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Model: large-traversaal/Alif-1.0-8B-Instruct
Source: Original Platform

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README.md

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+---
+base_model: unsloth/Meta-Llama-3.1-8B
+tags:
+- text-generation-inference
+- transformers
+- unsloth
+- llama
+- trl
+license: apache-2.0
+language:
+- en
+- ur
+---
+
+
+# Model Card for Alif 1.0 8B Instruct
+
+**Alif 1.0 8B Instruct** is an open-source model with highly advanced multilingual reasoning capabilities. It utilizes human refined multilingual synthetic data paired with reasoning to enhance cultural nuance and reasoning capabilities in english and urdu languages.
+
+- **Developed by:** large-traversaal
+- **License:** apache-2.0
+- **Base model:** unsloth/Meta-Llama-3.1-8B
+- **Model:** Alif-1.0-8B-Instruct
+- **Model Size:** 8 billion parameters
+
+This model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
+
+
+### How to Use Alif 1.0 8B Instruct
+
+Install the transformers, bitsandbytes libraries and load Alif 1.0 8B Instruct as follows:
+
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
+import torch
+from transformers import BitsAndBytesConfig
+
+model_id = "large-traversaal/Alif-1.0-8B-Instruct"
+
+# 4-bit quantization configuration
+quantization_config = BitsAndBytesConfig(
+    load_in_4bit=True,
+    bnb_4bit_compute_dtype=torch.float16,
+    bnb_4bit_use_double_quant=True,
+    bnb_4bit_quant_type="nf4"
+)
+
+# Load tokenizer and model in 4-bit
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    quantization_config=quantization_config,
+    device_map="auto"
+)
+
+# Create text generation pipeline
+chatbot = pipeline("text-generation", model=model, tokenizer=tokenizer, device_map="auto")
+
+# Function to chat
+def chat(message):
+    response = chatbot(message, max_new_tokens=100, do_sample=True, temperature=0.3)
+    return response[0]["generated_text"]
+
+# Example chat
+user_input = "شہر کراچی کی کیا اہمیت ہے؟"
+bot_response = chat(user_input)
+
+print(bot_response)
+
+```
+
+You can also try out this model using [TextStreamer](https://colab.research.google.com/drive/1mEPynC__uN2tKDvDho3f6MpcKW-GMiAh?usp=sharing) or [Gradio](https://colab.research.google.com/drive/1DUwlYBOMUd7FZaI631-y6y8fTNiy0pqt?usp=sharing) in Colab. It is also available in GGUF with various quantized formats for Ollama, LM Studio, Jan, and Llama.cpp.
+
+
+## Model Details
+
+**Input**: Models input text only.
+
+**Output**: Models generate text only.
+
+**Model Architecture**: Alif 1.0 8B Instruct is an auto-regressive language model that uses an optimized transformer architecture. Post-training includes continuous pretraining and supervised finetuning.
+
+For more details about how the model was trained, check out [our blogpost](https://blog.traversaal.ai/announcing-alif-1-0-our-first-urdu-llm-outperforming-other-open-source-llms/).
+
+
+### Evaluation
+We evaluated Alif 1.0 8B Instruct against Gemma 2 9B, Llama 3.1 8B, Mistral Nemo 12B, Qwen 2.5 7B and Cohere Aya Expanse 8B using the human annotated Urdu evaluation dataset and scores are determined using gpt-4o as a judge.
+
+<img src="result1.jpg" width="650" style="margin-left:'auto' margin-right:'auto' display:'block'"/>
+
+<img src="result2.jpg" width="650" style="margin-left:'auto' margin-right:'auto' display:'block'"/>
+
+### Citation
+```bash
+@article{ShafiqueAlif2025,
+  title        = {Alif: Advancing Urdu Large Language Models via Multilingual Synthetic Data Distillation},
+  author       = {Muhammad Ali Shafique and Kanwal Mehreen and Muhammad Arham and Maaz Amjad and Sabur Butt and Hamza Farooq},
+  journal      = {arXiv preprint arXiv:2510.09051},
+  year         = {2025},
+  url          = {https://arxiv.org/abs/2510.09051}
+}
+```
+
+### Model Card Contact
+
+For errors or additional questions about details in this model card, contact: contact@traversaal.ai
+

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+  "use_rslora": true
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