Text Generation
Transformers
Safetensors
English
llama
raspberry-pi
gpio
embedded
structured-output
json
tiny
Eval Results (legacy)
text-generation-inference
Instructions to use AwaleSagar/gpio-llm-nano-rpi5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
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
Download training/pretrain_log.json from AwaleSagar/gpio-llm-nano-rpi5: direct link, hf CLI and curl.
- Browser
- Download file 1.44 kB
-
https://huggingface.co/AwaleSagar/gpio-llm-nano-rpi5/resolve/main/training/pretrain_log.json
- Command line
-
hf download hf://AwaleSagar/gpio-llm-nano-rpi5/training/pretrain_log.json
-
curl -L -o pretrain_log.json https://huggingface.co/AwaleSagar/gpio-llm-nano-rpi5/resolve/main/training/pretrain_log.json
1.44 kB
| { | |
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| "warmup": 100, | |
| "min_lr_frac": 0.1, | |
| "eval_every": 1000, | |
| "max_minutes": 45.0, | |
| "seed": 1234, | |
| "out": "nano-pt", | |
| "compile": true | |
| }, | |
| "params": 4960704, | |
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| ] | |
| } |