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HassanB4
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Model Details
Model Provider
HassanB4
Model Tree
humain-ai/ALLaM-7B-Instruct-preview
Input Modalities
Output Modalities
Supported Functionality
Trained and evaluated on targets that appear in the training data: COVID-19 Vaccine, Digital Transformation, Women Empowerment. Train 3,502 / Dev 619 tweets (Mawqif-v2).
humain-ai/ALLaM-7B-Instruct-preview
| ID | Label | Meaning |
|---|---|---|
| 0 | Favor | Tweet supports the target |
| 1 | Against | Tweet opposes the target |
| 2 | None | Neutral, irrelevant, or unclear stance |
No held-out dev score for this checkpoint, by design: this is a final refit of the
t1_s10_allam7b_lora_prompt recipe,
retrained on the combined train+dev data immediately before the official test submission (dev
Favg2 for the pre-refit checkpoint: 86.06). Trading the held-out check for more training data
is standard practice right before a competition test submission.
python
from transformers import AutoTokenizer, AutoModelForSequenceClassificationfrom peft import PeftModelimport torchbase_model_id = "humain-ai/ALLaM-7B-Instruct-preview"adapter_id = "HassanB4/t1_s11_allam7b_lora_prompt_final"id2label = {0: "Favor", 1: "Against", 2: "None"}tokenizer = AutoTokenizer.from_pretrained(adapter_id)base_model = AutoModelForSequenceClassification.from_pretrained(base_model_id, num_labels=3)model = PeftModel.from_pretrained(base_model, adapter_id)model.eval()text = "..."target = "..."prompt = f"{text} [SEP] {target}"inputs = tokenizer(prompt, return_tensors="pt", truncation=True, max_length=512)with torch.no_grad():logits = model(**inputs).logitspredicted_label = id2label[int(torch.argmax(logits, dim=-1)[0])]print(predicted_label)
Part of the NAMAA Community StanceEval-2026 submission (Track 1). A system description paper is in preparation; citation details will be added once available.