---
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'"/>

### Model Card Contact

For errors or additional questions about details in this model card, contact: contact@traversaal.ai