Text Generation
Transformers
Safetensors
English
llama
my-little-pony
mlp
fanfiction
small-language-model
from-scratch
lissa
roleplay
rp
character
edge-deployment
text-generation-inference
Instructions to use CastIronMind/LissaNano-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use CastIronMind/LissaNano-Instruct with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="CastIronMind/LissaNano-Instruct")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("CastIronMind/LissaNano-Instruct") model = AutoModelForCausalLM.from_pretrained("CastIronMind/LissaNano-Instruct", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use CastIronMind/LissaNano-Instruct with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "CastIronMind/LissaNano-Instruct" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "CastIronMind/LissaNano-Instruct", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/CastIronMind/LissaNano-Instruct
- SGLang
How to use CastIronMind/LissaNano-Instruct 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 "CastIronMind/LissaNano-Instruct" \ --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": "CastIronMind/LissaNano-Instruct", "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 "CastIronMind/LissaNano-Instruct" \ --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": "CastIronMind/LissaNano-Instruct", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use CastIronMind/LissaNano-Instruct with Docker Model Runner:
docker model run hf.co/CastIronMind/LissaNano-Instruct
Commit History
2 days ago
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link browser demo
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link GGUF quantizations (LissaNano-Instruct-GGUF)
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Update README.md
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Full release: model, tokenizer, logo, English card
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Rewrite model card in English
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Initial release: model, tokenizer, card
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