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[Doc][Misc] Improve readability and fix typos in documentation (#8340)

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### What this PR does / why we need it?

This PR improves the readability of the documentation by fixing typos,
correcting command extensions, and fixing broken links in the Chinese
README.

### Does this PR introduce _any_ user-facing change?

No.

### How was this patch tested?

Documentation changes only.

---------

Signed-off-by: sunshine202600 <sunshine202600@163.com>
This commit is contained in:
sunshine202600
2026-04-17 08:54:38 +08:00
committed by GitHub
parent 8952fddc7e
commit 1dd1de8153
46 changed files with 90 additions and 92 deletions
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10
docs/source/tutorials/models/Qwen3_reranker.md
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@@ -95,14 +95,14 @@ model_name = "Qwen/Qwen3-Reranker-8B"
# What is the difference between the official original version and one
# that has been converted into a sequence classification model?
# Qwen3-Reranker is a language model that doing reranker by using the
# Qwen3-Reranker is a language model that does reranker by using the
# logits of "no" and "yes" tokens.
# It needs to computing 151669 tokens logits, making this method extremely
# inefficient, not to mention incompatible with the vllm score API.
# It needs to compute 151669 tokens logits, making this method extremely
# inefficient, not to mention incompatible with the vLLM score API.
# A method for converting the original model into a sequence classification
# model was proposed. See: https://huggingface.co/Qwen/Qwen3-Reranker-0.6B/discussions/3
# Models converted offline using this method can not only be more efficient
# and support the vllm score API, but also make the init parameters more
# and support the vLLM score API, but also make the init parameters more
# concise, for example.
# model = LLM(model="Qwen/Qwen3-Reranker-8B", task="score")
@@ -120,7 +120,7 @@ model = LLM(
)
# Why do we need hf_overrides for the official original version:
# vllm converts it to Qwen3ForSequenceClassification when loaded for
# vLLM converts it to Qwen3ForSequenceClassification when loaded for
# better performance.
# - Firstly, we need using `"architectures": ["Qwen3ForSequenceClassification"],`
# to manually route to Qwen3ForSequenceClassification.