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Alfa Code - Multimodal Coding GGUF

RTX 3050 4GB: use tiny-50M demo. 1B+ needs cloud 8xH100.

1. Data

python scripts/download_data.py --dataset code_small_test --max-rows 500 Output: data/processed/train.jsonl {modality,prompt,think,answer}

2. Tokenizer + Train

python scripts/train_tokenizer.py -> tokenizers/alfa-32k.json (verified 47KB) pip install torch --index-url https://download.pytorch.org/whl/cu121 python scripts/train.py -> outputs/tiny-50M

3. GGUF

git clone https://github.com/ggerganov/llama.cpp third_party/llama.cpp python scripts/convert_to_gguf.py --ckpt outputs/tiny-50M --quant Q4_K_M --out outputs/tiny-50M.gguf

4. UI (reasoning live->collapsed + image/file/video)

python app/ui.py -> http://127.0.0.1:7860

5. Hub sync

hf upload mrsandip/Alfa-Code . . --exclude __pycache__ --exclude .venv --commit-message "Update"

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Alfa-Code