---
language:
- ja
tags:
- heron
- vision
- image-captioning
- VQA
pipeline_tag: image-to-text
license:
- cc-by-nc-4.0
inference: false
---
# Heron GIT Japanese StableLM Base 7B


## Model Details
Heron GIT Japanese StableLM Base 7B is a vision-language model that can converse about input images.<br>
This model was trained using [the heron library](https://github.com/turingmotors/heron). Please refer to the code for details.


## Usage

Follow [the installation guide](https://github.com/turingmotors/heron/). 

```python
import torch
from heron.models.git_llm.git_japanese_stablelm_alpha import GitJapaneseStableLMAlphaForCausalLM
from transformers import AutoProcessor

device_id = 0
device = f"cuda:{device_id}"

MODEL_NAME = "turing-motors/heron-chat-git-ja-stablelm-base-7b-v1"
    
model = GitJapaneseStableLMAlphaForCausalLM.from_pretrained(
    MODEL_NAME, torch_dtype=torch.float16, ignore_mismatched_sizes=True
)
model.eval()
model.to(device)

# prepare a processor
processor = AutoProcessor.from_pretrained(MODEL_NAME)

import requests
from PIL import Image

# prepare inputs
url = "https://www.barnorama.com/wp-content/uploads/2016/12/03-Confusing-Pictures.jpg"
image = Image.open(requests.get(url, stream=True).raw)

text = f"##human: この画像の面白い点は何ですか?\n##gpt: "

# do preprocessing
inputs = processor(
    text=text,
    images=image,
    return_tensors="pt",
    truncation=True,
)

inputs = {k: v.to(device) for k, v in inputs.items()}

# do inference
with torch.no_grad():
    out = model.generate(**inputs, max_length=256, do_sample=False, temperature=0., no_repeat_ngram_size=2)

# print result
print(processor.tokenizer.batch_decode(out))
```


## Model Details
* **Developed by**: [Turing Inc.](https://www.turing-motors.com/)
* **Adaptor type**: [GIT](https://arxiv.org/abs/2205.14100)
* **Lamguage Model**: [Japanese StableLM Base Alpha](https://huggingface.co/stabilityai/japanese-stablelm-base-alpha-7b)
* **Language(s)**: Japanese

### Training

1. The GIT adaptor was trained with LLaVA-Pratrain-JA.
2. The LLM and the adapter were fully fine-tuned with LLaVA-Instruct-620K-JA-v2.

### Training Dataset

1. LLaVA-Pratrain-JA
2. LLaVA-Instruct-620K-JA-v2


## Use and Limitations

### Intended Use

This model is intended for use in chat-like applications and for research purposes.

### Limitations

The model may produce inaccurate or false information, and its accuracy is not guaranteed. It is still in the research and development stage.

## How to cite
```bibtex
@misc{}
```

---
license: cc-by-nc-4.0
---