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T1 S10 Allam7b LoRA Prompt

3-class Arabic text classifier over (text, target) pairs, formulated as a prompted sequence-classification head on top of a LoRA-adapted instruction-tuned LLM. Access to the model weights requires manual approval...

Fuentehuggingface.co/HassanB4/t1_s10_allam7b_lora_prompt/tree/5ea932371e4634e1666167a29a08fb330bde0e02
TipoLoRAModelo basehumain-ai/ALLaM-7B-Instruct-previewVersiónFP32Bcalidad

Fuente del modelo

Tipo
LoRA
Tamaño del archivo
153 MB
Modelo base
humain-ai/ALLaM-7B-Instruct-preview
Versión
vFP32
Actualizado
31 oct 2026
Descargas
5
Fuente
Hugging Face

Descripción de la fuente

Arabic Text-Target Classifier (LoRA) — t1_s10_allam7b_lora_prompt

3-class Arabic text classifier over (text, target) pairs, formulated as a prompted sequence-classification head on top of a LoRA-adapted instruction-tuned LLM. Access to the model weights requires manual approval from the repository owner.

Base model

humain-ai/ALLaM-7B-Instruct-preview

Labels

  • LABEL_0
  • LABEL_1
  • LABEL_2

Usage

from transformers import AutoTokenizer, AutoModelForSequenceClassification
from peft import PeftModel
import torch

base_model_id = "humain-ai/ALLaM-7B-Instruct-preview"
adapter_id = "HassanB4/t1_s10_allam7b_lora_prompt"

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).logits

predicted_label = int(torch.argmax(logits, dim=-1)[0])

Fuentes

1 fuente
Hugging FacePrincipalVersión 5ea932371e4634e1666167a29a08fb330bde0e02
huggingface.co/HassanB4/t1_s10_allam7b_lora_prompt/tree/5ea932371e4634e1666167a29a08fb330bde0e02

Verificado 31 oct

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Repository files

File inventory only; completeness is unverified.

Artefactos del modelo

1 artefacto

adapter_model.safetensors

safetensors · FP32 (unverified) · 153 MB · SHA-256 b969247a72af…993e · Hugging Face

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Extractos de fuentes

3 extractos
Readme15 agohuggingface.co/HassanB4/t1_s10_allam7b_lora_prompt

--- language: ar license: mit library_name: peft base_model: humain-ai/ALLaM-7B-Instruct-preview tags: - arabic - text-classification - lora --- # Arabic Text-Target Classifier (LoRA) — t1_s10_allam7b_lora_prompt 3-class Arabic text classifier over (text, target) pairs, formulated as a prompted sequence-classification head on top of a LoRA-adapted instruction-tuned LLM. Access to the model weights requires manual approval from the repository owner. ## Base model `humain-ai/ALLaM-7B-Instruct-preview` ## Labels - `LABEL_0` - `LABEL_1` - `LABEL_2` ## Usage ```python from transformers import AutoTokenizer, AutoModelForSequenceClassification from peft import PeftModel import torch base_model_id = "humain-ai/ALLaM-7B-Instruct-preview" adapter_id = "HassanB4/t1_s10_allam7b_lora_prompt" 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).logits predicted_label = int(torch.argmax(logits, dim=-1)[0]) ```

Note31 octhuggingface.co/HassanB4/t1_s10_allam7b_lora_prompt

Source context: 5 downloads · 0 likes · Pipeline text-classification · Library peft · Repo HassanB4/t1_s10_allam7b_lora_prompt

Note15 agohuggingface.co/HassanB4/t1_s10_allam7b_lora_prompt

Source context: 7 downloads · 0 likes · Pipeline text-classification · Library peft · Repo HassanB4/t1_s10_allam7b_lora_prompt