Instructions to use typer-org/booper-story-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use typer-org/booper-story-v2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="typer-org/booper-story-v2")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("typer-org/booper-story-v2") model = AutoModelForCausalLM.from_pretrained("typer-org/booper-story-v2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use typer-org/booper-story-v2 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "typer-org/booper-story-v2" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "typer-org/booper-story-v2", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/typer-org/booper-story-v2
- SGLang
How to use typer-org/booper-story-v2 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 "typer-org/booper-story-v2" \ --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": "typer-org/booper-story-v2", "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 "typer-org/booper-story-v2" \ --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": "typer-org/booper-story-v2", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use typer-org/booper-story-v2 with Docker Model Runner:
docker model run hf.co/typer-org/booper-story-v2
booper-story-v2
Long-form SFT of ProCreations/Booper-Big-Chat-INT8 (Mixtral MoE, 150M total / ~50M active, vocab 16384) so booper answers
story/long-answer requests instead of one-liners. Trained with sft/sft_longform.py from
https://github.com/frgmt0/babble in the pair layout <bos> prompt <sep> response <eos> (loss on the response).
Data mix
- tinystories: 32000 examples
- writingprompts: 40000 examples
- no_robots: 16000 examples
- smoltalk: 16000 examples
- discord: 56000 examples
Training
- steps: 1220, tokens: 37,022,555
- val loss: 2.0340 -> 1.7995
- device: mps, lr 4e-05, seq len 1024
Samples (temperature 0.8)
write me a short story about a dragon who is afraid of fire
Once upon a time, there was a dragon who lived in the forest. He lived in a small and strong forest, where he had to be careful of trees. One day, the dragon heard a noise from his neighbors. He told the dragon that he wanted to play with his friends, but it was too late.
write a short story about a detective in a city that never sleeps
Once upon a time there was a very strong detective. It would be a very good night until it was time to go back to sleep. One day, the detective had been up for the night. He was getting ready to go. As he was looking around, he saw a big room in his right hand. The detective
hey booper whats up
i gotta go on a walk
Loading
Same INT8 layout as the base: load_int8.py in this repo, or babble.hfserve with BABBLE_HF_MODEL_DIR pointed at a snapshot.
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Model tree for typer-org/booper-story-v2
Base model
ProCreations/Booper-Big