Text Generation
Safetensors
GGUF
Italian
physisml
curriculum-learning
from-scratch
italian
research
experimental
Instructions to use speleoalex/physisml-it-preview with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use speleoalex/physisml-it-preview with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf speleoalex/physisml-it-preview # Run inference directly in the terminal: llama cli -hf speleoalex/physisml-it-preview
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf speleoalex/physisml-it-preview # Run inference directly in the terminal: llama cli -hf speleoalex/physisml-it-preview
Build from source code
git clone https://github.com/ggml-org/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-app # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama serve -hf speleoalex/physisml-it-preview # Run inference directly in the terminal: ./build/bin/llama cli -hf speleoalex/physisml-it-preview
Use Docker
docker model run hf.co/speleoalex/physisml-it-preview
- Llama new
- LM Studio
- Jan
- vLLM
How to use speleoalex/physisml-it-preview with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "speleoalex/physisml-it-preview" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "speleoalex/physisml-it-preview", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/speleoalex/physisml-it-preview
- Ollama
How to use speleoalex/physisml-it-preview with Ollama:
ollama run hf.co/speleoalex/physisml-it-preview
- Unsloth Desktop
- Docker Model Runner
How to use speleoalex/physisml-it-preview with Docker Model Runner:
docker model run hf.co/speleoalex/physisml-it-preview
- Lemonade
How to use speleoalex/physisml-it-preview with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull speleoalex/physisml-it-preview
Run and chat with the model
lemonade run user.physisml-it-preview-{{QUANT_TAG}}List all available models
lemonade list
- Atomic Chat
File size: 1,314 Bytes
6470b22 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 | # PhysisML in ollama.
#
# python3 scripts/export_gguf.py \
# --checkpoint models/checkpoints/it/level_12/final_dreamed.pt \
# --tokenizer models/checkpoints/it/level_12/tokenizer.json \
# --output models/physisml.gguf
# ollama create physisml -f Modelfile && ollama run physisml
#
# TEMPLATE is the bare prompt: the model was taught single-turn question/answer
# lines, so any chat wrapper is out of distribution. Greedy because the margin
# is thin and the published scores are measured that way.
#
# No stop strings: the model emits <|EOS|> on its own since the corpus started
# carrying it (P(EOS) after a finished answer went from 0.00004 to 0.85-1.00).
# It used to need `PARAMETER stop . ! ?` , which also ate the final punctuation.
#
# `ollama run` INTERACTIVELY sends the accumulated conversation back as context.
# With a 128-token window and no multi-turn training, three exchanges are enough
# to crowd out the question: 'cosa fa la mamma?' answers 'la mamma mangia' alone
# and 'il cane' with the previous turns prepended. Use /clear, one-shot
# `ollama run physisml "..."`, or the API — all three are stateless.
FROM ./physisml.gguf
TEMPLATE {{ .Prompt }}
PARAMETER temperature 0
PARAMETER top_k 1
PARAMETER num_predict 48
PARAMETER num_ctx 128
PARAMETER repeat_penalty 1
|