Merge branch 'main' into feat/optional_deps

Author: sjmonson

docs/backends.md

@@ -40,6 +40,24 @@ docker run --gpus 1 -ti --shm-size 1g --ipc=host --rm -p 8080:80 \
 
 For more information on starting a TGI server, see the [TGI Documentation](https://huggingface.co/docs/text-generation-inference/index).
 
+### 3. llama.cpp
+
+[llama.cpp](https://github.com/ggml-org/llama.cpp) provides lightweight, OpenAI-compatible server through its [llama-server](https://github.com/ggml-org/llama.cpp/blob/master/tools/server) tool.
+
+To start a llama.cpp server with the gpt-oss-20b model, you can use the following command:
+
+```bash
+llama-server -hf ggml-org/gpt-oss-20b-GGUF --alias gpt-oss-20b --ctx-size 0 --jinja -ub 2048 -b 2048
+```
+
+Note that we are providing an alias `gpt-oss-20b` for the model name because `guidellm` is using it to retrieve model metadata in JSON format and such metadata is not included in GGUF model repositories. A simple workaround is to download the metadata files from safetensors repository and place them in a local directory named after the alias:
+
+```bash
+huggingface-cli download openai/gpt-oss-20b --include "*.json" --local-dir gpt-oss-20b/
+```
+
+Now you can run `guidellm` as usual and it will be able to fetch the model metadata from the local directory.
+
 ## Expanding Backend Support
 
 GuideLLM is an open platform, and we encourage contributions to extend its backend support. Whether it's adding new server implementations, integrating with Python-based backends, or enhancing existing capabilities, your contributions are welcome. For more details on how to contribute, see the [CONTRIBUTING.md](https://github.com/vllm-project/guidellm/blob/main/CONTRIBUTING.md) file.

src/guidellm/backend/openai.py

@@ -688,7 +688,7 @@ def _extract_completions_delta_content(
             return data["choices"][0]["text"]
 
         if type_ == "chat_completions":
-            return data["choices"][0]["delta"]["content"]
+            return data.get("choices", [{}])[0].get("delta", {}).get("content")
 
         raise ValueError(f"Unsupported type: {type_}")
 

src/guidellm/presentation/data_models.py

@@ -190,7 +190,7 @@ class TabularDistributionSummary(DistributionSummary):
     """
 
     @computed_field
-    def percentile_rows(self) -> list[dict[str, float]]:
+    def percentile_rows(self) -> list[dict[str, Union[str, float]]]:
         rows = [
             {"percentile": name, "value": value}
             for name, value in self.percentiles.model_dump().items()