Text Generation
Transformers
Safetensors
English
llama
small-language-model
tiny
on-device
from-scratch
reasoning
Eval Results (legacy)
text-generation-inference
Instructions to use MaliosDark/Isabel-50M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MaliosDark/Isabel-50M with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="MaliosDark/Isabel-50M")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("MaliosDark/Isabel-50M") model = AutoModelForCausalLM.from_pretrained("MaliosDark/Isabel-50M", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use MaliosDark/Isabel-50M with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "MaliosDark/Isabel-50M" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "MaliosDark/Isabel-50M", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/MaliosDark/Isabel-50M
- SGLang
How to use MaliosDark/Isabel-50M 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 "MaliosDark/Isabel-50M" \ --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": "MaliosDark/Isabel-50M", "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 "MaliosDark/Isabel-50M" \ --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": "MaliosDark/Isabel-50M", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use MaliosDark/Isabel-50M with Docker Model Runner:
docker model run hf.co/MaliosDark/Isabel-50M
Commit History
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Remove premature #1 claim
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Best model: digit-tokenizer, Avg 40.1, #1 in ~50M class
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Branding: Ideoa Labs only (remove legal entity)
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Branding: use Ideoa Labs only
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Branding: use Ideoa Labs only
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Add named ~50M class comparison table to card
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Sync paper to Avg ~36.7
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Update to stronger-base model (Avg ~36.7) + class comparison in card
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Add technical report (with ~50M class comparison)
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Honest correction: real full-test numbers (Avg ~36.1)
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Card: remove architecture model reference
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Update card: generic comparison, no em-dashes
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Initial upload: Isabel-50M (from-scratch tiny LM) — private draft
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initial commit
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