PerRing
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llava-v1.6-34b-hf

Transformers

Safetensors

llava

pretraining

Inference Endpoints

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PerRing commited on Mar 5

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----
-library_name: transformers
-tags: []
----
-# Model Card for Model ID
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-## Model Details
-### Model Description
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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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-## How to Get Started with the Model
-Use the code below to get started with the model.
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+this repo is huggingface version of liuhaotian/llava-v1.6-34b
+# Issue
+Despite the completion of generation, '\n' is repeatedly generated, so be mindful of adjusting the 'max_length'.
+```python
+import requests
+from PIL import Image
+import torch
+from transformers import AutoProcessor, LlavaForConditionalGeneration
+model_id = "PerRing/llava-v1.6-34b-hf"
+model = LlavaForConditionalGeneration.from_pretrained(
+    model_id,
+    torch_dtype=torch.float16,
+    low_cpu_mem_usage=True,
+).to(0)
+processor = AutoProcessor.from_pretrained(model_id)
+Q='explain about this image'
+prompt = f"""<|im_start|>system
+Answer the questions.<|im_end|><|im_start|>user
+<image>
+{Q}<|im_end|><|im_start|>assistant
+"""
+image_file = "https://images.pexels.com/photos/757889/pexels-photo-757889.jpeg?auto=compress&cs=tinysrgb&w=1260&h=750&dpr=2"
+raw_image = Image.open(requests.get(image_file, stream=True).raw)
+inputs = processor(prompt, raw_image, return_tensors='pt').to(0, torch.float16)
+output = model.generate(**inputs, max_length=256)
+print(processor.decode(output[0], skip_special_tokens=True))
+```
+## result
+```output
+<|im_start|> system
+Answer the questions.<|im_start|> user
+explain about this image<|im_start|> assistant
+The image shows a bouquet of purple flowers arranged in a clear glass vase. The vase is placed on a balcony railing. The balcony railing is made of metal and has a black color. The flowers are purple in color. The bouquet of flowers is placed in the clear glass vase. The vase is made of clear glass. The clear glass vase is placed on the balcony railing. The balcony railing is made of metal and has a black color. The bouquet of purple flowers is placed in the clear glass vase. The vase is made of clear glass.
+```
+# Original(liuhaotian/llava-v1.6-34b) README.md
+<br>
+<br>
+# LLaVA Model Card
+## Model details
+**Model type:**
+LLaVA is an open-source chatbot trained by fine-tuning LLM on multimodal instruction-following data.
+It is an auto-regressive language model, based on the transformer architecture.
+Base LLM: [NousResearch/Nous-Hermes-2-Yi-34B](https://huggingface.co/NousResearch/Nous-Hermes-2-Yi-34B)
+**Model date:**
+LLaVA-v1.6-34B was trained in December 2023.
+**Paper or resources for more information:**
+https://llava-vl.github.io/
+## License
+[NousResearch/Nous-Hermes-2-Yi-34B](https://huggingface.co/NousResearch/Nous-Hermes-2-Yi-34B) license.
+**Where to send questions or comments about the model:**
+https://github.com/haotian-liu/LLaVA/issues
+## Intended use
+**Primary intended uses:**
+The primary use of LLaVA is research on large multimodal models and chatbots.
+**Primary intended users:**
+The primary intended users of the model are researchers and hobbyists in computer vision, natural language processing, machine learning, and artificial intelligence.
+## Training dataset
+- 558K filtered image-text pairs from LAION/CC/SBU, captioned by BLIP.
+- 158K GPT-generated multimodal instruction-following data.
+- 500K academic-task-oriented VQA data mixture.
+- 50K GPT-4V data mixture.
+- 40K ShareGPT data.
+## Evaluation dataset
+A collection of 12 benchmarks, including 5 academic VQA benchmarks and 7 recent benchmarks specifically proposed for instruction-following LMMs.