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--- |
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datasets: |
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- Lin-Chen/ShareGPT4V |
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pipeline_tag: image-text-to-text |
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library_name: xtuner |
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--- |
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<div align="center"> |
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<img src="https://github.com/InternLM/lmdeploy/assets/36994684/0cf8d00f-e86b-40ba-9b54-dc8f1bc6c8d8" width="600"/> |
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[](https://github.com/InternLM/xtuner) |
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</div> |
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## Model |
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llava-phi-3-mini is a LLaVA model fine-tuned from [microsoft/Phi-3-mini-4k-instruct](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct) and [CLIP-ViT-Large-patch14-336](https://huggingface.co/openai/clip-vit-large-patch14-336) with [ShareGPT4V-PT](https://huggingface.co/datasets/Lin-Chen/ShareGPT4V) and [InternVL-SFT](https://github.com/OpenGVLab/InternVL/tree/main/internvl_chat#prepare-training-datasets) by [XTuner](https://github.com/InternLM/xtuner). |
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**Note: This model is in official LLaVA format.** |
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Resources: |
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- GitHub: [xtuner](https://github.com/InternLM/xtuner) |
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- HuggingFace LLaVA format model: [xtuner/llava-phi-3-mini-hf](https://huggingface.co/xtuner/llava-phi-3-mini-hf) |
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- GGUF LLaVA model: [xtuner/llava-phi-3-mini-gguf](https://huggingface.co/xtuner/llava-phi-3-mini-gguf) |
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- XTuner LLaVA format model: [xtuner/llava-phi-3-mini-xtuner](https://huggingface.co/xtuner/llava-phi-3-mini-xtuner) |
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## Details |
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| Model | Visual Encoder | Projector | Resolution | Pretraining Strategy | Fine-tuning Strategy | Pretrain Dataset | Fine-tune Dataset | Pretrain Epoch | Fine-tune Epoch | |
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| :-------------------- | ------------------: | --------: | ---------: | ---------------------: | ------------------------: | ------------------------: | -----------------------: | -------------- | --------------- | |
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| LLaVA-v1.5-7B | CLIP-L | MLP | 336 | Frozen LLM, Frozen ViT | Full LLM, Frozen ViT | LLaVA-PT (558K) | LLaVA-Mix (665K) | 1 | 1 | |
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| LLaVA-Llama-3-8B | CLIP-L | MLP | 336 | Frozen LLM, Frozen ViT | Full LLM, LoRA ViT | LLaVA-PT (558K) | LLaVA-Mix (665K) | 1 | 1 | |
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| LLaVA-Llama-3-8B-v1.1 | CLIP-L | MLP | 336 | Frozen LLM, Frozen ViT | Full LLM, LoRA ViT | ShareGPT4V-PT (1246K) | InternVL-SFT (1268K) | 1 | 1 | |
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| **LLaVA-Phi-3-mini** | CLIP-L | MLP | 336 | Frozen LLM, Frozen ViT | Full LLM, Full ViT | ShareGPT4V-PT (1246K) | InternVL-SFT (1268K) | 1 | 2 | |
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## Results |
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<div align="center"> |
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<img src="https://github.com/InternLM/xtuner/assets/36994684/78524f65-260d-4ae3-a687-03fc5a19dcbb" alt="Image" width=500" /> |
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</div> |
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| Model | MMBench Test (EN) | MMMU Val | SEED-IMG | AI2D Test | ScienceQA Test | HallusionBench aAcc | POPE | GQA | TextVQA | MME | MMStar | |
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| :-------------------- | :---------------: | :-------: | :------: | :-------: | :------------: | :-----------------: | :--: | :--: | :-----: | :------: | :----: | |
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| LLaVA-v1.5-7B | 66.5 | 35.3 | 60.5 | 54.8 | 70.4 | 44.9 | 85.9 | 62.0 | 58.2 | 1511/348 | 30.3 | |
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| LLaVA-Llama-3-8B | 68.9 | 36.8 | 69.8 | 60.9 | 73.3 | 47.3 | 87.2 | 63.5 | 58.0 | 1506/295 | 38.2 | |
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| LLaVA-Llama-3-8B-v1.1 | 72.3 | 37.1 | 70.1 | 70.0 | 72.9 | 47.7 | 86.4 | 62.6 | 59.0 | 1469/349 | 45.1 | |
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| **LLaVA-Phi-3-mini** | 69.2 | 41.4 | 70.0 | 69.3 | 73.7 | 49.8 | 87.3 | 61.5 | 57.8 | 1477/313 | 43.7 | |
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## Quickstart |
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### Chat by LLaVA official library |
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1. Install official LLaVA library |
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```bash |
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pip install git+https://github.com/haotian-liu/LLaVA.git |
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``` |
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2. Chat by below script |
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<details> |
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<summary>cli.py</summary> |
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```python |
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import argparse |
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from io import BytesIO |
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import requests |
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import torch |
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from llava.constants import DEFAULT_IMAGE_TOKEN, IMAGE_TOKEN_INDEX |
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from llava.conversation import Conversation, SeparatorStyle |
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from llava.mm_utils import process_images, tokenizer_image_token |
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from llava.model import LlavaLlamaForCausalLM |
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from PIL import Image |
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from transformers import (AutoTokenizer, BitsAndBytesConfig, StoppingCriteria, |
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StoppingCriteriaList, TextStreamer) |
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def load_image(image_file): |
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if image_file.startswith('http://') or image_file.startswith('https://'): |
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response = requests.get(image_file) |
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image = Image.open(BytesIO(response.content)).convert('RGB') |
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else: |
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image = Image.open(image_file).convert('RGB') |
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return image |
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class StopWordStoppingCriteria(StoppingCriteria): |
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"""StopWord stopping criteria.""" |
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def __init__(self, tokenizer, stop_word): |
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self.tokenizer = tokenizer |
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self.stop_word = stop_word |
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self.length = len(self.stop_word) |
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def __call__(self, input_ids, *args, **kwargs) -> bool: |
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cur_text = self.tokenizer.decode(input_ids[0]) |
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cur_text = cur_text.replace('\r', '').replace('\n', '') |
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return cur_text[-self.length:] == self.stop_word |
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def get_stop_criteria(tokenizer, stop_words=[]): |
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stop_criteria = StoppingCriteriaList() |
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for word in stop_words: |
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stop_criteria.append(StopWordStoppingCriteria(tokenizer, word)) |
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return stop_criteria |
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def main(args): |
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kwargs = {'device_map': args.device} |
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if args.load_8bit: |
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kwargs['load_in_8bit'] = True |
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elif args.load_4bit: |
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kwargs['load_in_4bit'] = True |
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kwargs['quantization_config'] = BitsAndBytesConfig( |
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load_in_4bit=True, |
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bnb_4bit_compute_dtype=torch.float16, |
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bnb_4bit_use_double_quant=True, |
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bnb_4bit_quant_type='nf4') |
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else: |
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kwargs['torch_dtype'] = torch.float16 |
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tokenizer = AutoTokenizer.from_pretrained(args.model_path) |
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model = LlavaLlamaForCausalLM.from_pretrained( |
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args.model_path, low_cpu_mem_usage=True, **kwargs) |
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vision_tower = model.get_vision_tower() |
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if not vision_tower.is_loaded: |
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vision_tower.load_model(device_map=args.device) |
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image_processor = vision_tower.image_processor |
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conv = Conversation( |
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system=system='<|system|>\nAnswer the questions.', |
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roles=('<|user|>\n', '<|assistant|>\n'), |
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messages=[], |
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offset=0, |
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sep_style=SeparatorStyle.MPT, |
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sep='<|end|>', |
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) |
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roles = conv.roles |
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image = load_image(args.image_file) |
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image_size = image.size |
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image_tensor = process_images([image], image_processor, model.config) |
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if type(image_tensor) is list: |
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image_tensor = [ |
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image.to(model.device, dtype=torch.float16) |
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for image in image_tensor |
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] |
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else: |
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image_tensor = image_tensor.to(model.device, dtype=torch.float16) |
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while True: |
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try: |
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inp = input(f'{roles[0]}: ') |
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except EOFError: |
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inp = '' |
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if not inp: |
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print('exit...') |
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break |
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print(f'{roles[1]}: ', end='') |
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if image is not None: |
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inp = DEFAULT_IMAGE_TOKEN + '\n' + inp |
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image = None |
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conv.append_message(conv.roles[0], inp) |
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conv.append_message(conv.roles[1], None) |
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prompt = conv.get_prompt() |
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input_ids = tokenizer_image_token( |
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prompt, tokenizer, IMAGE_TOKEN_INDEX, |
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return_tensors='pt').unsqueeze(0).to(model.device) |
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stop_criteria = get_stop_criteria( |
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tokenizer=tokenizer, stop_words=[conv.sep]) |
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streamer = TextStreamer( |
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tokenizer, skip_prompt=True, skip_special_tokens=True) |
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with torch.inference_mode(): |
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output_ids = model.generate( |
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input_ids, |
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images=image_tensor, |
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image_sizes=[image_size], |
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do_sample=True if args.temperature > 0 else False, |
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temperature=args.temperature, |
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max_new_tokens=args.max_new_tokens, |
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streamer=streamer, |
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stopping_criteria=stop_criteria, |
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use_cache=True) |
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outputs = tokenizer.decode(output_ids[0]).strip() |
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conv.messages[-1][-1] = outputs |
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if args.debug: |
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print('\n', {'prompt': prompt, 'outputs': outputs}, '\n') |
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if __name__ == '__main__': |
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parser = argparse.ArgumentParser() |
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parser.add_argument( |
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'--model-path', type=str, default='xtuner/llava-llama-3-8b-v1_1-hf') |
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parser.add_argument('--image-file', type=str, required=True) |
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parser.add_argument('--device', type=str, default='auto') |
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parser.add_argument('--temperature', type=float, default=0.2) |
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parser.add_argument('--max-new-tokens', type=int, default=512) |
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parser.add_argument('--load-8bit', action='store_true') |
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parser.add_argument('--load-4bit', action='store_true') |
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parser.add_argument('--debug', action='store_true') |
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args = parser.parse_args() |
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main(args) |
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``` |
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</details> |
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``` |
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python ./cli.py --model-path xtuner/llava-phi-3-mini --image-file https://raw.githubusercontent.com/open-mmlab/mmdeploy/main/tests/data/tiger.jpeg --load-4bit |
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``` |
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### Reproduce |
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Please refer to [docs](https://github.com/InternLM/xtuner/tree/main/xtuner/configs/llava/phi3_mini_4k_instruct_clip_vit_large_p14_336#readme). |
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## Citation |
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```bibtex |
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@misc{2023xtuner, |
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title={XTuner: A Toolkit for Efficiently Fine-tuning LLM}, |
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author={XTuner Contributors}, |
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howpublished = {\url{https://github.com/InternLM/xtuner}}, |
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year={2023} |
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} |
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``` |
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