtl/phi-2-electrical-engineering" \
    --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": "STEM-AI-mtl/phi-2-electrical-engineering",
		"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 "STEM-AI-mtl/phi-2-electrical-engineering" \
        --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": "STEM-AI-mtl/phi-2-electrical-engineering",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use STEM-AI-mtl/phi-2-electrical-engineering with Docker Model Runner:
```
docker model run hf.co/STEM-AI-mtl/phi-2-electrical-engineering
```

STEM-AI-mtl commited on Apr 2, 2024

Commit

ea44c8a

verified ·

1 Parent(s): 24df9a5

Update README.md

Browse files

Files changed (1) hide show

README.md +5 -7

README.md CHANGED Viewed

@@ -42,15 +42,13 @@ Refer to microsoft/phi-2 model card for recommended prompt format.
 ### Training Data
-Dataset related to electrical engineering: STEM-AI-mtl/Electrical-engineering
 It is composed of queries, 65% about general electrical engineering, 25% about Kicad (EDA software) and 10% about Python code for Kicad's scripting console.
-Combined with
-Dataset related to STEM and NLP: garage-bAInd/Open-Platypus
 ### Training Procedure
-LoRa script:  https://github.com/STEM-ai/Phi-2/raw/4eaa6aaa2679427a810ace5a061b9c951942d66a/LoRa.py
 A LoRa PEFT was performed on a 48 Gb A40 Nvidia GPU.
@@ -61,6 +59,6 @@ William Harbec
 ### Inference example
-Standard: https://github.com/STEM-ai/Phi-2/blob/4eaa6aaa2679427a810ace5a061b9c951942d66a/chat.py
-GPTQ format: https://github.com/STEM-ai/Phi-2/blob/ab1ced8d7922765344d824acf1924df99606b4fc/chat-GPTQ.py

 ### Training Data
+Dataset related to electrical engineering: [STEM-AI-mtl/Electrical-engineering](https://huggingface.co/datasets/STEM-AI-mtl/Electrical-engineering)
 It is composed of queries, 65% about general electrical engineering, 25% about Kicad (EDA software) and 10% about Python code for Kicad's scripting console.
+In additionataset related to STEM and NLP: [garage-bAInd/Open-Platypus](https://huggingface.co/datasets/garage-bAInd/Open-Platypus)
 ### Training Procedure
+[LoRa script](https://github.com/STEM-ai/Phi-2/raw/4eaa6aaa2679427a810ace5a061b9c951942d66a/LoRa.py)
 A LoRa PEFT was performed on a 48 Gb A40 Nvidia GPU.
 ### Inference example
+[Standard](https://github.com/STEM-ai/Phi-2/blob/4eaa6aaa2679427a810ace5a061b9c951942d66a/chat.py)
+[GPTQ format](https://github.com/STEM-ai/Phi-2/blob/ab1ced8d7922765344d824acf1924df99606b4fc/chat-GPTQ.py)