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)

Inference
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 Jan 7, 2024

Commit

2536e0a

1 Parent(s): 80d01d4

Update README.md

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@@ -11,10 +11,10 @@ tags:
 ---
 # Model Card for Model ID
-This is the adapters from the LoRa fine-tuning of the phi-2 model from Microsoft. It was trained on the Electrical-engineering dataset.
 - **Developed by:** STEM.AI
-- **Model type:** Q&A
 - **Language(s) (NLP):** English
 - **Finetuned from model [optional]:** microsoft/phi-2
@@ -28,16 +28,19 @@ Q&A related to electrical engineering, and Kicad software. Creation of Python co
 ### Training Data
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
-[More Information Needed]
 ### Training Procedure
-A LoRa PEFT was performed on a 48 Gb Nvidia GPU.
 ## Model Card Authors [optional]
 STEM.AI: stem.ai.mtl@gmail.com

 ---
 # Model Card for Model ID
+This is the adapters from the LoRa fine-tuning of the phi-2 model from Microsoft. It was trained on the Electrical-engineering dataset combined with garage-bAInd/Open-Platypus.
 - **Developed by:** STEM.AI
+- **Model type:** Q&A and code generation
 - **Language(s) (NLP):** English
 - **Finetuned from model [optional]:** microsoft/phi-2
 ### Training Data
+Dataset related to electrical engineering: STEM-AI-mtl/Electrical-engineering
+Combined with
+Dataset related to STEM and NLP: garage-bAInd/Open-Platypus
 ### Training Procedure
+A LoRa PEFT was performed on a 48 Gb A40 Nvidia GPU.
 ## Model Card Authors [optional]
 STEM.AI: stem.ai.mtl@gmail.com
+William Harbec