Text Generation
Transformers
PyTorch
English
llama
llama2
llama-2
llama2 architecture
litellama
text-generation-inference
Instructions to use ahxt/LiteLlama-460M-1T with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ahxt/LiteLlama-460M-1T with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="ahxt/LiteLlama-460M-1T")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("ahxt/LiteLlama-460M-1T") model = AutoModelForCausalLM.from_pretrained("ahxt/LiteLlama-460M-1T") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use ahxt/LiteLlama-460M-1T with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "ahxt/LiteLlama-460M-1T" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "ahxt/LiteLlama-460M-1T", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/ahxt/LiteLlama-460M-1T
- SGLang
How to use ahxt/LiteLlama-460M-1T 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 "ahxt/LiteLlama-460M-1T" \ --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": "ahxt/LiteLlama-460M-1T", "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 "ahxt/LiteLlama-460M-1T" \ --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": "ahxt/LiteLlama-460M-1T", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use ahxt/LiteLlama-460M-1T with Docker Model Runner:
docker model run hf.co/ahxt/LiteLlama-460M-1T
About the maximum length of input token
#14 opened almost 2 years ago
by
PandlBird
Adding Evaluation Results
#13 opened about 2 years ago
by
leaderboard-pr-bot
Adding Evaluation Results
#12 opened about 2 years ago
by
leaderboard-pr-bot
Adding `safetensors` variant of this model
#11 opened over 2 years ago
by
SFconvertbot
Time to make MoE lol
1
#10 opened over 2 years ago
by
Kquant03
EOS and PAD tokens
👍 2
3
#9 opened over 2 years ago
by
dvruette
Make Training Details Caculation Visible
#8 opened over 2 years ago
by
maywell
how did you train it?
👍 2
#7 opened over 2 years ago
by
lxww301
Boring answer
1
#6 opened over 2 years ago
by
foowaa
OpenSource Code
1
#4 opened over 2 years ago
by
satpalsr
Update README.md
#3 opened over 2 years ago
by
Tonic
Add tokenizer.json
#2 opened over 2 years ago
by
Xenova
Add ONNX weights
#1 opened over 2 years ago
by
Xenova