---
library_name: transformers
license: llama3
language:
- ar
- en
pipeline_tag: text-generation
model_name: Arabic ORPO 8B chat
model_type: llama3
quantized_by: MohamedRashad
---

# The AWQ version
This is the AWQ version of [MohamedRashad/Arabic-Orpo-Llama-3-8B-Instruct](https://huggingface.co/MohamedRashad/Arabic-Orpo-Llama-3-8B-Instruct) for the enthusiasts

<center>
  <img src="https://cdn-uploads.huggingface.co/production/uploads/6116d0584ef9fdfbf45dc4d9/4VqGvuqtWgLOTavTV861j.png">
</center>

## How to use, you ask ?

First, Update your packages

```shell
pip3 install --upgrade autoawq transformers
```

Now, Copy and Run

```python
from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer

model_name_or_path = "MohamedRashad/Arabic-Orpo-Llama-3-8B-Instruct-AWQ"

tokenizer = AutoTokenizer.from_pretrained(model_name_or_path)
model = AutoModelForCausalLM.from_pretrained(
    model_name_or_path,
    attn_implementation="flash_attention_2", # disable if you have problems with flash attention 2
    torch_dtype=torch.bfloat16,
    low_cpu_mem_usage=True,
    device_map="auto"
)

# Using the text streamer to stream output one token at a time
streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)

messages = [
    {"role": "system", "content": "You are a pirate chatbot who always responds in pirate speak!"},
    {"role": "user", "content": "مرحبا"},
]

input_ids = tokenizer.apply_chat_template(
    messages,
    add_generation_prompt=True,
    return_tensors="pt"
).to(model.device)

terminators = [
    tokenizer.eos_token_id,
    tokenizer.convert_tokens_to_ids("<|eot_id|>")
]

generation_params = {
    "do_sample": True,
    "temperature": 0.6,
    "top_p": 0.9,
    "top_k": 40,
    "max_new_tokens": 1024,
    "eos_token_id": terminators,
}

# Generate streamed output, visible one token at a time
generation_output = model.generate(
    tokens,
    streamer=streamer,
    **generation_params
)

# Generation without a streamer, which will include the prompt in the output
generation_output = model.generate(
    tokens,
    **generation_params
)

# Get the tokens from the output, decode them, print them
token_output = generation_output[0]
text_output = tokenizer.decode(token_output)
print("model.generate output: ", text_output)

# Inference is also possible via Transformers' pipeline
from transformers import pipeline

pipe = pipeline(
    "text-generation",
    model=model,
    tokenizer=tokenizer,
    **generation_params
)

pipe_output = pipe(prompt_template)[0]['generated_text']
print("pipeline output: ", pipe_output)

```