Instructions to use OCPP-PulseEnergy/pulseenergy-ocpp-llm with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use OCPP-PulseEnergy/pulseenergy-ocpp-llm with Transformers:

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

pipe = pipeline("text-generation", model="OCPP-PulseEnergy/pulseenergy-ocpp-llm")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("OCPP-PulseEnergy/pulseenergy-ocpp-llm", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use OCPP-PulseEnergy/pulseenergy-ocpp-llm with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/OCPP-PulseEnergy/pulseenergy-ocpp-llm

SGLang

How to use OCPP-PulseEnergy/pulseenergy-ocpp-llm 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 "OCPP-PulseEnergy/pulseenergy-ocpp-llm" \
    --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": "OCPP-PulseEnergy/pulseenergy-ocpp-llm",
		"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 "OCPP-PulseEnergy/pulseenergy-ocpp-llm" \
        --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": "OCPP-PulseEnergy/pulseenergy-ocpp-llm",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio

How to use OCPP-PulseEnergy/pulseenergy-ocpp-llm with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for OCPP-PulseEnergy/pulseenergy-ocpp-llm to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for OCPP-PulseEnergy/pulseenergy-ocpp-llm to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for OCPP-PulseEnergy/pulseenergy-ocpp-llm to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="OCPP-PulseEnergy/pulseenergy-ocpp-llm",
    max_seq_length=2048,
)

Docker Model Runner
How to use OCPP-PulseEnergy/pulseenergy-ocpp-llm with Docker Model Runner:
```
docker model run hf.co/OCPP-PulseEnergy/pulseenergy-ocpp-llm
```

pulseenergy-ocpp-llm

A fine-tuned diagnostics model for OCPP (Open Charge Point Protocol) EV charging networks, built by Pulse Energy. It is trained to interpret OCPP message logs, surface likely root causes for charger faults, and explain protocol-level behaviour in plain language.

Model details

Developed by: Pulse Energy Technologies Pvt. Ltd.
Base model: Qwen2.5-7B
Language: English
License: MIT

Intended use

Designed for charge point operators (CPOs) and support teams to:

Diagnose charger faults from OCPP message traces (BootNotification, StatusNotification, MeterValues, StartTransaction/StopTransaction, etc.)
Map OCPP error codes and status transitions to probable causes
Explain OCPP 1.6 / 2.0.1 protocol behaviour and message semantics
Assist first-line support triage before escalation

Limitations and recommendations

Coverage is strongest for common OCPP 1.6 fault patterns; rarer or vendor-specific extensions may be less reliable.
The model can produce plausible but incorrect root-cause attributions; keep a human in the loop for any operational action.
Recommended decoding: low temperature (≈0.2–0.4) for deterministic diagnostics.

Citation

@misc{pulseenergy_ocpp_llm,
  title  = {pulseenergy-ocpp-llm: An OCPP Diagnostics LLM},
  author = {Pulse Energy Technologies},
  year   = {2026},
  howpublished = {\url{https://huggingface.co/OCPP-PulseEnergy/pulseenergy-ocpp-llm}}
}

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Model tree for OCPP-PulseEnergy/pulseenergy-ocpp-llm

Base model

Qwen/Qwen2.5-7B

Finetuned

unsloth/Qwen2.5-7B

Adapter

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