biondizzle/vllm
1
0
Fork 0
Code Issues Pull Requests Actions 2 Packages Projects Releases Wiki Activity

[Docs] Fix hardcoded links in docs (#21287)

Browse Source 
Signed-off-by: Harry Mellor <19981378+hmellor@users.noreply.github.com>
This commit is contained in:
Harry Mellor
2025-07-21 10:23:57 +01:00
committed by GitHub
parent 042af0c8d3
commit be54a951a3
5 changed files with 6 additions and 7 deletions
Download Patch File Download Diff File Expand all files Collapse all files

2
docs/features/multimodal_inputs.md
View File

@@ -98,7 +98,7 @@ To substitute multiple images inside the same text prompt, you can pass in a lis
Full example: <gh-file:examples/offline_inference/vision_language_multi_image.py>
If using the [LLM.chat](https://docs.vllm.ai/en/stable/models/generative_models.html#llmchat) method, you can pass images directly in the message content using various formats: image URLs, PIL Image objects, or pre-computed embeddings:
If using the [LLM.chat](../models/generative_models.md#llmchat) method, you can pass images directly in the message content using various formats: image URLs, PIL Image objects, or pre-computed embeddings:
```python
from vllm import LLM

2
docs/features/quantization/bitblas.md
View File

@@ -5,7 +5,7 @@ vLLM now supports [BitBLAS](https://github.com/microsoft/BitBLAS) for more effic
!!! note
Ensure your hardware supports the selected `dtype` (`torch.bfloat16` or `torch.float16`).
Most recent NVIDIA GPUs support `float16`, while `bfloat16` is more common on newer architectures like Ampere or Hopper.
For details see [supported hardware](https://docs.vllm.ai/en/latest/features/quantization/supported_hardware.html).
For details see [supported hardware](supported_hardware.md).
Below are the steps to utilize BitBLAS with vLLM.

2
docs/features/tool_calling.md
View File

@@ -95,7 +95,7 @@ specify the `name` of one of the tools in the `tool_choice` parameter of the cha
## Required Function Calling
vLLM supports the `tool_choice='required'` option in the chat completion API. Similar to the named function calling, it also uses guided decoding, so this is enabled by default and will work with any supported model. The required guided decoding features (JSON schema with `anyOf`) are currently only supported in the V0 engine with the guided decoding backend `outlines`. However, support for alternative decoding backends are on the [roadmap](https://docs.vllm.ai/en/latest/usage/v1_guide.html#feature-model) for the V1 engine.
vLLM supports the `tool_choice='required'` option in the chat completion API. Similar to the named function calling, it also uses guided decoding, so this is enabled by default and will work with any supported model. The required guided decoding features (JSON schema with `anyOf`) are currently only supported in the V0 engine with the guided decoding backend `outlines`. However, support for alternative decoding backends are on the [roadmap](../usage/v1_guide.md#features) for the V1 engine.
When tool_choice='required' is set, the model is guaranteed to generate one or more tool calls based on the specified tool list in the `tools` parameter. The number of tool calls depends on the user's query. The output format strictly follows the schema defined in the `tools` parameter.