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  ---
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  library_name: transformers
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- tags: []
 
 
 
 
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  ---
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- # Model Card for Model ID
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- <!-- Provide a quick summary of what the model is/does. -->
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- ## Model Details
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- ### Model Description
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- <!-- Provide a longer summary of what this model is. -->
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- This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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- - **Developed by:** [More Information Needed]
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- - **Funded by [optional]:** [More Information Needed]
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- - **Shared by [optional]:** [More Information Needed]
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- - **Model type:** [More Information Needed]
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- - **Language(s) (NLP):** [More Information Needed]
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- - **License:** [More Information Needed]
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- - **Finetuned from model [optional]:** [More Information Needed]
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- ### Model Sources [optional]
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- <!-- Provide the basic links for the model. -->
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- - **Repository:** [More Information Needed]
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- - **Paper [optional]:** [More Information Needed]
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- - **Demo [optional]:** [More Information Needed]
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- ## Uses
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- <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
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- ### Direct Use
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- <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
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- [More Information Needed]
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- ### Downstream Use [optional]
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- <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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- [More Information Needed]
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- ### Out-of-Scope Use
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- <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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- [More Information Needed]
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- ## Bias, Risks, and Limitations
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- <!-- This section is meant to convey both technical and sociotechnical limitations. -->
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- [More Information Needed]
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- ### Recommendations
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- <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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- Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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- ## How to Get Started with the Model
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- Use the code below to get started with the model.
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- [More Information Needed]
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- ## Training Details
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- ### Training Data
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- <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
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- [More Information Needed]
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- ### Training Procedure
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- <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
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- #### Preprocessing [optional]
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- [More Information Needed]
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- #### Training Hyperparameters
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- - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
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- #### Speeds, Sizes, Times [optional]
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- <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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- [More Information Needed]
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- ## Evaluation
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- <!-- This section describes the evaluation protocols and provides the results. -->
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- ### Testing Data, Factors & Metrics
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- #### Testing Data
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- <!-- This should link to a Dataset Card if possible. -->
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- [More Information Needed]
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- #### Factors
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- <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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- [More Information Needed]
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- #### Metrics
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- <!-- These are the evaluation metrics being used, ideally with a description of why. -->
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- ### Results
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- #### Summary
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- ## Model Examination [optional]
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- <!-- Relevant interpretability work for the model goes here -->
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- [More Information Needed]
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- ## Environmental Impact
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- <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
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- Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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- - **Hardware Type:** [More Information Needed]
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- - **Hours used:** [More Information Needed]
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- - **Cloud Provider:** [More Information Needed]
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- - **Compute Region:** [More Information Needed]
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- - **Carbon Emitted:** [More Information Needed]
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- ## Technical Specifications [optional]
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- ### Model Architecture and Objective
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- ### Compute Infrastructure
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- #### Hardware
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- #### Software
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- ## Citation [optional]
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- <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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- **BibTeX:**
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- **APA:**
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- ## Glossary [optional]
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- <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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- [More Information Needed]
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- ## More Information [optional]
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- ## Model Card Authors [optional]
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- ## Model Card Contact
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- [More Information Needed]
 
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  ---
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  library_name: transformers
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+ license: apache-2.0
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+ base_model:
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+ - rhymes-ai/Aria-sequential_mlp
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+ - rhymes-ai/Aria
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+ pipeline_tag: image-text-to-text
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  ---
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+ # Aria-sequential_mlp-bnb_nf4
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+ 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.
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+ 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.
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+
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+ Run this model with:
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+ ``` python
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+ import requests
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+ import torch
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+ from PIL import Image
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+ from transformers import AutoModelForCausalLM, AutoProcessor
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+ torch.cuda.set_device(0)
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+
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+ model_id_or_path = "thwin27/Aria-sequential_mlp-bnb_nf4"
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+
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+ model = AutoModelForCausalLM.from_pretrained(model_id_or_path, device_map="auto", torch_dtype=torch.bfloat16, trust_remote_code=True)
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+ processor = AutoProcessor.from_pretrained(model_id_or_path, trust_remote_code=True)
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+
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+ image_path = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/cat.png"
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+
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+ image = Image.open(requests.get(image_path, stream=True).raw)
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+
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+ messages = [
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+ {
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+ "role": "user",
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+ "content": [
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+ {"text": None, "type": "image"},
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+ {"text": "what is the image?", "type": "text"},
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+ ],
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+ }
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+ ]
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+
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+ text = processor.apply_chat_template(messages, add_generation_prompt=True)
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+ inputs = processor(text=text, images=image, return_tensors="pt")
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+ inputs["pixel_values"] = inputs["pixel_values"].to(model.dtype)
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+ inputs = {k: v.to(model.device) for k, v in inputs.items()}
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+
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+ with torch.inference_mode(), torch.cuda.amp.autocast(dtype=torch.bfloat16):
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+ output = model.generate(
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+ **inputs,
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+ max_new_tokens=500,
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+ stop_strings=["<|im_end|>"],
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+ tokenizer=processor.tokenizer,
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+ do_sample=True,
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+ temperature=0.9,
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+ )
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+ output_ids = output[0][inputs["input_ids"].shape[1]:]
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+ result = processor.decode(output_ids, skip_special_tokens=True)
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+
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+ print(result)
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+ print(f'Max allocated memory: {torch.cuda.max_memory_allocated(device="cuda") / 1024 ** 3:.3f}GiB')
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+ ```
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+ Quantization created with:
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+ ``` python
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+ from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
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+ model_id = "rhymes-ai/Aria-sequential_mlp"
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+ nf4_config = BitsAndBytesConfig(
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+ load_in_4bit=True,
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+ bnb_4bit_quant_type="nf4",
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+ bnb_4bit_compute_dtype=torch.bfloat16,
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+ bnb_4bit_use_double_quant=True,
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+ llm_int8_enable_fp32_cpu_offload=True,
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+ llm_int8_skip_modules=["language_model.lm_head", "multi_modal_projector", "vision_tower"],
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+ )
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+
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+ model_nf4 = AutoModelForCausalLM.from_pretrained(model_id, quantization_config=nf4_config)
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+ ```