diff --git a/docs/zh_cn/multi_modal/llava.md b/docs/zh_cn/multi_modal/llava.md
index cf95e15d5c..c40f37308a 100644
--- a/docs/zh_cn/multi_modal/llava.md
+++ b/docs/zh_cn/multi_modal/llava.md
@@ -1,3 +1,135 @@
# LLaVA
-TODO
+LMDeploy 支持以下 LLaVA 系列模型，具体如下表所示：
+
+| 模型 | 大小 | 支持的推理引擎 |
+| :----------------------------------: | :--: | :----------------: |
+| llava-hf/Llava-interleave-qwen-7b-hf | 7B | TurboMind, PyTorch |
+| llava-hf/llava-1.5-7b-hf | 7B | TurboMind, PyTorch |
+| liuhaotian/llava-v1.6-vicuna-7b | 7B | TurboMind, PyTorch |
+| liuhaotian/llava-v1.6-mistral-7b | 7B | TurboMind, PyTorch |
+
+接下来的章节将演示如何使用 LMDeploy 部署 LLaVA 模型，并以 [llava-hf/llava-interleave](https://huggingface.co/llava-hf/llava-interleave-qwen-7b-hf) 为例。
+
+## 安装
+
+请按照[安装指南](../get_started/installation.md)安装 LMDeploy。
+
+或者，您也可以使用官方的 Docker 镜像：
+
+```shell
+docker pull openmmlab/lmdeploy:latest
+```
+
+## 离线推理
+
+以下示例代码展示了 VLM pipeline 的基本用法。有关详细信息，请参考 [VLM 离线推理流程](./vl_pipeline.md)。
+
+```python
+from lmdeploy import GenerationConfig, TurbomindEngineConfig, pipeline
+from lmdeploy.vl import load_image
+
+pipe = pipeline("llava-hf/llava-interleave-qwen-7b-hf", backend_config=TurbomindEngineConfig(cache_max_entry_count=0.5),
+ gen_config=GenerationConfig(max_new_tokens=512))
+
+image = load_image('https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-VL/assets/demo.jpeg')
+prompt = 'Describe the image.'
+print(f'prompt:{prompt}')
+response = pipe((prompt, image))
+print(response)
+```
+
+更多示例：
+
+
+ 多图片多轮对话，组合图片
+
+```python
+from lmdeploy import pipeline, GenerationConfig
+
+pipe = pipeline('llava-hf/llava-interleave-qwen-7b-hf', log_level='INFO')
+messages = [
+ dict(role='user', content=[
+ dict(type='text', text='Describe the two images in detail.'),
+ dict(type='image_url', image_url=dict(url='https://raw.githubusercontent.com/QwenLM/Qwen-VL/master/assets/mm_tutorial/Beijing_Small.jpeg')),
+ dict(type='image_url', image_url=dict(url='https://raw.githubusercontent.com/QwenLM/Qwen-VL/master/assets/mm_tutorial/Chongqing_Small.jpeg'))
+ ])
+]
+out = pipe(messages, gen_config=GenerationConfig(top_k=1))
+
+messages.append(dict(role='assistant', content=out.text))
+messages.append(dict(role='user', content='What are the similarities and differences between these two images.'))
+out = pipe(messages, gen_config=GenerationConfig(top_k=1))
+```
+
+
+
+## 在线服务
+
+可以使用 `lmdeploy serve api_server` CLI 启动服务器：
+
+```shell
+lmdeploy serve api_server llava-hf/llava-interleave-qwen-7b-hf
+```
+
+或者，使用前面提到的 Docker 镜像启动服务：
+
+```shell
+docker run --runtime nvidia --gpus all \
+ -v ~/.cache/huggingface:/root/.cache/huggingface \
+ --env "HUGGING_FACE_HUB_TOKEN=" \
+ -p 23333:23333 \
+ --ipc=host \
+ openmmlab/lmdeploy:latest \
+ lmdeploy serve api_server llava-hf/llava-interleave-qwen-7b-hf
+```
+
+采用 Docker Compose 部署也是一种常见选择。在 lmdeploy 项目的根目录创建 `docker-compose.yml` 文件，如下：
+
+```yaml
+version: '3.5'
+
+services:
+ lmdeploy:
+ container_name: lmdeploy
+ image: openmmlab/lmdeploy:latest
+ ports:
+ - "23333:23333"
+ environment:
+ HUGGING_FACE_HUB_TOKEN:
+ volumes:
+ - ~/.cache/huggingface:/root/.cache/huggingface
+ stdin_open: true
+ tty: true
+ ipc: host
+ command: lmdeploy serve api_server llava-hf/llava-interleave-qwen-7b-hf
+ deploy:
+ resources:
+ reservations:
+ devices:
+ - driver: nvidia
+ count: "all"
+ capabilities: [gpu]
+```
+
+然后，可以执行以下命令启动服务：
+
+```shell
+docker-compose up -d
+```
+
+当运行 `docker logs -f lmdeploy` 后看到如下日志，说明服务启动成功：
+
+```text
+HINT: Please open http://0.0.0.0:23333 in a browser for detailed api usage!!!
+HINT: Please open http://0.0.0.0:23333 in a browser for detailed api usage!!!
+HINT: Please open http://0.0.0.0:23333 in a browser for detailed api usage!!!
+INFO: Started server process [2439]
+INFO: Waiting for application startup.
+INFO: Application startup complete.
+INFO: Uvicorn running on http://0.0.0.0:23333 (Press CTRL+C to quit)
+```
+
+可以通过 `lmdeploy serve api_server -h` 查看 `lmdeploy serve api_server` 的参数详情。
+
+关于 `api_server` 以及如何访问服务的更多信息可以在[这里](api_server_vl.md)找到。
diff --git a/lmdeploy/turbomind/generate_gemm_config.py b/lmdeploy/turbomind/generate_gemm_config.py
index a7689ebc27..34e769776f 100644
--- a/lmdeploy/turbomind/generate_gemm_config.py
+++ b/lmdeploy/turbomind/generate_gemm_config.py
@@ -54,19 +54,14 @@ def main(head_num: int = 32,
from transformers import AutoConfig
config = AutoConfig.from_pretrained(model_path,
trust_remote_code=True)
- try:
- head_num = config.num_attention_heads
- size_per_head = config.hidden_size // head_num
- inter_size = config.intermediate_size
- vocab_size = config.vocab_size
- except AttributeError:
- for key in ['language_config', 'llm_config', 'text_config']:
- config = getattr(config, key, config)
-
- head_num = config.num_attention_heads
- size_per_head = config.hidden_size // head_num
- inter_size = config.intermediate_size
- vocab_size = config.vocab_size
+
+ for key in ['language_config', 'llm_config', 'text_config']:
+ config = getattr(config, key, config)
+ head_num = config.num_attention_heads
+ size_per_head = config.hidden_size // head_num
+ inter_size = config.intermediate_size
+ vocab_size = config.vocab_size
+
for bsz in range(1, max_batch_size + 1):
subprocess.call(
f'{get_llama_gemm()} {bsz} 1 1 {head_num} {size_per_head}'
diff --git a/lmdeploy/turbomind/supported_models.py b/lmdeploy/turbomind/supported_models.py
index 17f8edf22c..88ca22717d 100644
--- a/lmdeploy/turbomind/supported_models.py
+++ b/lmdeploy/turbomind/supported_models.py
@@ -98,6 +98,9 @@ def _is_head_dim_supported(cfg):
# internvl2-4b,internlm2-1b are not working yet
support_by_turbomind = _is_head_dim_supported(cfg.llm_config)
elif arch == 'LlavaForConditionalGeneration':
- support_by_turbomind = _is_head_dim_supported(cfg.text_config)
+ sub_arch = cfg.text_config.architectures[0]
+ if sub_arch in ['Qwen2ForCausalLM', 'LlamaForCausalLM']:
+ support_by_turbomind = _is_head_dim_supported(
+ cfg.text_config)
return support_by_turbomind