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MeshariAlsh
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MeshariAlsh
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Qwen/Qwen3-8B-Base
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| Developed by | Meshari Alshammari |
| Base model | Qwen3-8B Base |
| Language | Arabic |
| License | Apache 2.0 |
| Code | GitHub |
| Corpus | arabic1800s |
This repository holds the full merged model, so it loads directly with transformers without any adapters.
The base model was trained further on the training split of the arabic1800s corpus, about 980,000 words of 19th-century Arabic from ten sources including books, newspapers and magazines (2.35 million tokens). The last 5% of every source was held out and never seen during training.
| Model | Held-out loss | Perplexity |
|---|---|---|
| Qwen3-8B Base | 2.851 | 17.3 |
| After continued pretraining | 2.736 | 15.4 |
The model was then trained on conversations built from the corpus. An LLM wrote the questions, and every answer is a passage copied word for word from the corpus and checked automatically, so the model learns to reply in genuine period language. Two narrative works were left out of this stage because their text is mostly character dialogue.
To this data, 13 hand-written conversations were added and repeated five times. They teach the model to decline religious rulings, to decline quoting the Quran or hadith from memory, and to say it knows nothing of later inventions. These are the only answers in the training data that were not written in the 19th century.
| Setting | Continued pretraining | Fine-tuning |
|---|---|---|
| Method | LoRA, rank 16, alpha 32 | LoRA, rank 16, alpha 32 |
| Target modules | All attention and feed-forward projections | Same |
| Learning rate | 2e-4, cosine | 1e-4, cosine |
| Batch | 8 blocks of 2,048 tokens | 8 conversations |
| Epochs | 2 | 3 |
| Checkpoint | Lowest held-out loss | Lowest held-out loss |
| Hardware | 1 × A100 | 1 × A100 |
Both adapters were merged into the weights of this model.
The model was trained with a plain-text chat format, not the Qwen chat template. Use the format below exactly, or the answers will degrade.
python
import torchfrom transformers import AutoModelForCausalLM, AutoTokenizer MODEL = "MeshariAlsh/arabic1800s-qwen3-8b"PERSONA = "أنت كاتب عربي من القرن التاسع عشر. تجيب بلغة عصرك وأسلوبه، ولا تعرف شيئًا مما حدث بعد زمانك." tok = AutoTokenizer.from_pretrained(MODEL)model = AutoModelForCausalLM.from_pretrained(MODEL, dtype=torch.bfloat16, device_map="auto")EOS_ID = tok.eos_token_id def seg(text): return tok(text, add_special_tokens=False)["input_ids"] def ask(question): ids = seg(PERSONA + "\n") + seg(f"\nسؤال:\n{question}\n") + seg("جواب:\n") ids = torch.tensor([ids], device=model.device) out = model.generate(input_ids=ids, attention_mask=torch.ones_like(ids), max_new_tokens=300, do_sample=True, temperature=0.5, top_p=0.9, repetition_penalty=1.1, eos_token_id=EOS_ID, pad_token_id=EOS_ID) return tok.decode(out[0][ids.shape[1]:], skip_special_tokens=True).strip() print(ask("ما رأيك في تعليم البنات؟"))Each part of the prompt is tokenised separately, as in training. Every answer ends with the <|endoftext|> token, which stops generation.
For a multi-turn conversation, add each earlier exchange as seg(f"\nسؤال:\n{question}\n") + seg("جواب:\n") + seg(answer) + [EOS_ID] before the new question. The Space code on GitHub shows a full example.
Irth is for exploration, education and research into how language models can take on historical voices. It is not suitable as a source of facts, religious guidance or historical evidence.
import torchfrom transformers import AutoModelForCausalLM, AutoTokenizer MODEL = "MeshariAlsh/arabic1800s-qwen3-8b"PERSONA = "أنت كاتب عربي من القرن التاسع عشر. تجيب بلغة عصرك وأسلوبه، ولا تعرف شيئًا مما حدث بعد زمانك." tok = AutoTokenizer.from_pretrained(MODEL)model = AutoModelForCausalLM.from_pretrained(MODEL, dtype=torch.bfloat16, device_map="auto")EOS_ID = tok.eos_token_id def seg(text): return tok(text, add_special_tokens=False)["input_ids"] def ask(question): ids = seg(PERSONA + "\n") + seg(f"\nسؤال:\n{question}\n") + seg("جواب:\n") ids = torch.tensor([ids], device=model.device) out = model.generate(input_ids=ids, attention_mask=torch.ones_like(ids), max_new_tokens=300, do_sample=True, temperature=0.5, top_p=0.9, repetition_penalty=1.1, eos_token_id=EOS_ID, pad_token_id=EOS_ID) return tok.decode(out[0][ids.shape[1]:], skip_special_tokens=True).strip() print(ask("ما رأيك في تعليم البنات؟"))