Instructions to use TinyLlama/TinyLlama-1.1B-step-50K-105b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use TinyLlama/TinyLlama-1.1B-step-50K-105b with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="TinyLlama/TinyLlama-1.1B-step-50K-105b")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("TinyLlama/TinyLlama-1.1B-step-50K-105b") model = AutoModelForCausalLM.from_pretrained("TinyLlama/TinyLlama-1.1B-step-50K-105b", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use TinyLlama/TinyLlama-1.1B-step-50K-105b with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "TinyLlama/TinyLlama-1.1B-step-50K-105b" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "TinyLlama/TinyLlama-1.1B-step-50K-105b", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/TinyLlama/TinyLlama-1.1B-step-50K-105b
- SGLang
How to use TinyLlama/TinyLlama-1.1B-step-50K-105b 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 "TinyLlama/TinyLlama-1.1B-step-50K-105b" \ --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": "TinyLlama/TinyLlama-1.1B-step-50K-105b", "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 "TinyLlama/TinyLlama-1.1B-step-50K-105b" \ --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": "TinyLlama/TinyLlama-1.1B-step-50K-105b", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use TinyLlama/TinyLlama-1.1B-step-50K-105b with Docker Model Runner:
docker model run hf.co/TinyLlama/TinyLlama-1.1B-step-50K-105b
Adding Evaluation Results
#12 opened over 2 years ago
by
leaderboard-pr-bot
Update README.md
#11 opened over 2 years ago
by
Sefika
Adding Evaluation Results
#10 opened almost 3 years ago
by
leaderboard-pr-bot
Can I use this model in bf16 mode?
#9 opened almost 3 years ago
by
panzeyu2013
How to train model with databricks-dolly-15k.jsonl dataset format.
#5 opened about 3 years ago
by
Tapendra
How can I use this?
3
#4 opened about 3 years ago
by
Youssefmahboub
Should this work with llama2.c from karpathy?
5
#2 opened about 3 years ago
by
JulianX4