Instructions to use TinyLlama/tinyLlama-intermediate-checkpoints-after-1T-token with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use TinyLlama/tinyLlama-intermediate-checkpoints-after-1T-token with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="TinyLlama/tinyLlama-intermediate-checkpoints-after-1T-token")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("TinyLlama/tinyLlama-intermediate-checkpoints-after-1T-token")
model = AutoModelForCausalLM.from_pretrained("TinyLlama/tinyLlama-intermediate-checkpoints-after-1T-token")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use TinyLlama/tinyLlama-intermediate-checkpoints-after-1T-token with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "TinyLlama/tinyLlama-intermediate-checkpoints-after-1T-token"
# 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-intermediate-checkpoints-after-1T-token",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/TinyLlama/tinyLlama-intermediate-checkpoints-after-1T-token

SGLang

How to use TinyLlama/tinyLlama-intermediate-checkpoints-after-1T-token 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-intermediate-checkpoints-after-1T-token" \
    --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-intermediate-checkpoints-after-1T-token",
		"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-intermediate-checkpoints-after-1T-token" \
        --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-intermediate-checkpoints-after-1T-token",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use TinyLlama/tinyLlama-intermediate-checkpoints-after-1T-token with Docker Model Runner:
```
docker model run hf.co/TinyLlama/tinyLlama-intermediate-checkpoints-after-1T-token
```

TinyLlama-1.1B

https://github.com/jzhang38/TinyLlama

The TinyLlama project aims to pretrain a 1.1B Llama model on 3 trillion tokens. With some proper optimization, we can achieve this within a span of "just" 90 days using 16 A100-40G GPUs 🚀🚀. The training has started on 2023-09-01.

We adopted exactly the same architecture and tokenizer as Llama 2. This means TinyLlama can be plugged and played in many open-source projects built upon Llama. Besides, TinyLlama is compact with only 1.1B parameters. This compactness allows it to cater to a multitude of applications demanding a restricted computation and memory footprint.

This Collection

This collection contains all checkpoints after the 1T fix. Branch name indicates the step and number of tokens seen.

Downloads last month: 11

TinyLlama
/

tinyLlama-intermediate-checkpoints-after-1T-token

TinyLlama-1.1B

This Collection

Dataset used to train TinyLlama/tinyLlama-intermediate-checkpoints-after-1T-token