chore: import upstream snapshot with attribution

docs/features/quantization/gguf.md

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+# GGUF
+
+!!! warning
+    Please note that GGUF support in vLLM is highly experimental and under-optimized at the moment, it might be incompatible with other features. Currently, you can use GGUF as a way to reduce memory footprint. If you encounter any issues, please report them to the vLLM team.
+
+!!! note
+    GGUF support has migrated to OOT [vllm-gguf-plugin](https://github.com/vllm-project/vllm-gguf-plugin). Make sure you have GGUF plugin installed before serving a GGUF model.
+
+Before serving a GGUF model, make sure to install the [vllm-gguf-plugin](https://github.com/vllm-project/vllm-gguf-plugin):
+
+```bash
+uv pip install vllm-gguf-plugin
+```
+
+To run a GGUF model with vLLM, you can use the `repo_id:quant_type` format to load directly from HuggingFace. For example, to load a Q4_K_M quantized model from [unsloth/Qwen3-0.6B-GGUF](https://huggingface.co/unsloth/Qwen3-0.6B-GGUF):
+
+```bash
+# We recommend using the tokenizer from base model to avoid long-time and buggy tokenizer conversion.
+vllm serve unsloth/Qwen3-0.6B-GGUF:Q4_K_M --tokenizer Qwen/Qwen3-0.6B
+```
+
+You can also add `--tensor-parallel-size 2` to enable tensor parallelism inference with 2 GPUs:
+
+```bash
+vllm serve unsloth/Qwen3-0.6B-GGUF:Q4_K_M \
+   --tokenizer Qwen/Qwen3-0.6B \
+   --tensor-parallel-size 2
+```
+
+Alternatively, you can download and use a local GGUF file:
+
+```bash
+wget https://huggingface.co/unsloth/Qwen3-0.6B-GGUF/resolve/main/Qwen3-0.6B-Q4_K_M.gguf
+vllm serve ./Qwen3-0.6B-Q4_K_M.gguf --tokenizer Qwen/Qwen3-0.6B
+```
+
+!!! warning
+    We recommend using the tokenizer from base model instead of GGUF model. Because the tokenizer conversion from GGUF is time-consuming and unstable, especially for some models with large vocab size.
+
+GGUF assumes that HuggingFace can convert the metadata to a config file. In case HuggingFace doesn't support your model you can manually create a config and pass it as hf-config-path
+
+```bash
+# If your model is not supported by HuggingFace you can manually provide a HuggingFace compatible config path
+vllm serve unsloth/Qwen3-0.6B-GGUF:Q4_K_M \
+   --tokenizer Qwen/Qwen3-0.6B \
+   --hf-config-path Qwen/Qwen3-0.6B
+```
+
+You can also use the GGUF model directly through the LLM entrypoint:
+
+??? code
+
+      ```python
+      from vllm import LLM, SamplingParams
+
+      # In this script, we demonstrate how to pass input to the chat method:
+      conversation = [
+         {
+            "role": "system",
+            "content": "You are a helpful assistant",
+         },
+         {
+            "role": "user",
+            "content": "Hello",
+         },
+         {
+            "role": "assistant",
+            "content": "Hello! How can I assist you today?",
+         },
+         {
+            "role": "user",
+            "content": "Write an essay about the importance of higher education.",
+         },
+      ]
+
+      # Create a sampling params object.
+      sampling_params = SamplingParams(temperature=0.8, top_p=0.95)
+
+      # Create an LLM using repo_id:quant_type format.
+      llm = LLM(
+         model="unsloth/Qwen3-0.6B-GGUF:Q4_K_M",
+         tokenizer="Qwen/Qwen3-0.6B",
+      )
+      # Generate texts from the prompts. The output is a list of RequestOutput objects
+      # that contain the prompt, generated text, and other information.
+      outputs = llm.chat(conversation, sampling_params)
+
+      # Print the outputs.
+      for output in outputs:
+         prompt = output.prompt
+         generated_text = output.outputs[0].text
+         print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
+      ```