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khursani8/synthpre-557m
synthpre-557m is a machine learning model from khursani8. Use it for the machine learning task on the model card, and read the license before you ship it in a product. The card lists the license as apache-2.0.
Base language model for Bahasa Melayu (Malaysian Malay). Dense Llama-style transformer, 557.3M parameters, RoPE/SwiGLU/RMSNorm, tied embeddings, 1024-token context, Qwen2.5 tokenizer (151,936 vocab).
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From the Hugging Face model README
Base language model for Bahasa Melayu (Malaysian Malay). Dense Llama-style transformer, 557.3M parameters, RoPE/SwiGLU/RMSNorm, tied embeddings, 1024-token context, Qwen2.5 tokenizer (151,936 vocab).
This is a mid-training checkpoint of an in-progress run, published for internal use:
All training data was collected and curated by us — an in-house Malaysian-web crawl and manual curation pipeline, plus hand-reviewed government/hansard/legal documents. We do not use any third-party text collections. 13.88B tokens, 10.85M documents. Pipeline: manual review calibration (182 hand-labeled documents for language identification), Indonesian contamination removal (~20% of raw), quality filtering, exact + MinHash deduplication (within and across pulls), domain-disjoint train/eval/heldout splits. No synthetic data (measured neutral at scale — see repo report).
Base model for: Malay language understanding, continued pretraining, instruction/tool-calling fine-tuning (BFCL-JSON format), Malaysian-domain applications. Not instruction-tuned; expect base-model behavior (completion, not conversation). English capability is incidental only — the training mix is effectively 100% Malay.
Val loss 2.3658 on heldout Malay web text (domain-disjoint). Zero-shot bfcl-ms (Malay function-calling) baseline pending; instruct/tool SFT phases planned on top of this base.