Text Generation
Transformers
Safetensors
English
gpt2
Neuron
Neura Tech AI
Neuron-300M
text-generation-inference
Instructions to use Neuron-AI-Labs/Neuron-300M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Neuron-AI-Labs/Neuron-300M with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Neuron-AI-Labs/Neuron-300M")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Neuron-AI-Labs/Neuron-300M") model = AutoModelForCausalLM.from_pretrained("Neuron-AI-Labs/Neuron-300M", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Neuron-AI-Labs/Neuron-300M with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Neuron-AI-Labs/Neuron-300M" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Neuron-AI-Labs/Neuron-300M", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Neuron-AI-Labs/Neuron-300M
- SGLang
How to use Neuron-AI-Labs/Neuron-300M 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 "Neuron-AI-Labs/Neuron-300M" \ --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": "Neuron-AI-Labs/Neuron-300M", "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 "Neuron-AI-Labs/Neuron-300M" \ --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": "Neuron-AI-Labs/Neuron-300M", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Neuron-AI-Labs/Neuron-300M with Docker Model Runner:
docker model run hf.co/Neuron-AI-Labs/Neuron-300M
Download training_args.bin from Neuron-AI-Labs/Neuron-300M: direct link, hf CLI and curl.
- Browser
- Download file 5.2 kB
-
https://huggingface.co/Neuron-AI-Labs/Neuron-300M/resolve/main/training_args.bin
- Command line
-
hf download hf://Neuron-AI-Labs/Neuron-300M/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Neuron-AI-Labs/Neuron-300M/resolve/main/training_args.bin
5.2 kB
- Xet hash:
- cdea973dec20201860b24132b23240e395294c7485b474a1c2708536638124b9
- Size of remote file:
- 5.2 kB
- SHA256:
- 57e9333bc9cf95a57f871de91af3359001c374a2b049de0d90a527ee093c30ea
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