Instructions to use unsloth/tinyllama with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use unsloth/tinyllama with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="unsloth/tinyllama")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("unsloth/tinyllama") model = AutoModelForCausalLM.from_pretrained("unsloth/tinyllama") - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use unsloth/tinyllama with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "unsloth/tinyllama" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "unsloth/tinyllama", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/unsloth/tinyllama
- SGLang
How to use unsloth/tinyllama 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 "unsloth/tinyllama" \ --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": "unsloth/tinyllama", "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 "unsloth/tinyllama" \ --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": "unsloth/tinyllama", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Unsloth Studio new
How to use unsloth/tinyllama with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for unsloth/tinyllama to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for unsloth/tinyllama to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for unsloth/tinyllama to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="unsloth/tinyllama", max_seq_length=2048, ) - Docker Model Runner
How to use unsloth/tinyllama with Docker Model Runner:
docker model run hf.co/unsloth/tinyllama
Finetune Mistral, Gemma, Llama 2-5x faster with 70% less memory via Unsloth!
A reupload from https://huggingface.co/TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T
We have a Google Colab Tesla T4 notebook for TinyLlama with 4096 max sequence length RoPE Scaling here: https://colab.research.google.com/drive/1AZghoNBQaMDgWJpi4RbffGM1h6raLUj9?usp=sharing
โจ Finetune for Free
All notebooks are beginner friendly! Add your dataset, click "Run All", and you'll get a 2x faster finetuned model which can be exported to GGUF, vLLM or uploaded to Hugging Face.
| Unsloth supports | Free Notebooks | Performance | Memory use |
|---|---|---|---|
| Gemma 7b | โถ๏ธ Start on Colab | 2.4x faster | 58% less |
| Mistral 7b | โถ๏ธ Start on Colab | 2.2x faster | 62% less |
| Llama-2 7b | โถ๏ธ Start on Colab | 2.2x faster | 43% less |
| TinyLlama | โถ๏ธ Start on Colab | 3.9x faster | 74% less |
| CodeLlama 34b A100 | โถ๏ธ Start on Colab | 1.9x faster | 27% less |
| Mistral 7b 1xT4 | โถ๏ธ Start on Kaggle | 5x faster* | 62% less |
| DPO - Zephyr | โถ๏ธ Start on Colab | 1.9x faster | 19% less |
- This conversational notebook is useful for ShareGPT ChatML / Vicuna templates.
- This text completion notebook is for raw text. This DPO notebook replicates Zephyr.
- * Kaggle has 2x T4s, but we use 1. Due to overhead, 1x T4 is 5x faster.
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