Text Generation
Transformers.js
PyTorch
ONNX
Safetensors
Transformers
llama
text-generation-inference
Instructions to use Xenova/llama2.c-stories15M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers.js
How to use Xenova/llama2.c-stories15M with Transformers.js:
// npm i @huggingface/transformers import { pipeline } from '@huggingface/transformers'; // Allocate pipeline const pipe = await pipeline('text-generation', 'Xenova/llama2.c-stories15M'); - Transformers
How to use Xenova/llama2.c-stories15M with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Xenova/llama2.c-stories15M")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Xenova/llama2.c-stories15M") model = AutoModelForCausalLM.from_pretrained("Xenova/llama2.c-stories15M") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use Xenova/llama2.c-stories15M with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Xenova/llama2.c-stories15M" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Xenova/llama2.c-stories15M", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Xenova/llama2.c-stories15M
- SGLang
How to use Xenova/llama2.c-stories15M 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 "Xenova/llama2.c-stories15M" \ --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": "Xenova/llama2.c-stories15M", "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 "Xenova/llama2.c-stories15M" \ --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": "Xenova/llama2.c-stories15M", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Xenova/llama2.c-stories15M with Docker Model Runner:
docker model run hf.co/Xenova/llama2.c-stories15M
Usage (Transformers.js)
If you haven't already, you can install the Transformers.js JavaScript library from NPM using:
npm i @huggingface/transformers
You can then use the model to generate text like this:
import { pipeline } from "@huggingface/transformers";
// Create a text-generation pipeline
const generator = await pipeline('text-generation', 'Xenova/llama2.c-stories15M');
const text = 'Once upon a time,';
const output = await generator(text);
console.log(output);
// [{ generated_text: "Once upon a time, there was a little girl named Lily. She loved to play outside in" }]
const output2 = await generator(text, { max_new_tokens: 50 });
console.log(output2);
// [{ generated_text: "Once upon a time, there was a little girl named Lily. She loved to play outside in the sunshine. One day, she saw a big, dark cloud in the sky. She knew it was going to rain soon.\nLily ran inside her house" }]
- Downloads last month
- 7,978