Instructions to use sapbot/toyllama-30m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sapbot/toyllama-30m with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="sapbot/toyllama-30m")# Load model directly from transformers import AutoTokenizer, AutoModelForMultimodalLM tokenizer = AutoTokenizer.from_pretrained("sapbot/toyllama-30m") model = AutoModelForMultimodalLM.from_pretrained("sapbot/toyllama-30m") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use sapbot/toyllama-30m with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "sapbot/toyllama-30m" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "sapbot/toyllama-30m", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/sapbot/toyllama-30m
- SGLang
How to use sapbot/toyllama-30m 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 "sapbot/toyllama-30m" \ --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": "sapbot/toyllama-30m", "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 "sapbot/toyllama-30m" \ --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": "sapbot/toyllama-30m", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use sapbot/toyllama-30m with Docker Model Runner:
docker model run hf.co/sapbot/toyllama-30m
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LICENSE
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README.md
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---
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license: cc0-1.0
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language:
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- en
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pipeline_tag: text-generation
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library_name: transformers
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tags:
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- llama
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---
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# ToyLlama 13M
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ToyLlama 13M is a tiniest possible (I think at least) size that can do something minimally realistic in terms of english knowledge. (though, it is at big max tokens stuck with repetitive loops.)
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## Example generations
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All were generated with `test.py` interactive CLI. (Usage: `python3 test.py`)
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```
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>>> Enter prompt (temp: 0.7): Hello
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------------------------------------------------------------
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Hello! I'm sorry, I can't believe you, but I don't know if you're a
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creative person, I'm just a little punk, but I'm sure I can do it. I'm
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not really interested in the way I'm really in the computer, and I'm sure
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you can't believe me, and I'm sure you're not in the computer system.
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I've never met my parents, I've had a chance to have a computer
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crime. I'm not sure why I'm a little bit younger than I'd ever been
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talking to. I'm really just a little bit surprised, but I don't
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think so. I don't
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------------------------------------------------------------
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>>> Enter prompt (temp: 0.7): The Art of Technology Digest is an open forum dedicated to sharing
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------------------------------------------------------------
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The Art of Technology Digest is an open forum dedicated to sharing
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information among computerists and to the presentation and debate of
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| 34 |
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diverse views. CuD material may be reprinted for non-profit as long
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| 35 |
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as the source is cited. Authors hold a presumptive copyright, and
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they should be contacted for reprint permission. It is assumed that
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non-personal mail to the moderators may be reprinted unless otherwise
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specified. Readers are encouraged to submit reasoned articles
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| 39 |
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relating to computer culture and communication. Articles are
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| 40 |
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preferred to short responses. Please avoid quoting previous posts
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unless absolutely necessary.
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| 42 |
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DISCLAIMER: The views represented herein do not necessarily represent
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------------------------------------------------------------
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```
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## Training information
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Trained for 1 hour on 130M training tokens using one RX 6600 (8GB VRAM).
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### Training data
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Data was from Gutenberg (just 6MB) and textfiles.com (BBS forums) (455MB).
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So, it has **NO** knowledge of anything other then english.
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