---
license: apache-2.0
---

AWQ Quantized

```
!pip install  git+https://github.com/huggingface/transformers.git -q
!pip install huggingface_hub
!pip install autoawq -q
```

```
from awq import AutoAWQForCausalLM
from transformers import AutoTokenizer
import torch

# Assuming your model and tokenizer are loaded
model_name_or_path = "arlineka/manbasya_2x7b_MOE"
model = AutoAWQForCausalLM.from_quantized(model_name_or_path, fuse_layer=True, trust_remote_code=False, safetensors=True)
tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, trust_remote_code=False)

# Set device to CUDA if available
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

# Move model to the device
model.to(device)

# Prepare your input text and move input tensors to the same device
input_text = "Hello. Input Here"
input_ids = tokenizer.encode(input_text, return_tensors="pt").to(device)

# Now generate text with model and input tensors on the same device
output = model.generate(input_ids, max_new_tokens=2048)  # Example usage, adjust as necessary
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)

print(generated_text)
```