Instructions to use AwaleSagar/gpio-llm-nano-rpi5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use AwaleSagar/gpio-llm-nano-rpi5 with Transformers:

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

pipe = pipeline("text-generation", model="AwaleSagar/gpio-llm-nano-rpi5")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("AwaleSagar/gpio-llm-nano-rpi5")
model = AutoModelForCausalLM.from_pretrained("AwaleSagar/gpio-llm-nano-rpi5", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use AwaleSagar/gpio-llm-nano-rpi5 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "AwaleSagar/gpio-llm-nano-rpi5"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "AwaleSagar/gpio-llm-nano-rpi5",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/AwaleSagar/gpio-llm-nano-rpi5

SGLang

How to use AwaleSagar/gpio-llm-nano-rpi5 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 "AwaleSagar/gpio-llm-nano-rpi5" \
    --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": "AwaleSagar/gpio-llm-nano-rpi5",
		"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 "AwaleSagar/gpio-llm-nano-rpi5" \
        --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": "AwaleSagar/gpio-llm-nano-rpi5",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use AwaleSagar/gpio-llm-nano-rpi5 with Docker Model Runner:
```
docker model run hf.co/AwaleSagar/gpio-llm-nano-rpi5
```

gpio-llm-nano-rpi5 / training /eval_c_engine_pi_zero2w.json

AwaleSagar

Release gpio-llm-nano-rpi5 (GPIO-LLM v0.1.0)

8579c16 verified 12 days ago

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585 Bytes

	{
	"file": "pi_nano_constrained.out",
	"rows": 5083,
	"exact": 0.9171748967145387,
	"valid_json": 1.0,
	"unsafe_execute": 0.048238482384823846,
	"non_exec_rows": 1845,
	"latency_ms_p50": 132.3,
	"latency_ms_p95": 279.96,
	"latency_ms_max": 1514.0,
	"per_action_exact": {
	"ask_clarification": 0.8560606060606061,
	"gpio_mode": 0.9637305699481865,
	"gpio_pulse": 0.9755434782608695,
	"gpio_pwm": 0.9965397923875432,
	"gpio_read": 0.9877622377622378,
	"gpio_sequence": 0.9046653144016227,
	"gpio_write": 0.8296370967741935,
	"report_error": 0.9139784946236559,
	"wait": 1.0
	}
	}