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amirhosseinjpl
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Model Details
Model Provider
amirhosseinjpl
Model Tree
Qwen/Qwen2.5-7B-Instruct
Input Modalities
Output Modalities
Supported Functionality
python
import torchfrom transformers import AutoModelForCausalLM, AutoTokenizerrepo = "amirhosseinjpl/jplstar"tok = AutoTokenizer.from_pretrained(repo)model = AutoModelForCausalLM.from_pretrained(repo, torch_dtype=torch.bfloat16, device_map="auto")messages = [{"role": "system","content": "Translate the following Standard Persian sentence into Abizi dialect.",},{"role": "user", "content": "دیشب رفتم مغازه."},]prompt = tok.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)inputs = tok(prompt, return_tensors="pt").to(model.device)out = model.generate(**inputs,max_new_tokens=128,do_sample=False,pad_token_id=tok.eos_token_id,)print(tok.decode(out[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))
Greedy decoding is what produced the numbers below. Sampling is for the demo Space only.
Held-out rows were isolated from train-Final. No reverse-pair cut from the same sentence into eval.
| split | n | file |
|---|---|---|
| train | 10,207 | train-Final.jsonl |
| valid | 30 | valid-Final.jsonl |
| test | 50 | test_data_for_v4 |
Metric: character overlap (F1 over character bags after yeh/kaf normalize). Exact match is the wrong headline: Abizi spelling is not stable.
| split | exact | overlap |
|---|---|---|
| valid | 0/30 | 81.5% |
| test | 0/50 | 85.8% |
Valid is a 30-row file. Do not cite a 50-row file as valid. Test > valid on this metric is compatible with split difficulty and small-n noise. It is not proof of generalization. Human gold-vs-pred is the judge. Overlap is a proxy. Not BLEU.
Hyperparameters live in the training notebook, not in this card.
amirhosseinjpl/telisk (loads this model)Apache-2.0 for the fine-tune artifacts, subject to the Qwen2.5-Instruct license of the base.