thwin27
/

Aria-sequential_mlp-bnb_nf4

@@ -1,199 +1,78 @@
 ---
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 ---
 library_name: transformers
+license: apache-2.0
+base_model:
+- rhymes-ai/Aria-sequential_mlp
+- rhymes-ai/Aria
+pipeline_tag: image-text-to-text
 ---
+# Aria-sequential_mlp-bnb_nf4
+BitsAndBytes NF4 quantization from [Aria-sequential_mlp](https://huggingface.co/rhymes-ai/Aria-sequential_mlp), requires about 13.8 GB of VRAM and works on a RTX 3090.
+Currently the model is not 5 GB sharded, as this seems to cause [problems](https://stackoverflow.com/questions/79068298/valueerror-supplied-state-dict-for-layers-does-not-contain-bitsandbytes-an) when loading serialized BNB models. This might make it impossible to load the model in free-tier Colab.
+Run this model with:
+``` python
+import requests
+import torch
+from PIL import Image
+from transformers import AutoModelForCausalLM, AutoProcessor
+torch.cuda.set_device(0)
+model_id_or_path = "thwin27/Aria-sequential_mlp-bnb_nf4"
+model = AutoModelForCausalLM.from_pretrained(model_id_or_path, device_map="auto", torch_dtype=torch.bfloat16, trust_remote_code=True)
+processor = AutoProcessor.from_pretrained(model_id_or_path, trust_remote_code=True)
+image_path = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/cat.png"
+image = Image.open(requests.get(image_path, stream=True).raw)
+messages = [
+    {
+        "role": "user",
+        "content": [
+            {"text": None, "type": "image"},
+            {"text": "what is the image?", "type": "text"},
+        ],
+    }
+]
+text = processor.apply_chat_template(messages, add_generation_prompt=True)
+inputs = processor(text=text, images=image, return_tensors="pt")
+inputs["pixel_values"] = inputs["pixel_values"].to(model.dtype)
+inputs = {k: v.to(model.device) for k, v in inputs.items()}
+with torch.inference_mode(), torch.cuda.amp.autocast(dtype=torch.bfloat16):
+    output = model.generate(
+        **inputs,
+        max_new_tokens=500,
+        stop_strings=["<|im_end|>"],
+        tokenizer=processor.tokenizer,
+        do_sample=True,
+        temperature=0.9,
+    )
+    output_ids = output[0][inputs["input_ids"].shape[1]:]
+    result = processor.decode(output_ids, skip_special_tokens=True)
+print(result)
+print(f'Max allocated memory: {torch.cuda.max_memory_allocated(device="cuda") / 1024 ** 3:.3f}GiB')
+```
+Quantization created with:
+``` python
+from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
+model_id = "rhymes-ai/Aria-sequential_mlp"
+nf4_config = BitsAndBytesConfig(
+    load_in_4bit=True,
+    bnb_4bit_quant_type="nf4",
+    bnb_4bit_compute_dtype=torch.bfloat16,
+    bnb_4bit_use_double_quant=True,
+    llm_int8_enable_fp32_cpu_offload=True,
+    llm_int8_skip_modules=["language_model.lm_head", "multi_modal_projector", "vision_tower"],
+    )
+model_nf4 = AutoModelForCausalLM.from_pretrained(model_id, quantization_config=nf4_config)
+```