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Update README.md
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README.md
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@@ -34,7 +34,7 @@ import torch
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from PIL import Image
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from transformers import AutoTokenizer, AutoModelForCausalLM
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ckpt_path = "internlm/internlm-xcomposer2-7b"
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tokenizer = AutoTokenizer.from_pretrained(ckpt_path, trust_remote_code=True)
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# Set `torch_dtype=torch.float16` to load model in float16, otherwise it will be loaded as float32 and might cause OOM Error.
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model = AutoModelForCausalLM.from_pretrained(ckpt_path, torch_dtype=torch.float16, trust_remote_code=True).cuda()
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model = model.eval()
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@@ -68,9 +68,10 @@ In conclusion, pandas are truly remarkable animals that deserve our admiration a
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```python
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import torch
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from transformers import AutoTokenizer, AutoModelForCausalLM
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ckpt_path = "internlm/internlm-xcomposer2-7b"
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tokenizer = AutoTokenizer.from_pretrained(ckpt_path, trust_remote_code=True)
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# `torch_dtype=torch.float16` 可以令模型以 float16 精度加载，否则 transformers 会将模型加载为 float32，导致显存不足
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model = AutoModelForCausalLM.from_pretrained(ckpt_path, torch_dtype=torch.float16, trust_remote_code=True).cuda()
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model = model.eval()
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from PIL import Image
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from transformers import AutoTokenizer, AutoModelForCausalLM
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ckpt_path = "internlm/internlm-xcomposer2-7b"
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tokenizer = AutoTokenizer.from_pretrained(ckpt_path, trust_remote_code=True)
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# Set `torch_dtype=torch.float16` to load model in float16, otherwise it will be loaded as float32 and might cause OOM Error.
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model = AutoModelForCausalLM.from_pretrained(ckpt_path, torch_dtype=torch.float16, trust_remote_code=True).cuda()
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model = model.eval()
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```python
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import torch
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from PIL import Image
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from transformers import AutoTokenizer, AutoModelForCausalLM
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ckpt_path = "internlm/internlm-xcomposer2-7b"
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tokenizer = AutoTokenizer.from_pretrained(ckpt_path, trust_remote_code=True)
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# `torch_dtype=torch.float16` 可以令模型以 float16 精度加载，否则 transformers 会将模型加载为 float32，导致显存不足
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model = AutoModelForCausalLM.from_pretrained(ckpt_path, torch_dtype=torch.float16, trust_remote_code=True).cuda()
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model = model.eval()
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