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@ -11,6 +11,7 @@ Read this in [English](README_en.md)
## 项目更新
- 🔥 **News**: ```2024/10/12```: 增加了 GLM-4v-9B 模型对vllm框架的支持
- 🔥 **News**: ```2024/09/06```: 增加了在 GLM-4v-9B 模型上构建OpenAI API兼容的服务端
- 🔥 **News**: ```2024/09/05``` 我们开源了使LLMs能够在长上下文问答中生成细粒度引用的模型 [longcite-glm4-9b](https://huggingface.co/THUDM/LongCite-glm4-9b)
以及数据集 [LongCite-45k](https://huggingface.co/datasets/THUDM/LongCite-45k),
@ -252,7 +253,39 @@ with torch.no_grad():
print(tokenizer.decode(outputs[0]))
```
注意: GLM-4V-9B 暂不支持使用 vLLM 方式调用。
使用 vLLM 后端进行推理:
```python
from PIL import Image
from vllm import LLM, SamplingParams
model_name = "THUDM/glm-4v-9b"
llm = LLM(model=model_name,
tensor_parallel_size=1,
max_model_len=8192,
trust_remote_code=True,
enforce_eager=True)
stop_token_ids = [151329, 151336, 151338]
sampling_params = SamplingParams(temperature=0.2,
max_tokens=1024,
stop_token_ids=stop_token_ids)
prompt = "What's the content of the image?"
image = Image.open("your image").convert('RGB')
inputs = {
"prompt": prompt,
"multi_modal_data": {
"image": image
},
}
outputs = llm.generate(inputs, sampling_params=sampling_params)
for o in outputs:
generated_text = o.outputs[0].text
print(generated_text)
```
## 完整项目列表