Domain-adapted base language model for Middle East geopolitics. Intended for researchers and developers building RAG pipelines, domain-specific text generation, or further fine-tuning on this domain.

Not intended for: direct user-facing applications without additional instruction fine-tuning, high-stakes decision making, or tasks requiring up-to-date information (training data collected up to early 2025).

Training Data

Corpus: Nyries/racine-ai-middle-east

7 016 training documents / 780 held-out (split seed=42, no overlap verified)
Sources: RAND Corporation (311), Brookings Institution (5 009), Wilson Center (1 138), arXiv (55), Wikipedia thematic (1 339 articles, 43 geopolitical categories)
Size: ~58 MB train / ~6.5 MB held-out — ~11.7M tokens total
Language: English (99%+)
License: all sources scraped under permissive or fair-use terms (RAND, Brookings, Wilson Center publish open-access policy reports; arXiv CC-BY; Wikipedia CC-BY-SA)

Cleaning pipeline: exact deduplication → MinHash LSH (Jaccard ≥ 0.85, removed 20 docs) → language filter (removed 29 non-English) → repetition filter (removed 7) → boilerplate removal → basic PII suppression (email, phone regex).

Training Configuration

Parameter	Value
Framework	transformers 4.51+ + accelerate
Base model	Qwen/Qwen3.5-0.8B-Base
Precision	bf16
Epochs	1
Effective batch size	32 sequences (per_device=2 × grad_accum=16)
Max sequence length	1 024 tokens
Learning rate	1e-4 (cosine scheduler)
Warmup steps	30
Weight decay	0.01
Gradient checkpointing	yes
Tokens seen	~7.2M (7 016 docs × ~1 024 tokens)
Compute	OVH AI Training — A100 32 GB
Training time	~2h30 (220 optimizer steps)
Seed	42

Evaluation

Comparison against base Qwen/Qwen3.5-0.8B-Base on held-out sets:

Metric	Base Qwen3.5-0.8B	This model	Delta
Perplexity held-out (200 docs)	16.083	14.115	-1.97 (-12.2%)
Domain QA accuracy (50 q, MMLU-style)	64.0%	60.0%	-4.0%
Hellaswag-200 accuracy	44.5%	42.0%	-2.5%

Reproduce:

bash

git clone https://github.com/Nyries/racine-ai-middle-east-cpt
cd cpt
python eval_perplexity.py --finetuned ./checkpoints/checkpoint-220
python eval_qa.py --finetuned ./checkpoints/checkpoint-220
python eval_hellaswag.py --finetuned ./checkpoints/checkpoint-220

Limitations

1 epoch only: the model has seen each training token once. Additional epochs or a larger corpus would likely yield stronger domain adaptation.
Catastrophic forgetting: minor regressions on Domain QA (-4%) and Hellaswag (-2.5%) are consistent with CPT without generalist replay. The model is not instruction-tuned — use a chat/instruct variant for conversational tasks.
Sequence length: documents are truncated at 1 024 tokens. Long policy reports lose their tail content.
No instruction tuning: this is a base model. It will not follow instructions or answer questions in a structured way without further SFT.
Knowledge cutoff: corpus collected up to early 2025. Events after that date are not represented.

Identified Biases

Source bias: corpus skews toward Western think tanks (Brookings, RAND, Wilson Center). Perspectives from regional actors (Arab governments, Iranian sources, Turkish media) are underrepresented.
English-only: despite the multilingual nature of the region, the corpus is almost exclusively in English, which may bias the model toward Western framings of regional events.
Topic imbalance: Brookings contributes 64% of documents, which may overweight US foreign policy analysis relative to regional dynamics.

Citation

bibtex

@misc{nyries2025qwen3middleeast,
  title={Qwen3.5-0.8B continuous pre-training on Middle East geopolitics},
  author={Nyries},
  year={2025},
  url={https://huggingface.co/Nyries/qwen3-0.8b-middle-east-cpt}
}