Instructions to use 02fe22bee008/gpt2-cell-recommendation-system with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use 02fe22bee008/gpt2-cell-recommendation-system with Transformers:

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

pipe = pipeline("text-generation", model="02fe22bee008/gpt2-cell-recommendation-system")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("02fe22bee008/gpt2-cell-recommendation-system")
model = AutoModelForCausalLM.from_pretrained("02fe22bee008/gpt2-cell-recommendation-system")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use 02fe22bee008/gpt2-cell-recommendation-system with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "02fe22bee008/gpt2-cell-recommendation-system"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "02fe22bee008/gpt2-cell-recommendation-system",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/02fe22bee008/gpt2-cell-recommendation-system

SGLang

How to use 02fe22bee008/gpt2-cell-recommendation-system 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 "02fe22bee008/gpt2-cell-recommendation-system" \
    --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": "02fe22bee008/gpt2-cell-recommendation-system",
		"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 "02fe22bee008/gpt2-cell-recommendation-system" \
        --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": "02fe22bee008/gpt2-cell-recommendation-system",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use 02fe22bee008/gpt2-cell-recommendation-system with Docker Model Runner:
```
docker model run hf.co/02fe22bee008/gpt2-cell-recommendation-system
```

Model Card for Model ID

This model performs intelligent battery cell fault diagnosis using real-time voltage, temperature, and current inputs. It consists of a hybrid architecture:

A RandomForestClassifier predicts the cell fault status.

A GPT-2 model, fine-tuned on labeled diagnosis data, generates recommendations based on sensor readings.

The model is deployed with an TTS modules can be added to explain results via speech or video.

Model Details

Model Description

A RandomForestClassifier for predicting battery cell status based on voltage, temperature, and current.

A fine-tuned GPT-2 language model to generate recommendation text tailored to the predicted fault.

This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.

Developed by: Bhoomika Patil (IEEE Member, AESS Vice Chair 2024, PES Chair 2025)
Funded by [optional]: [More Information Needed]
Shared by [optional]: [More Information Needed]
Model type: Classification + Text Generation
Language(s) (NLP): [More Information Needed]
License: [More Information Needed]
Finetuned from model [optional]: gpt2 by Hugging Face

Model Sources [optional]

Repository: [More Information Needed]
Paper [optional]: [More Information Needed]
Demo [optional]: [More Information Needed]

Uses

Direct Use

This model is intended to be used in:

Embedded EV BMS fault diagnosis

Lab-scale battery monitoring systems

Educational/academic demonstration kits

Downstream Use [optional]

Can be used for:

Real-time IoT fault detection dashboards

Voice-based battery recommendation bots

Integration into EV charging systems

Out-of-Scope Use

Not suitable for grid-level battery systems without retraining

Not a substitute for hardware-based protections

Bias, Risks, and Limitations

May give inaccurate predictions outside of the trained sensor range

GPT-2 recommendations may not always be technically feasible or safe

Does not consider time-series or historical trends

Limited robustness for deployment in production-grade EVs

Recommendations

Validate GPT-generated text before acting

Add confidence thresholds for alerts

Complement with hardware-level fault detection

How to Get Started with the Model

Use the code below to get started with the model.

from transformers import GPT2Tokenizer, GPT2LMHeadModel

tokenizer = GPT2Tokenizer.from_pretrained("your-username/gpt2-cell-diagnosis-system") model = GPT2LMHeadModel.from_pretrained("your-username/gpt2-cell-diagnosis-system")

prompt = "Voltage: 3.2V, Temperature: 48C, Current: 0.7A, Status: Overheat\nRecommendation:" inputs = tokenizer(prompt, return_tensors="pt") outputs = model.generate(**inputs, max_length=60) print(tokenizer.decode(outputs[0]))

Training Details

Training Data

Custom Excel dataset (CellDD.xlsx)

Includes labeled examples of Voltage, Temperature, Current, Status, and textual recommendations

Training Procedure

Preprocessing [optional]

Numerical columns normalized

Text cleaned and used to create prompts for GPT-2

Training Hyperparameters

Training regime: [More Information Needed]

Speeds, Sizes, Times [optional]

[More Information Needed]

Evaluation

Testing Data, Factors & Metrics

Testing Data

20% split from original dataset (not public)

Factors

Classifier Accuracy: ~96%

Manual evaluation of GPT-2 text output

Metrics

Classifier Accuracy: ~96%

Manual evaluation of GPT-2 text output

Results

[More Information Needed]

Summary

Model Examination [optional]

[More Information Needed]

Environmental Impact

Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

Hardware Type: Google Colab (1x T4 GPU)
Hours used: ~25 minutes
Cloud Provider: [More Information Needed]
Compute Region: [More Information Needed]
Carbon Emitted: [More Information Needed]

Technical Specifications [optional]

Model Architecture and Objective

[More Information Needed]

Compute Infrastructure

[More Information Needed]

Hardware

[More Information Needed]

Software

[More Information Needed]

Citation [optional]

BibTeX:

[More Information Needed]

APA:

[More Information Needed]

Glossary [optional]

[More Information Needed]

More Information [optional]

[More Information Needed]

Model Card Authors [optional]

[More Information Needed]

Model Card Contact

[More Information Needed]

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Safetensors

Model size

0.1B params

Tensor type

F32

Paper for 02fe22bee008/gpt2-cell-recommendation-system

Quantifying the Carbon Emissions of Machine Learning

Paper • 1910.09700 • Published Oct 21, 2019 • 48