Create README.md

README.md

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+---
+datasets:
+- NeelNanda/pile-10k
+---
+
+## Model Details
+
+This model is an int4 model with group_size 128 and symmetric quantization of [fancyfeast/llama-joycaption-alpha-two-hf-llava](https://huggingface.co/fancyfeast/llama-joycaption-alpha-two-hf-llava) generated by [intel/auto-round](https://github.com/intel/auto-round). Load the model with revision="" to use AutoGPTQ format.
+
+## How To Use
+
+### Requirements
+Please see the [Github](https://github.com/fpgaminer/joycaption) for more details.
+
+### INT4 Inference
+```python
+from auto_round import AutoRoundConfig ## must import for auto-round format
+import requests
+import torch
+from PIL import Image
+from transformers import AutoProcessor, LlavaForConditionalGeneration
+
+
+quantized_model_path="OPEA/llama-joycaption-alpha-two-hf-llava-int4-sym-inc"
+
+# Load JoyCaption INT4 Model
+processor = AutoProcessor.from_pretrained(quantized_model_path)
+llava_model = LlavaForConditionalGeneration.from_pretrained(quantized_model_path, device_map=0)
+llava_model.eval()
+
+image_url = "http://images.cocodataset.org/train2017/000000116003.jpg"
+content = "Write a descriptive caption for this image in a formal tone."
+
+# Preparation for inference
+with torch.no_grad():
+	image = Image.open(requests.get(image_url, stream=True).raw)
+	messages = [
+		{
+			"role": "system",
+			"content": "You are a helpful image captioner.",
+		},
+		{
+			"role": "user",
+			"content": content,
+		},
+	]
+	prompt = processor.apply_chat_template(messages, tokenize = False, add_generation_prompt = True)
+	assert isinstance(prompt, str)
+	inputs = processor(text=[prompt], images=[image], return_tensors="pt").to(model.device)
+	inputs['pixel_values'] = inputs['pixel_values'].to(model.dtype)
+
+	# Generate the captions
+	generate_ids = llava_model.generate(
+		**inputs,
+		max_new_tokens=50,
+		do_sample=False,
+		suppress_tokens=None,
+		use_cache=True,
+		temperature=0.6,
+		top_k=None,
+		top_p=0.9,
+	)[0]
+
+	# Trim off the prompt
+	generate_ids = generate_ids[inputs['input_ids'].shape[1]:]
+
+	# Decode the caption
+	caption = processor.tokenizer.decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)
+	caption = caption.strip()
+	print(caption)
+
+
+##INT4: 
+
+##BF16: 
+
+image_url = "http://images.cocodataset.org/train2017/000000411975.jpg"
+content = "How many people are on the baseball field in the picture?"
+
+##INT4: 
+
+##BF16: 
+
+
+image_url = "http://images.cocodataset.org/train2017/000000093025.jpg"
+content = "How many people and animals are there in the image?"
+
+##INT4: 
+
+##BF16: 
+
+```
+
+
+### Generate the model
+Here is the sample command to reproduce the model.
+```bash
+pip install auto-round
+auto-round-mllm \
+--model  \
+--device 0 \
+--group_size 128 \
+--bits 4 \
+--iters 1000 \
+--nsample 512 \
+--seqlen 2048 \
+--template default \
+--model_dtype "float16" \
+--format 'auto_gptq,auto_round' \
+--output_dir "./tmp_autoround"
+```
+
+## Ethical Considerations and Limitations
+
+The model can produce factually incorrect output, and should not be relied on to produce factually accurate information. Because of the limitations of the pretrained model and the finetuning datasets, it is possible that this model could generate lewd, biased or otherwise offensive outputs.
+
+Therefore, before deploying any applications of the model, developers should perform safety testing.
+
+## Caveats and Recommendations
+
+Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model.
+
+Here are a couple of useful links to learn more about Intel's AI software:
+
+- Intel Neural Compressor [link](https://github.com/intel/neural-compressor)
+
+## Disclaimer
+
+The license on this model does not constitute legal advice. We are not responsible for the actions of third parties who use this model. Please consult an attorney before using this model for commercial purposes.
+
+## Cite
+
+@article{cheng2023optimize, title={Optimize weight rounding via signed gradient descent for the quantization of llms}, author={Cheng, Wenhua and Zhang, Weiwei and Shen, Haihao and Cai, Yiyang and He, Xin and Lv, Kaokao and Liu, Yi}, journal={arXiv preprint arXiv:2309.05516}, year={2023} }
+
+[arxiv](https://arxiv.org/abs/2309.05516) [github](https://github.com/intel/auto-round)